A survey on the role of negation in sentiment analysis
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paper_title: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based On Minimum Cuts

paper_content:

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
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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: Examining The Role Of Linguistic Knowledge Sources In The Automatic Identification And Classification Of Reviews

paper_content:

This paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system.
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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: 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.
---
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: Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis

paper_content:

Determining the polarity of a sentiment-bearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learning-based approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%). We also find that "content-word negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered.
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paper_title: Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis

paper_content:

Text is not only an important medium to describe facts and events, but also to effectively communicate information about the writer's (positive or negative) sentiment underlying an opinion, and an affect or emotion (e.g. happy, fearful, surprised etc.). We consider sentiment assessment and emotion sensing from text as two different problems, whereby sentiment assessment is a prior task to emotion sensing. This paper presents an approach to sentiment assessment, i.e. the recognition of negative or positive sense of a sentence. We perform semantic dependency analysis on the semantic verb frames of each sentence, and apply a set of rules to each dependency relation to calculate the contextual valence of the whole sentence. By employing a domain-independent, rule-based approach, our system is able to automatically identify sentence-level sentiment. Empirical results indicate that our system outperforms another state-of-the-art approach.
---
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.
---
paper_title: Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis

paper_content:

Determining the polarity of a sentiment-bearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learning-based approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%). We also find that "content-word negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered.
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paper_title: The effect of negation on sentiment analysis and retrieval effectiveness

paper_content:

We investigate the problem of determining the polarity of sentiments when one or more occurrences of a negation term such as "not" appear in a sentence. The concept of the scope of a negation term is introduced. By using a parse tree and typed dependencies generated by a parser and special rules proposed by us, we provide a procedure to identify the scope of each negation term. Experimental results show that the identification of the scope of negation improves both the accuracy of sentiment analysis and the retrieval effectiveness of opinion retrieval.
---
paper_title: Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis

paper_content:

Determining the polarity of a sentiment-bearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learning-based approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%). We also find that "content-word negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered.
---
paper_title: Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis

paper_content:

Text is not only an important medium to describe facts and events, but also to effectively communicate information about the writer's (positive or negative) sentiment underlying an opinion, and an affect or emotion (e.g. happy, fearful, surprised etc.). We consider sentiment assessment and emotion sensing from text as two different problems, whereby sentiment assessment is a prior task to emotion sensing. This paper presents an approach to sentiment assessment, i.e. the recognition of negative or positive sense of a sentence. We perform semantic dependency analysis on the semantic verb frames of each sentence, and apply a set of rules to each dependency relation to calculate the contextual valence of the whole sentence. By employing a domain-independent, rule-based approach, our system is able to automatically identify sentence-level sentiment. Empirical results indicate that our system outperforms another state-of-the-art approach.
---
paper_title: The effect of negation on sentiment analysis and retrieval effectiveness

paper_content:

We investigate the problem of determining the polarity of sentiments when one or more occurrences of a negation term such as "not" appear in a sentence. The concept of the scope of a negation term is introduced. By using a parse tree and typed dependencies generated by a parser and special rules proposed by us, we provide a procedure to identify the scope of each negation term. Experimental results show that the identification of the scope of negation improves both the accuracy of sentiment analysis and the retrieval effectiveness of opinion retrieval.
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paper_title: A Metalearning Approach to Processing the Scope of Negation

paper_content:

Finding negation signals and their scope in text is an important subtask in information extraction. In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system combines several classifiers and works in two phases. To investigate the robustness of the approach, the system is tested on the three subcorpora of the BioScope corpus representing different text types. It achieves the best results to date for this task, with an error reduction of 32.07% compared to current state of the art results.
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paper_title: Learning the Scope of Negation in Biomedical Texts

paper_content:

In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of these negation signals. Our approach to negation detection differs in two main aspects from existing research on negation. First, we focus on finding the scope of negation signals, instead of determining whether a term is negated or not. Second, we apply supervised machine learning techniques, whereas most existing systems apply rule-based algorithms. As far as we know, this way of approaching the negation scope finding task is novel.
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paper_title: The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts

paper_content:

This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus). The corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief annotator -- also responsible for setting up the annotation guidelines -- who resolved cases where the annotators disagreed. We will report our statistics on corpus size, ambiguity levels and the consistency of annotations.
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paper_title: Using Morphological and Syntactic Structures for Chinese Opinion Analysis

paper_content:

This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection.
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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: Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm

paper_content:

This paper presents a parse-and-paraphrase paradigm to assess the degrees of sentiment for product reviews. Sentiment identification has been well studied; however, most previous work provides binary polarities only (positive and negative), and the polarity of sentiment is simply reversed when a negation is detected. The extraction of lexical features such as unigram/bigram also complicates the sentiment classification task, as linguistic structure such as implicit long-distance dependency is often disregarded. In this paper, we propose an approach to extracting adverb-adjective-noun phrases based on clause structure obtained by parsing sentences into a hierarchical representation. We also propose a robust general solution for modeling the contribution of adverbials and negation to the score for degree of sentiment. In an application involving extracting aspect-based pros and cons from restaurant reviews, we obtained a 45% relative improvement in recall through the use of parsing methods, while also improving precision.
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paper_title: Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text

paper_content:

