Subjectivity and Sentiment Analysis of Arabic: A Survey
---
paper_title: Cross-Linguistic Sentiment Analysis: From English to Spanish

paper_content:

We explore the adaptation of English resources and techniques for text sentiment analysis to a new language, Spanish. Our main focus is the modification of an existing English semantic orientation calculator and the building of dictionaries; however we also compare alternate approaches, including machine translation and Support Vector Machine classification. The results indicate that, although languageindependent methods provide a decent baseline performance, there is also a significant cost to automation, and thus the best path to long-term improvement is through the inclusion of language-specific knowledge and resources.
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paper_title: Using SentiWordNet for multilingual sentiment analysis

paper_content:

This paper introduces a methodology for determining polarity of text within a multilingual framework. The method leverages on lexical resources for sentiment analysis available in English (SentiWordNet). First, a document in a different language than English is translated into English using standard translation software. Then, the translated document is classified according to its sentiment into one of the classes "positive" and "negative". For sentiment classification, a document is searched for sentiment bearing words like adjectives. By means of SentiWordNet, scores for positivity and negativity are determined for these words. An interpretation of the scores then leads to the document polarity. The method is tested for German movie reviews selected from Amazon and is compared to a statistical polarity classifier based on n-grams. The results show that working with standard technology and existing sentiment analysis approaches is a viable approach to sentiment analysis within a multilingual framework.
---
paper_title: A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

paper_content:

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.
---
paper_title: Bilingual Experiments with an Arabic-English Corpus for Opinion Mining

paper_content:

Recently, Opinion Mining (OM) is receiving more attention due to the abundance of forums, blogs, ecommerce web sites, news reports and additional web sources where people tend to express their opinions. There are a number of works about Sentiment Analysis (SA) studying the task of identifying the polarity, whether the opinion expressed in a text is positive or negative about a given topic. However, most of research is focused on English texts and there are very few resources for other languages. In this work we present an Opinion Corpus for Arabic (OCA) composed of Arabic reviews extracted from specialized web pages related to movies and films using this language. Moreover, we have translated the OCA corpus into English, generating the EVOCA corpus (English Version of OCA). In the experiments carried out in this work we have used different machine learning algorithms to classify the polarity in these corpora showing that, although the experiments with EVOCA are worse than OCA, the results are comparable with other English experiments, since the loss of precision due to the translation is very slight.
---
paper_title: Unspeakable Sentences: Narration and Representation in the Language of Fiction

paper_content:

Preface Introduction 1. The expression of subjectivity and the sentences of direct and indirect speech 2. The sentence of represented speech and thought 3. Communication and the sentence of discourse 4. The sentences of narration and discourse 5. The sentence representing non-reflective consciousness and the absence of the narrator 6. The historical development of narrative style Conclusion: Narration and representation: the knowledge of the clock and the lens Notes Bibliography Name index Subject index
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paper_title: Conveying Subjectivity of a Lexicon of One Language into Another Using a Bilingual Dictionary and a Link Analysis Algorithm

paper_content:

This paper proposes a method that automatically creates a sentiment lexicon in a new language using a sentiment lexicon in a resource–rich language with only a bilingual dictionary. We resolve some of the difficulties in selecting appropriate senses when translating lexicon, and present a framework that sequentially applies an iterative link analysis algorithm to enhance the quality of lexicons of both the source and target languages. The experimental results have empirically shown to improve the sentiment lexicon in the source language as well as create a good quality lexicon in the new language.
---
paper_title: OCA: Opinion corpus for Arabic

paper_content:

Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research. © 2011 Wiley Periodicals, Inc.
---
paper_title: Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

paper_content:

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91p on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.
---
paper_title: Sentence-Level and Document-Level Sentiment Mining for Arabic Texts

paper_content:

In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.
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paper_title: The Arabic Language

paper_content:

This general introduction to the Arabic Language, now available in paperback, places special emphasis on the history and variation of the language. Concentrating on the difference between the two types of Arabic - the Classical standard language and the dialects - Kees Versteegh charts the history and development of the Arabic language from the earliest beginnings to modern times. The reader is offered a solid grounding in the structure of the language, its historical context and its use in various literary and non-literary genres, as well as an understanding of the role of Arabic as a cultural, religious and political world language. Intended as an introductory guide for students of Arabic, it will also be a useful tool for discussions both from a historical linguistic and from a socio-linguistic perspective. Coverage includes all aspects of the history of Arabic, the Arabic linguistic tradition, Arabic dialects and Arabic as a world language. Links are made between linguistic history and cultural history, while the author emphasises the role of contacts between Arabic and other languages. This important book will be an ideal text for all those wishing to acquire an understanding or develop their knowledge of the Arabic language. Key Features: * A general introduction to the Arabic language * Accessible and effective communication of information * Impeccably documented * Updated guide to further reading * User-friendly index
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paper_title: Mining Arabic Business Reviews

paper_content:

For languages with rich content over the web, business reviews are easily accessible via many known websites, e.g., Yelp.com. For languages with poor content over the web like Arabic, there are very few websites (we are actually aware of only one that is indeed unpopular) that provide business reviews. However, this does not mean that such reviews do not exist. They indeed exist unstructured in websites not originally intended for reviews, e.g., Forums and Blogs. Hence, there is a need to mine for those Arabic reviews from the web in order to provide them in the search results when a user searches for a business or a category of businesses. In this paper, we show how to extract the business reviews scattered on the web written in the Arabic language. The mined reviews are analyzed to also provide their sentiments (positive, negative or neutral). This way, we provide our users the information they need about the local businesses in the language they understand, and therefore provide a better search experience for the Middle East region, which mostly speaks Arabic.
---
paper_title: â€Yes we can?â€: Subjectivity Annotation and Tagging for the Health Domain

paper_content:

The area of Subjectivity and sentiment analysis (SSA) has been witnessing a flurry of novel research. However, only few attempts have been made to build SSA systems for the health domain. In the current study, we report efforts to partially bridge this gap. We present a new labeled corpus of professional articles collected from major Websites focused on the Obama health reform plan (OHRP). We introduce a new annotation scheme that incorporates subjectivity as well as topics directly related to the OHRP and describe a highlysuccessful SSA system that exploits the annotation. In the process, we introduce a number of novel features and a wide-coverage polarity lexicon for the health domain.
---
paper_title: A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

paper_content:

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.
---
paper_title: Integrated Machine Learning Techniques for Arabic Named Entity Recognition

paper_content:

Named Entity Recognition (NER) task has become essential to improve the performance of many NLP tasks. Its aim is to endeavor a solution to boost accurately the identification of extracted named entities. This paper presents a novel solution for Arabic Named Entity Recognition (ANER) problem. The solution is an integration approach between two machine learning techniques, namely bootstrapping semi-supervised pattern recognition and Conditional Random Fields (CRF) classifier as a supervised technique. The paper solution contributions are the exploit of pattern and word semantic fields as CRF features, the adventure of utilizing bootstrapping semisupervised pattern recognition technique in Arabic Language, and the integration success to improve the performance of its components. Moreover, as per to our knowledge, this proposed integration has not been utilized for NER task of other natural languages. Using 6-fold cross-validation experimental tests, the solution is proved that it outperforms previous CRF sole work
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paper_title: Arabic opinion mining using combined classification approach

paper_content:

In this paper, we present a combined approach that automatically extracts opinions from Arabic documents. Most research efforts in the area of opinion mining deal with English texts and little work with Arabic text. Unlike English, from our experiments, we found that using only one method on Arabic opinioned documents produce a poor performance. So, we used a combined approach that consists of three methods. At the beginning, lexicon based method is used to classify as much documents as possible. The resultant classified documents used as training set for maximum entropy method which subsequently classifies some other documents. Finally, k-nearest method used the classified documents from lexicon based method and maximum entropy as training set and classifies the rest of the documents. Our experiments showed that in average, the accuracy moved (almost) from 50% when using only lexicon based method to 60% when used lexicon based method and maximum entropy together, to 80% when using the three combined methods.
---
paper_title: Subjectivity and Sentiment Annotation of Modern Standard Arabic Newswire

paper_content:

Subjectivity and sentiment analysis (SSA) is an area that has been witnessing a flurry of novel research. However, only few attempts have been made to build SSA systems for morphologically-rich languages (MRL). In the current study, we report efforts to partially bridge this gap. We present a newly labeled corpus of Modern Standard Arabic (MSA) from the news domain manually annotated for subjectivity and domain at the sentence level. We summarize our linguistically-motivated annotation guidelines and provide examples from our corpus exemplifying the different phenomena. Throughout the paper, we discuss expression of subjectivity in natural language, combining various previously scattered insights belonging to many branches of linguistics.
---
paper_title: OCA: Opinion corpus for Arabic

paper_content:

Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research. © 2011 Wiley Periodicals, Inc.
---
paper_title: AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis

paper_content:

We present AWATIF, a multi-genre corpus of Modern Standard Arabic (MSA) labeled for subjectivity and sentiment analysis (SSA) at the sentence level. The corpus is labeled using both regular as well as crowd sourcing methods under three different conditions with two types of annotation guidelines. We describe the sub-corpora constituting the corpus and provide examples from the various SSA categories. In the process, we present our linguistically-motivated and genre-nuanced annotation guidelines and provide evidence showing their impact on the labeling task.
---
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: Multi-lingual Sentiment Analysis of Financial News Streams

paper_content:

Department of Computing, University of Surrey Guildford, Surrey, GU2 7XH, United Kingdom E-mail: y.almas@surrey.ac.uk Encouraged by the feasibility demonstration that a relatively low-cost grid environment can speed up the processing of continuous text streams of financial news in English, we have attempted to replicate our methods for automatic sentiment analysis in two major languages of the world – Arabic and Chinese. We show that our local grammar approach, developed on an archive of English (Indo-European language) texts, works equally on the typologically different Chinese (Sino-Asiatic) and Arabic (Semitic) languages. Grid Technology for Financial Modeling and Simulation February 3/4, 2006 – Palermo, (Italy)
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paper_title: Language-independent sentiment classification using three common words

paper_content:

Many methods for cross-lingual processing tasks are resource-dependent, which will not work without machine translation system or bilingual lexicon. In this paper, we propose a novel approach for multilingual sentiment classification just by few seed words. For a given language, the proposed approach learns a sentiment classifier from the initial seed words instead of any labeled data. We employ our method both in supervised learning and unsupervised learning. Experimental results demonstrate that our method relies less on external resource but performs as well as or better than the baseline.
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paper_title: Using appraisal groups for sentiment analysis

paper_content:

Little work to date in sentiment analysis (classifying texts by `positive' or `negative' orientation) has attempted to use fine-grained semantic distinctions in features used for classification. We present a new method for sentiment classification based on extracting and analyzing appraisal groups such as ``very good'' or ``not terribly funny''. An appraisal group is represented as a set of attribute values in several task-independent semantic taxonomies, based on Appraisal Theory. Semi-automated methods were used to build a lexicon of appraising adjectives and their modifiers. We classify movie reviews using features based upon these taxonomies combined with standard ``bag-of-words'' features, and report state-of-the-art accuracy of 90.2%. In addition, we find that some types of appraisal appear to be more significant for sentiment classification than others.
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paper_title: Language-Independent Sentiment Analysis Using Subjectivity and Positional Information

paper_content:

We describe a novel language-independent approach to the task of determining the polarity, positive or negative, of the author's opinion on a specific topic in natural language text. In particular, weights are assigned to attributes, individual words or word bi-grams, based on their position and on their likelihood of being subjective. The subjectivity of each attribute is estimated in a two-step process, where first the probability of being subjective is calculated for each sentence containing the attribute, and then these probabilities are used to alter the attribute's weights for polarity classification. The evaluation results on a standard dataset of movie reviews shows 89.85% classification accuracy, which rivals the best previously published results for this dataset for systems that use no additional linguistic information nor external resources.
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paper_title: Feature subsumption for sentiment classification in multiple languages

paper_content:

An open problem in machine learning-based sentiment classification is how to extract complex features that outperform simple features; figuring out which types of features are most valuable is another Most of the studies focus primarily on character or word Ngrams features, but substring-group features have never been considered in sentiment classification area before In this study, the substring-group features are extracted and selected for sentiment classification by means of transductive learning-based algorithm To demonstrate generality, experiments have been conducted on three open datasets in three different languages: Chinese, English and Spanish The experimental results show that the proposed algorithm's performance is usually superior to the best performance in related work, and the proposed feature subsumption algorithm for sentiment classification is multilingual Compared to the inductive learning-based algorithm, the experimental results also illustrate that the transductive learning-based algorithm can significantly improve the performance of sentiment classification As for term weighting, the experiments show that the “tfidf-c” outperforms all other term weighting approaches in the proposed algorithm.
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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.
---
paper_title: Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

paper_content:

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91p on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.
---
paper_title: Towards Sentiment Analysis of Financial Texts in Croatian.

paper_content:

The paper presents results of an experiment dealing with sentiment analysis of Croatian text from the domain of finance. The goal of the experiment was to design a system model for automatic detection of general sentiment and polarity phrases in these texts. We have assembled a document collection from web sources writing on the financial market in Croatia and manually annotated articles from a subset of that collection for general sentiment. Additionally, we have manually annotated a number of these articles for phrases encoding positive or negative sentiment within a text. In the paper, we provide an analysis of the compiled resources. We show a statistically significant correspondence (1) between the overall market trend on the Zagreb Stock Exchange and the number of positively and negatively accented articles within periods of trend and (2) between the general sentiment of articles and the number of polarity phrases within those articles. We use this analysis as an input for designing a rule-based local grammar system for automatic detection of polarity phrases and evaluate it on held out data. The system achieves F1-scores of 0.61 (P: 0.94, R: 0.45) and 0.63 (P: 0.97, R: 0.47) on positive and negative polarity phrases.
---
paper_title: Mining Arabic Business Reviews

paper_content:

For languages with rich content over the web, business reviews are easily accessible via many known websites, e.g., Yelp.com. For languages with poor content over the web like Arabic, there are very few websites (we are actually aware of only one that is indeed unpopular) that provide business reviews. However, this does not mean that such reviews do not exist. They indeed exist unstructured in websites not originally intended for reviews, e.g., Forums and Blogs. Hence, there is a need to mine for those Arabic reviews from the web in order to provide them in the search results when a user searches for a business or a category of businesses. In this paper, we show how to extract the business reviews scattered on the web written in the Arabic language. The mined reviews are analyzed to also provide their sentiments (positive, negative or neutral). This way, we provide our users the information they need about the local businesses in the language they understand, and therefore provide a better search experience for the Middle East region, which mostly speaks Arabic.
---
paper_title: Bilingual Experiments with an Arabic-English Corpus for Opinion Mining

paper_content:

Recently, Opinion Mining (OM) is receiving more attention due to the abundance of forums, blogs, ecommerce web sites, news reports and additional web sources where people tend to express their opinions. There are a number of works about Sentiment Analysis (SA) studying the task of identifying the polarity, whether the opinion expressed in a text is positive or negative about a given topic. However, most of research is focused on English texts and there are very few resources for other languages. In this work we present an Opinion Corpus for Arabic (OCA) composed of Arabic reviews extracted from specialized web pages related to movies and films using this language. Moreover, we have translated the OCA corpus into English, generating the EVOCA corpus (English Version of OCA). In the experiments carried out in this work we have used different machine learning algorithms to classify the polarity in these corpora showing that, although the experiments with EVOCA are worse than OCA, the results are comparable with other English experiments, since the loss of precision due to the translation is very slight.
---
paper_title: Arabic opinion mining using combined classification approach

paper_content:

In this paper, we present a combined approach that automatically extracts opinions from Arabic documents. Most research efforts in the area of opinion mining deal with English texts and little work with Arabic text. Unlike English, from our experiments, we found that using only one method on Arabic opinioned documents produce a poor performance. So, we used a combined approach that consists of three methods. At the beginning, lexicon based method is used to classify as much documents as possible. The resultant classified documents used as training set for maximum entropy method which subsequently classifies some other documents. Finally, k-nearest method used the classified documents from lexicon based method and maximum entropy as training set and classifies the rest of the documents. Our experiments showed that in average, the accuracy moved (almost) from 50% when using only lexicon based method to 60% when used lexicon based method and maximum entropy together, to 80% when using the three combined methods.
---
paper_title: OCA: Opinion corpus for Arabic

paper_content:

Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research. © 2011 Wiley Periodicals, Inc.
---
paper_title: SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media

paper_content:

In this work, we present SAMAR, a system for Subjectivity and Sentiment Analysis (SSA) for Arabic social media genres. We investigate: how to best represent lexical information; whether standard features are useful; how to treat Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results suggest that we need individualized solutions for each domain and task, but that lemmatization is a feature in all the best approaches.
---
paper_title: Sentence-Level and Document-Level Sentiment Mining for Arabic Texts

paper_content:

In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.
---
paper_title: Subjectivity and Sentiment Analysis of Modern Standard Arabic

paper_content:

Although Subjectivity and Sentiment Analysis (SSA) has been witnessing a flurry of novel research, there are few attempts to build SSA systems for Morphologically-Rich Languages (MRL). In the current study, we report efforts to partially fill this gap. We present a newly developed manually annotated corpus of Modern Standard Arabic (MSA) together with a new polarity lexicon. The corpus is a collection of newswire documents annotated on the sentence level. We also describe an automatic SSA tagging system that exploits the annotated data. We investigate the impact of different levels of preprocessing settings on the SSA classification task. We show that by explicitly accounting for the rich morphology the system is able to achieve significantly higher levels of performance.
---
paper_title: Subjectivity and Sentiment Annotation of Modern Standard Arabic Newswire

paper_content:

Subjectivity and sentiment analysis (SSA) is an area that has been witnessing a flurry of novel research. However, only few attempts have been made to build SSA systems for morphologically-rich languages (MRL). In the current study, we report efforts to partially bridge this gap. We present a newly labeled corpus of Modern Standard Arabic (MSA) from the news domain manually annotated for subjectivity and domain at the sentence level. We summarize our linguistically-motivated annotation guidelines and provide examples from our corpus exemplifying the different phenomena. Throughout the paper, we discuss expression of subjectivity in natural language, combining various previously scattered insights belonging to many branches of linguistics.
---
paper_title: AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis

paper_content:

We present AWATIF, a multi-genre corpus of Modern Standard Arabic (MSA) labeled for subjectivity and sentiment analysis (SSA) at the sentence level. The corpus is labeled using both regular as well as crowd sourcing methods under three different conditions with two types of annotation guidelines. We describe the sub-corpora constituting the corpus and provide examples from the various SSA categories. In the process, we present our linguistically-motivated and genre-nuanced annotation guidelines and provide evidence showing their impact on the labeling task.
---
paper_title: A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

paper_content:

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.
---

Title: Subjectivity and Sentiment Analysis of Arabic: A Survey

Section 1: Introduction
Description 1: Write about the importance of subjectivity and sentiment analysis (SSA) in web content mining, its applications, and the focus of the paper on Arabic SSA.

Section 2: Arabic
Description 2: Discuss the unique characteristics of the Arabic language, its regional dialects, and its prevalence on the internet.

Section 3: Corpora and Lexicons
Description 3: Review the available resources such as annotated corpora and lexicons crucial for building and testing Arabic sentiment analysis systems.

Section 4: Subjectivity and Sentiment Analysis Systems and Methods for Arabic
Description 4: Provide a detailed review of various methods employed to develop Arabic SSA systems, including language-independent and language-specific approaches.

Section 5: Language-Independent Feature Selection and Extraction
Description 5: Describe methods that use features not specific to Arabic for sentiment analysis, including algorithms and their application to Arabic.

Section 6: Standard IR and Hybrid Classifiers
Description 6: Discuss systems using traditional information retrieval methods, machine learning classifiers, and combined approaches for sentiment analysis in Arabic.

Section 7: Arabic-Specific, Social Media, and Genre-Specific Features
Description 7: Explain techniques that leverage the unique linguistic features of Arabic and consider different genres and social media data for sentiment analysis.

Section 8: Opinion Holder Extraction
Description 8: Review approaches for identifying the entities expressing opinions in Arabic texts, including pattern matching and machine learning methods.

Section 9: Conclusion
Description 9: Summarize the paper, highlighting the challenges, recommended strategies, and potential future directions for Arabic sentiment analysis research.