A review of opinion mining methods for analyzing citizens' contributions in public policy debate
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paper_title: Mapping eParticipation Research: Four Central Challenges

paper_content:

The emerging research area of eParticipation can be characterized as the study of technologyfacilitated citizen participation in (democratic) deliberation and decision-making. Using conventional literature study techniques, we identify 105 articles that are considered to be highly relevant to eParticipation. We develop a definitional schema that suggests different ways of understanding an emerging socio-technical research area and use this schema to map the research contributions identified. This allows us make an initial sketch of the scientific character of the area and its central concerns, theories, and methods. We extend the analysis to define four central research challenges for the field: understanding technology and participation; the strategic challenge; the design challenge; and the evaluation challenge. This article thus contributes to a developing account of eParticipation, which will help future researchers both to navigate the research area and to focus their research agendas.
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paper_title: Towards a systematic exploitation of web 2.0 and simulation modeling tools in public policy process

paper_content:

This paper describes a methodology for the systematic exploitation of the emerging web 2.0 social media by government organizations in the processes of public policies formulation, aiming to enhance e-participation, in combination with established simulation modeling techniques and tools. It is based on the concept of 'Policy Gadget' (Padget), which is a micro web application combining a policy message with underlying group knowledge in social media (in the form of content and user activities) and interacting with citizens in popular web 2.0 locations in order to get and convey their input to policy makers. Such 'Padgets' are created by a central platform-toolset and then deployed in many different Web 2.0 media. Citizens input from them will be used in various simulation modeling techniques and tools (such as the 'Systems Dynamics'), which are going to simulate different policy options and estimate their outcomes and effectiveness. A use case scenario of the proposed methodology is presented, which outlines how it can be used in 'real life' public policy design problems.
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paper_title: The Transformational Effect of Web 2.0 Technologies on Government

paper_content:

Web 2.0 technologies are now being deployed in government settings. For example, public agencies have used blogs to communicate information on public hearings, wikis and RSS feeds to coordinate work, and wikis to internally share expertise, and intelligence information. The potential for Web 2.0 tools create a public sector paradox. On the one hand, they have the potential to create real transformative opportunities related to key public sector issues of transparency, accountability, communication and collaboration, and to promote deeper levels of civic engagement. On the other hand, information flow within government, across government agencies and between government and the public is often highly restricted through regulations, specific reporting structures and therefore usually delayed through the filter of the bureaucratic constraints. What the emergent application and popularity of Web 2.0 tools show is that there is an apparent need within government to create, distribute and collect information outside the given hierarchical information flow. Clearly, these most recent Internet technologies are creating dramatic changes in the way people at a peer-to-peer production level communicate and collaborate over the Internet. And these have potentially transformative implications for the way public sector organizations do work and communicate with each other and with citizens. But they also create potential difficulties and challenges that have their roots in the institutional contexts these technologies are or will be deployed within. In other words, it is not the technology that hinders us from transformation and innovation – it is the organizational and institutional hurdles that need to be overcome. This paper provides an overview of the transformative organizational, technological and informational challenges ahead.
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paper_title: eParticipation: The Research Gaps

paper_content:

eParticipation is a challenging research domain comprising a large number of academic disciplines and existing in a complex social and political environment. In this paper we identify eParticipation research needs and barriers and in so doing indicate future research direction. We do this by first setting the context for eParticipation research. We then consider the current situation and analyse the challenges facing future research. The future research direction was identified through conducting workshops and analysing published papers. The results are six main research challenges: breadth of research field; research design; technology design; institutional resistance; equity, and theory. These six challenges are described in detail along with the research direction to address them.
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paper_title: An investigation of the use of structured e-forum for enhancing e-participation in parliaments

paper_content:

The e-participation research has investigated and suggested some Information and Communication Technologies (ICTs) tools such as e-forum, e-petition and e-community tools. This paper investigates the use of an advanced and more structured ICT tool, the 'structured e-forum', for supporting and enhancing e-participation and e-consultation in the legislation formation process in Parliaments. For this purpose, we designed and implemented an e-consultation pilot on a law under formation in the Greek Parliament, using a structured e-forum tool based on the Issue-Based Information Systems (IBISs) framework. This pilot has been evaluated using multiple methods (analysis of discussion tree, quantitative evaluation and qualitative evaluation).
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paper_title: Web data mining: exploring hyperlinks, contents, and usage data

paper_content:

This paper presents a review of the book "Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data" by Bing Liu. The review concludes that the breadth and depth of this book makes it a required staple for every Web mining researcher, student, or practitioner.
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paper_title: Opinion mining and sentiment analysis

paper_content:

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. ::: ::: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
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paper_title: Opinion observer: analyzing and comparing opinions on the Web

paper_content:

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.
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paper_title: Mining the peanut gallery: opinion extraction and semantic classification of product reviews

paper_content:

The web contains a wealth of product reviews, but sifting through them is a daunting task. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). We begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. When operating on individual sentences collected from web searches, performance is limited due to noise and ambiguity. But in the context of a complete web-based tool and aided by a simple method for grouping sentences into attributes, the results are qualitatively quite useful.
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paper_title: Sentiment Classification On Customer Feedback Data: Noisy Data, Large Feature Vectors, And The Role Of Linguistic Analysis

paper_content:

We demonstrate that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data. We show that by using large feature vectors in combination with feature reduction, we can train linear support vector machines that achieve high classification accuracy on data that present classification challenges even for a human annotator. We also show that, surprisingly, the addition of deep linguistic analysis features to a set of surface level word n-gram features contributes consistently to classification accuracy in this domain.
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paper_title: Pulse: Mining Customer Opinions from Free Text

paper_content:

We present a prototype system, code-named Pulse, for mining topics and sentiment orientation jointly from free text customer feedback. We describe the application of the prototype system to a database of car reviews. Pulse enables the exploration of large quantities of customer free text. The user can examine customer opinion “at a glance” or explore the data at a finer level of detail. We describe a simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface.
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paper_title: Joint Extraction Of Entities And Relations For Opinion Recognition

paper_content:

We present an approach for the joint extraction of entities and relations in the context of opinion recognition and analysis. We identify two types of opinion-related entities --- expressions of opinions and sources of opinions --- along with the linking relation that exists between them. Inspired by Roth and Yih (2004), we employ an integer linear programming approach to solve the joint opinion recognition task, and show that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities. Performance further improves when a semantic role labeling system is incorporated. The resulting system achieves F-measures of 79 and 69 for entity and relation extraction, respectively, improving substantially over prior results in the area.
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paper_title: Yahoo! for amazon: Sentiment extraction from small talk on the web

paper_content:

Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises different classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values---tech-sector postings are related to stock index levels, and to volumes and volatility. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.
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paper_title: Customizing Sentiment Classifiers to New Domains: a Case Study

paper_content:

Sentiment classification is a very domainspecific problem; classifiers trained in one domain do not perform well in others. Unfortunately, many domains are lacking in large amounts of labeled data for fully-supervised learning approaches. At the same time, sentiment classifiers need to be customizable to new domains in order to be useful in practice. We attempt to address these difficulties and constraints in this paper, where we survey four different approaches to customizing a sentiment classification system to a new target domain in the absence of large amounts of labeled data. We base our experiments on data from four different domains. After establishing that naive cross-domain classification results in poor classification accuracy, we compare results obtained by using each of the four approaches and discuss their advantages, disadvantages and performance.
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paper_title: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification

paper_content:

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains.
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paper_title: Knowledge Transfer and Opinion Detection in the TREC2006 Blog Track

paper_content:

The paper describes the opinion detection system developed in Carnegie Mellon University for TREC 2006 Blog track. The system performed a two-stage process: passage retrieval and opinion detection. Due to lack of training data for the TREC Blog corpus, online opinion reviews provided in other domains, such as movie review and product review, were used as the training data. Knowledge transfer was performed to make the cross-domain learning possible. Logistic regression ranked the sentence-level opinions vs. objective statements. The evaluation shows that the algorithm is effective in the task.
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paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
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paper_title: Thumbs Up? Sentiment Classification Using Machine Learning Techniques

paper_content:

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.
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paper_title: Opinion mining and sentiment analysis

paper_content:

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. ::: ::: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
---
paper_title: Customizing Sentiment Classifiers to New Domains: a Case Study

paper_content:

Sentiment classification is a very domainspecific problem; classifiers trained in one domain do not perform well in others. Unfortunately, many domains are lacking in large amounts of labeled data for fully-supervised learning approaches. At the same time, sentiment classifiers need to be customizable to new domains in order to be useful in practice. We attempt to address these difficulties and constraints in this paper, where we survey four different approaches to customizing a sentiment classification system to a new target domain in the absence of large amounts of labeled data. We base our experiments on data from four different domains. After establishing that naive cross-domain classification results in poor classification accuracy, we compare results obtained by using each of the four approaches and discuss their advantages, disadvantages and performance.
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paper_title: Effects Of Adjective Orientation And Gradability On Sentence Subjectivity

paper_content:

Subjectivity is a pragmatic, sentence-level feature that has important implications for text processing applications such as information extraction and information retrieval. We study the effects of dynamic adjectives, semantically oriented adjectives, and gradable adjectives on a simple subjectivity classifier, and establish that they are strong predictors of subjectivity. A novel trainable method that statistically combines two indicators of gradability is presented and evaluated, complementing existing automatic techniques for assigning orientation labels.
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paper_title: Automatic Identification Of Pro And Con Reasons In Online Reviews

paper_content:

In this paper, we present a system that automatically extracts the pros and cons from online reviews. Although many approaches have been developed for extracting opinions from text, our focus here is on extracting the reasons of the opinions, which may themselves be in the form of either fact or opinion. Leveraging online review sites with author-generated pros and cons, we propose a system for aligning the pros and cons to their sentences in review texts. A maximum entropy model is then trained on the resulting labeled set to subsequently extract pros and cons from online review sites that do not explicitly provide them. Our experimental results show that our resulting system identifies pros and cons with 66% precision and 76% recall.
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paper_title: Pulse: Mining Customer Opinions from Free Text

paper_content:

We present a prototype system, code-named Pulse, for mining topics and sentiment orientation jointly from free text customer feedback. We describe the application of the prototype system to a database of car reviews. Pulse enables the exploration of large quantities of customer free text. The user can examine customer opinion “at a glance” or explore the data at a finer level of detail. We describe a simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface.
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paper_title: Determining The Sentiment Of Opinions

paper_content:

Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.
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paper_title: Learning Extraction Patterns For Subjective Expressions

paper_content:

This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.
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paper_title: Crystal: Analyzing Predictive Opinions on the Web

paper_content:

In this paper, we present an election prediction system (Crystal) based on web users’ opinions posted on an election prediction website. Given a prediction message, Crystal first identifies which party the message predicts to win and then aggregates prediction analysis results of a large amount of opinions to project the election results. We collect past election prediction messages from the Web and automatically build a gold standard. We focus on capturing lexical patterns that people frequently use when they express their predictive opinions about a coming election. To predict election results, we apply SVM-based supervised learning. To improve performance, we propose a novel technique which generalizes n-gram feature patterns. Experimental results show that Crystal significantly outperforms several baselines as well as a non-generalized n-gram approach. Crystal predicts future elections with 81.68% accuracy.
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paper_title: Automatically Assessing Review Helpfulness

paper_content:

User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.
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paper_title: Learning Subjective Nouns Using Extraction Pattern Bootstrapping

paper_content:

We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.
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paper_title: Just How Mad Are You? Finding Strong and Weak Opinion Clauses

paper_content:

There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the strength of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. In 10-fold cross-validation experiments using support vector regression, we achieve improvements in mean-squared error over baseline ranging from 57% to 64%.
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paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
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paper_title: WordNet: An Electronic Lexical Database

paper_content:

A teaching device to acquaint dental students and also patients with endodontic root canal techniques performed by dentists and utilizing an electronic oscillator having a scale reading in electric current measurement and a pair of electrical circuit conductors being connected at one end to the terminals of the oscillator and the opposite ends thereof respectively being connectable to one or more small diameter metal wires which simulate dental reamers and files which are movable in root canal-simulating passages of uniform diameter complementary to that of said wires and formed in a transparent model of a human tooth including a root and cusp thereon and mounted in a transparent enclosure in which the root portion of the tooth extends with the cusp of the model extending above the upper end of the enclosure.
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paper_title: Towards Answering Opinion Questions: Separating Facts From Opinions And Identifying The Polarity Of Opinion Sentences

paper_content:

Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy).
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paper_title: Sentiment Classification of Movie Reviews Using Contextual Valence Shifters

paper_content:

We present two methods for determining the sentiment expressed by a movie review. The semantic orientation of a review can be positive, negative, or neutral. We examine the effect of valence shifters on classifying the reviews. We examine three types of valence shifters: negations, intensifiers, and diminishers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers and diminishers are used to increase and decrease, respectively, the degree to which a term is positive or negative. The first method classifies reviews based on the number of positive and negative terms they contain. We use the General Inquirer to identify positive and negative terms, as well as negation terms, intensifiers, and diminishers. We also use positive and negative terms from other sources, including a dictionary of synonym differences and a very large Web corpus. To compute corpus-based semantic orientation values of terms, we use their association scores with a small group of positive and negative terms. We show that extending the term-counting method with contextual valence shifters improves the accuracy of the classification. The second method uses a Machine Learning algorithm, Support Vector Machines. We start with unigram features and then add bigrams that consist of a valence shifter and another word. The accuracy of classification is very high, and the valence shifter bigrams slightly improve it. The features that contribute to the high accuracy are the words in the lists of positive and negative terms. Previous work focused on either the term-counting method or the Machine Learning method. We show that combining the two methods achieves better results than either method alone.
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paper_title: Feature Subsumption For Opinion Analysis

paper_content:

Lexical features are key to many approaches to sentiment analysis and opinion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico-syntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and performance. We use the subsumption hierarchy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing unnecessary features. We show that reducing the feature set improves performance on three opinion classification tasks, especially when combined with traditional feature selection.
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paper_title: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining

paper_content:

Opinion mining (OM) is a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. OM has a rich set of applications, ranging from tracking users’ opinions about products or about political candidates as expressed in online forums, to customer relationship management. In order to aid the extraction of opinions from text, recent research has tried to automatically determine the “PN-polarity” of subjective terms, i.e. identify whether a term that is a marker of opinionated content has a positive or a negative connotation. Research on determining whether a term is indeed a marker of opinionated content (a subjective term) or not (an objective term) has been, instead, much more scarce. In this work we describe SENTIWORDNET, a lexical resource in which each WORDNET synset s is associated to three numerical scoresObj(s), Pos(s) and Neg(s), describing how objective, positive, and negative the terms contained in the synset are. The method used to develop SENTIWORDNET is based on the quantitative analysis of the glosses associated to synsets, and on the use of the resulting vectorial term representations for semi-supervised synset classification. The three scores are derived by combining the results produced by a committee of eight ternary classifiers, all characterized by similar accuracy levels but different classification behaviour. SENTIWORDNET is freely available for research purposes, and is endowed with a Web-based graphical user interface.
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paper_title: Sentiment analysis: capturing favorability using natural language processing

paper_content:

This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.
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paper_title: A holistic lexicon-based approach to opinion mining

paper_content:

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly
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paper_title: ARSA: a sentiment-aware model for predicting sales performance using blogs

paper_content:

Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.
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paper_title: Mining WordNet For A Fuzzy Sentiment: Sentiment Tag Extraction From WordNet Glosses

paper_content:

Many of the tasks required for semantic tagging of phrases and texts rely on a list of words annotated with some semantic features. We present a method for extracting sentiment-bearing adjectives from WordNet using the Sentiment Tag Extraction Program (STEP). We did 58 STEP runs on unique non-intersecting seed lists drawn from manually annotated list of positive and negative adjectives and evaluated the results against other manually annotated lists. The 58 runs were then collapsed into a single set of 7, 813 unique words. For each word we computed a Net Overlap Score by subtracting the total number of runs assigning this word a negative sentiment from the total of the runs that consider it positive. We demonstrate that Net Overlap Score can be used as a measure of the words degree of membership in the fuzzy category of sentiment: the core adjectives, which had the highest Net Overlap scores, were identified most accurately both by STEP and by human annotators, while the words on the periphery of the category had the lowest scores and were associated with low rates of inter-annotator agreement.
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paper_title: WordNet: An Electronic Lexical Database

paper_content:

A teaching device to acquaint dental students and also patients with endodontic root canal techniques performed by dentists and utilizing an electronic oscillator having a scale reading in electric current measurement and a pair of electrical circuit conductors being connected at one end to the terminals of the oscillator and the opposite ends thereof respectively being connectable to one or more small diameter metal wires which simulate dental reamers and files which are movable in root canal-simulating passages of uniform diameter complementary to that of said wires and formed in a transparent model of a human tooth including a root and cusp thereon and mounted in a transparent enclosure in which the root portion of the tooth extends with the cusp of the model extending above the upper end of the enclosure.
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paper_title: Expanding Domain Sentiment Lexicon through Double Propagation

paper_content:

In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sentiment coherency in a sentence. In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. As the method propagates information through both sentiment words and features, we call it double propagation. The extraction rules are designed based on relations described in dependency trees. A new method is also proposed to assign polarities to newly discovered sentiment words in a domain. Experimental results show that our approach is able to extract a large number of new sentiment words. The polarity assignment method is also effective.
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paper_title: Fully Automatic Lexicon Expansion For Domain-Oriented Sentiment Analysis

paper_content:

This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to appear successively in contexts. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94% on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.
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paper_title: Yahoo! for amazon: Sentiment extraction from small talk on the web

paper_content:

Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises different classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values---tech-sector postings are related to stock index levels, and to volumes and volatility. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.
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paper_title: Mining product reputations on the Web

paper_content:

Knowing the reputations of your own and/or competitors' products is important for marketing and customer relationship management. It is, however, very costly to collect and analyze survey data manually. This paper presents a new framework for mining product reputations on the Internet. It automatically collects people's opinions about target products from Web pages, and it uses text mining techniques to obtain the reputations of those products.On the basis of human-test samples, we generate in advance syntactic and linguistic rules to determine whether any given statement is an opinion or not, as well as whether such any opinion is positive or negative in nature. We first collect statements regarding target products using a general search engine, and then, using the rules, extract opinions from among them and attach three labels to each opinion, labels indicating the positive/negative determination, the product name itself, and an numerical value expressing the degree of system confidence that the statement is, in fact, an opinion. The labeled opinions are then input into an opinion database.The mining of reputations, i.e., the finding of statistically meaningful information included in the database, is then conducted. We specify target categories using label values (such as positive opinions of product A) and perform four types of text mining: extraction of 1) characteristic words, 2) co-occurrence words, 3) typical sentences, for individual target categories, and 4) correspondence analysis among multiple target categories.Actual marketing data is used to demonstrate the validity and effectiveness of the framework, which offers a drastic reduction in the overall cost of reputation analysis over that of conventional survey approaches and supports the discovery of knowledge from the pool of opinions on the web.
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paper_title: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

paper_content:

We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general Webpages and news articles.
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paper_title: Determining Term Subjectivity And Term Orientation For Opinion Mining

paper_content:

Opinion mining is a recent subdiscipline of computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of “subjective” terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a negative connotation. This is believed to be of key importance for identifying the orientation of documents, i.e. determining whether a document expresses a positive or negative opinion about its subject matter. We contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the nonrealistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as “subjective” or “objective” is available, which is usually not the case. In this paper we confront the task of deciding whether a given term has a positive connotation, or a negative connotation, or has no subjective connotation at all; this problem thus subsumes the problem of determining subjectivity and the problem of determining orientation. We tackle this problem by testing three different variants of a semi-supervised method previously proposed for orientation detection. Our results show that determining subjectivity and orientation is a much harder problem than determining orientation alone.
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paper_title: Using WordNet to Measure Semantic Orientations of Adjectives

paper_content:

Current WordNet-based measures of distance or similarity focus almost exclusively on WordNet’s taxonomic relations. This effectively restricts their applicability to the syntactic categories of noun and verb. We investigate a graph-theoretic model of WordNet’s most important relation—synonymy—and propose measures that determine the semantic orientation of adjectives for three factors of subjective meaning. Evaluation against human judgments shows the effectiveness of the resulting measures.
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paper_title: Hidden sentiment association in Chinese web opinion mining. WWW

paper_content:

The boom of product review websites, blogs and forums on the web has attracted many research efforts on opinion mining. Recently, there was a growing interest in the finer-grained opinion mining, which detects opinions on different review features as opposed to the whole review level. The researches on feature-level opinion mining mainly rely on identifying the explicit relatedness between product feature words and opinion words in reviews. However, the sentiment relatedness between the two objects is usually complicated. For many cases, product feature words are implied by the opinion words in reviews. The detection of such hidden sentiment association is still a big challenge in opinion mining. Especially, it is an even harder task of feature-level opinion mining on Chinese reviews due to the nature of Chinese language. In this paper, we propose a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. More specially, 1) the approach clusters product features and opinion words simultaneously and iteratively by fusing both their content information and sentiment link information. 2) under the same framework, based on the product feature categories and opinion word groups, we construct the sentiment association set between the two groups of data objects by identifying their strongest n sentiment links. Moreover, knowledge from multi-source is incorporated to enhance clustering in the procedure. Based on the pre-constructed association set, our approach can largely predict opinions relating to different product features, even for the case without the explicit appearance of product feature words in reviews. Thus it provides a more accurate opinion evaluation. The experimental results demonstrate that our method outperforms the state-of-art algorithms.
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paper_title: ARSA: a sentiment-aware model for predicting sales performance using blogs

paper_content:

Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.
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paper_title: Topic sentiment mixture: modeling facets and opinions in weblogs

paper_content:

In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.
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paper_title: Extracting knowledge from evaluative text

paper_content:

Capturing knowledge from free-form evaluative texts about an entity is a challenging task. New techniques of feature extraction, polarity determination and strength evaluation have been proposed. Feature extraction is particularly important to the task as it provides the underpinnings of the extracted knowledge. The work in this paper introduces an improved method for feature extraction that draws on an existing unsupervised method. By including user-specific prior knowledge of the evaluated entity, we turn the task of feature extraction into one of term similarity by mapping crude (learned) features into a user-defined taxonomy of the entity's features. Results show promise both in terms of the accuracy of the mapping as well as the reduction in the semantic redundancy of crude features.
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paper_title: Expanding Domain Sentiment Lexicon through Double Propagation

paper_content:

In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sentiment coherency in a sentence. In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. As the method propagates information through both sentiment words and features, we call it double propagation. The extraction rules are designed based on relations described in dependency trees. A new method is also proposed to assign polarities to newly discovered sentiment words in a domain. Experimental results show that our approach is able to extract a large number of new sentiment words. The polarity assignment method is also effective.
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paper_title: Structured Models for Fine-to-Coarse Sentiment Analysis

paper_content:

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequence classification techniques using constrained Viterbi to ensure consistent solutions. The primary advantage of such a model is that it allows classification decisions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classification error relative to models trained in isolation.
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paper_title: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

paper_content:

We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general Webpages and news articles.
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paper_title: Extracting Product Features And Opinions From Reviews

paper_content:

Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces Opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products.Compared to previous work, Opine achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. Opine's novel use of relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.
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paper_title: Determining Term Subjectivity And Term Orientation For Opinion Mining

paper_content:

Opinion mining is a recent subdiscipline of computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of “subjective” terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a negative connotation. This is believed to be of key importance for identifying the orientation of documents, i.e. determining whether a document expresses a positive or negative opinion about its subject matter. We contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the nonrealistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as “subjective” or “objective” is available, which is usually not the case. In this paper we confront the task of deciding whether a given term has a positive connotation, or a negative connotation, or has no subjective connotation at all; this problem thus subsumes the problem of determining subjectivity and the problem of determining orientation. We tackle this problem by testing three different variants of a semi-supervised method previously proposed for orientation detection. Our results show that determining subjectivity and orientation is a much harder problem than determining orientation alone.
---
paper_title: A holistic lexicon-based approach to opinion mining

paper_content:

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly
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paper_title: Sentiment Learning on Product Reviews via Sentiment Ontology Tree

paper_content:

Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized. (2) Reviews or sentences mentioning several attributes associated with complicated sentiments are not dealt with very well. In this paper, we propose a novel HL-SOT approach to labeling a product's attributes and their associated sentiments in product reviews by a Hierarchical Learning (HL) process with a defined Sentiment Ontology Tree (SOT). The empirical analysis against a human-labeled data set demonstrates promising and reasonable performance of the proposed HL-SOT approach. While this paper is mainly on sentiment analysis on reviews of one product, our proposed HL-SOT approach is easily generalized to labeling a mix of reviews of more than one products.
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Title: A Review of Opinion Mining Methods for Analyzing Citizens' Contributions in Public Policy Debate

Section 1: Introduction
Description 1: Provide background information on eParticipation, its context in both Web 1.0 and Web 2.0, and the challenges posed by large quantities of unstructured textual contributions from citizens.

Section 2: Opinion Mining Objectives and Concepts
Description 2: Explain the basic concepts and objectives of opinion mining, the computational processing of opinions, and the overall categorization of opinion mining research into document/sentence-level and feature-based sentiment analysis.

Section 3: Document and Sentence Level Sentiment Analysis
Description 3: Review the main research stream that deals with sentiment analysis at both the document and sentence levels, including methodologies and tools used for classifying sentiments.

Section 4: Document-Level Sentiment Analysis
Description 4: Discuss supervised and unsupervised learning methods for sentiment analysis at the document level, including the significance of domain adaptation and the importance of polar words.

Section 5: Sentence-Level Sentiment Analysis
Description 5: Highlight the methods used for sentence-level subjectivity and sentiment classification, including bootstrapping techniques and handling of compound sentences.

Section 6: Polar Words
Description 6: Describe the different approaches for developing lists of polar (opinion) words, including manual, dictionary-based, and corpus-based methods.

Section 7: Feature-based Sentiment Analysis
Description 7: Elaborate on methods for identifying and analyzing specific object features commented on within opinionated texts to determine positive, negative, or neutral sentiments.

Section 8: Feature Extraction
Description 8: Detail the techniques for extracting object features from texts using unsupervised learning, and discuss handling of synonym issues.

Section 9: Identification of Opinion Orientation
Description 9: Provide insights into identifying the orientation of opinions expressed on particular features within a sentence, including lexicon-based approaches and handling of negations and but-clauses.

Section 10: Ontology-based Sentiment Analysis
Description 10: Discuss the use of domain-specific knowledge through ontologies for feature-based sentiment analysis, outlining the modules involved in ontology-based systems.

Section 11: Sentiment Analysis in project PADGETS
Description 11: Examine the novel approach used in the PADGETS project, incorporating Social Network Analysis theory to handle sentiment classification in languages with limited linguistic resources.

Section 12: Conclusions
Description 12: Summarize the importance of using opinion mining methods for analyzing citizens' textual contributions in public policy debates and outline a basic framework to integrate these methods effectively in eParticipation processes.