We describe and evaluate a new method of automatic seed word selection for un-supervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not require any annotated training data; it only requires information about commonly occurring negations and adverbials. Unsupervised techniques are promising for this task since they avoid problems of domain-dependency typically associated with supervised methods. The results obtained are close to those of supervised classifiers and sometimes better, up to an F1 of 92%.
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paper_title: Icwsm – a great catchy name: Semi-supervised recognition of sarcastic sentences in product reviews

paper_content:

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semisupervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.
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paper_title: Subjectivity Word Sense Disambiguation

paper_content:

Many approaches to opinion and sentiment analysis rely on lexicons of words that may be used to express subjectivity. These are compiled as lists of keywords, rather than word meanings (senses). However, many keywords have both subjective and objective senses. False hits -- subjectivity clues used with objective senses -- are a significant source of error in subjectivity and sentiment analysis. This talk will focus on sense-level opinion and sentiment analysis. First, I will give the results of a study showing that even words judged in previous work to be reliable opinion clues have significant degrees of subjectivity sense ambiguity. Then, we will consider the task of distinguishing between the subjective and objective senses of words in a dictionary, and the related task of creating "usage inventories" of opinion clues. Given such distinctions, the next step is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses (we call this task "SWSD"). We will see evidence that SWSD is more feasible than full word sense disambiguation, because it is more coarse grained -- often, the exact sense need not be pinpointed, and that SWSD can be exploited to improve the performance of opinion and sentiment analysis systems via sense-aware classification. Finally, I will discuss experiments in acquiring SWSD data, via token-based context discrimination where the context vector representation is adapted to distinguish between subjective and objective contexts, and the clustering process is enriched by pair-wise constraints, making it semi-supervised.
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paper_title: Recognizing Stances in Online Debates

paper_content:

This paper presents an unsupervised opinion analysis method for debate-side classification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classification task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods.
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Title: A Survey on the Role of Negation in Sentiment Analysis
Section 1: Introduction
Description 1: This section introduces the background, significance, and objectives of sentiment analysis, distinguishing it from subjectivity detection, and the role of negation in polarity classification.

Section 2: Motivation
Description 2: This section discusses the influence of surface-level negation on the polarity of opinions and highlights the complexities associated with modeling negation in sentiment analysis.

Section 3: The Survey
Description 3: This section outlines the focus of the survey on various computational approaches for negation modeling in sentiment analysis.

Section 4: Negation and Bag of Words in Supervised Machine Learning
Description 4: This section reviews how negation is handled in bag-of-words representations within supervised machine learning approaches and the challenges associated with this method.

Section 5: Incorporating Negation in Models that Include Knowledge of Polar Expressions -Early Works
Description 5: This section discusses early efforts to incorporate negation using knowledge of polar expressions and polarity lexicons in rule-based and supervised classifiers.

Section 6: Contextual Valence Shifters
Description 6: This section covers the introduction of contextual valence shifting models which adjust the polarity scores of expressions based on surrounding context.

Section 7: Features for Negation Modeling
Description 7: This section details various features used in machine learning for negation modeling, organized into negation features, shifter features, and polarity modification features.

Section 8: Other Approaches
Description 8: This section reviews additional recent work related to negation modeling in sentiment analysis.

Section 9: Semantic Composition
Description 9: This section explores methods using compositional semantics to determine the polarity of text spans, including the use of syntactic and abstract representation levels.

Section 10: Shallow Semantic Composition
Description 10: This section discusses simpler, rule-based approaches to semantic composition and their use in negation modeling.

Section 11: Scope Modeling
Description 11: This section examines different methods for modeling the scope of negation to enhance polarity classification accuracy.

Section 12: Negation within Words
Description 12: This section addresses cases where negation and negated content occur within individual words and the need for morphological analysis.

Section 13: Negation in Various Languages
Description 13: This section compares how negation is modeled in different languages and the challenges posed by language-specific structures.

Section 14: Bad and Not Good are Not the Same
Description 14: This section critiques the oversimplification of treating "not bad" as equivalent to "good" and proposes a more nuanced compositional model for negation and intensifiers.

Section 15: Using Negations in Lexicon Induction
Description 15: This section describes the use of negation patterns in the unsupervised induction of polarity lexicons.

Section 16: Irony -The Big Challenge
Description 16: This section discusses the challenges posed by irony in sentiment analysis and approaches for detecting ironic statements.

Section 17: Limits of Negation Modeling in Sentiment Analysis
Description 17: This section explores the limitations of current negation modeling approaches in sentiment analysis, including issues related to ambiguous polar expressions and the need for external world knowledge.

Section 18: Conclusion
Description 18: This section summarizes the survey findings and emphasizes the importance of negation modeling while highlighting areas for future research.