Sentiment Analysis: An Overview from Linguistics

Sentiment analysis is a growing field at the intersection of linguistics and computer science that attempts to automatically determine the sentiment contained in text. Sentiment can be characterized as positive or negative evaluation expressed through language. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative toward the item being reviewed. Sentiment analysis is now a common tool in the repertoire of social media analysis carried out by companies, marketers, and political analysts. Research on sentiment analysis extracts information from positive and negative words in text, from the context of those words, and from the linguistic structure of the text. This brief review examines in particular the contributions that linguistic knowledge can make to the task of automatically determining sentiment.

[1]  Yannick Versley,et al.  Subsentential Sentiment on a Shoestring: A Crosslingual Analysis of Compositional Classification , 2015, HLT-NAACL.

[2]  D. Biber,et al.  Styles of stance in English: Lexical and grammatical marking of evidentiality and affect , 1989 .

[3]  JungherrAndreas,et al.  Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions , 2012 .

[4]  C. Osgood,et al.  The Pollyanna hypothesis. , 1969 .

[5]  Joan L. Bybee,et al.  Modality in grammar and discourse , 1995 .

[6]  James P. Bagrow,et al.  Human language reveals a universal positivity bias , 2014, Proceedings of the National Academy of Sciences.

[7]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[8]  Joseph H. Greenberg,et al.  Language Universals: With Special Reference to Feature Hierarchies , 1966 .

[9]  Maite Taboada,et al.  Cross-Linguistic Sentiment Analysis: From English to Spanish , 2009, RANLP.

[10]  Nicholas Asher,et al.  Assessing Opinions in Texts , 2013 .

[11]  Antonio Moreno Ortiz,et al.  Lexicon-Based Sentiment Analysis of Twitter Messages in Spanish , 2013, Proces. del Leng. Natural.

[12]  Rada Mihalcea,et al.  Learning Multilingual Subjective Language via Cross-Lingual Projections , 2007, ACL.

[13]  Maite Taboada,et al.  Genre-Based Paragraph Classification for Sentiment Analysis , 2009, SIGDIAL Conference.

[14]  Shlomo Argamon,et al.  Extracting Appraisal Expressions , 2007, NAACL.

[15]  Stephan Oepen,et al.  Speculation and Negation: Rules, Rankers, and the Role of Syntax , 2012, CL.

[16]  Saif Mohammad,et al.  Sentiment Analysis of Short Informal Texts , 2014, J. Artif. Intell. Res..

[17]  Rosa M. Carro,et al.  Sentiment analysis in Facebook and its application to e-learning , 2014, Comput. Hum. Behav..

[18]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[19]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[20]  S. Iacus,et al.  Using Sentiment Analysis to Monitor Electoral Campaigns , 2015 .

[21]  Enrique Herrera-Viedma,et al.  Sentiment analysis: A review and comparative analysis of web services , 2015, Inf. Sci..

[22]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[23]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[24]  Liviu P. Dinu,et al.  The Naive Bayes Classifier in Opinion Mining: In Search of the Best Feature Set , 2012, CICLing.

[25]  Edward B. Royzman,et al.  Negativity Bias, Negativity Dominance, and Contagion , 2001 .

[26]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[27]  Victoria Bobicev,et al.  What Goes Around Comes Around: Learning Sentiments in Online Medical Forums , 2015, Cognitive Computation.

[28]  Fei Wang,et al.  Exploiting Discourse Relations for Sentiment Analysis , 2012, COLING.

[29]  Jorge Carrillo de Albornoz,et al.  An emotion-based model of negation, intensifiers, and modality for polarity and intensity classification , 2013, J. Assoc. Inf. Sci. Technol..

[30]  Stephanie Seneff,et al.  Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm , 2009, EMNLP.

[31]  James Pustejovsky,et al.  A factuality profiler for eventualities in text , 2008 .

[32]  Douglas Biber,et al.  Adverbial stance types in English , 1988 .

[33]  M. Taboada,et al.  The contribution of nonveridical rhetorical relations to evaluation in discourse , 2012 .

[34]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[35]  Hatem Ghorbel,et al.  Experiments in Cross-Lingual Sentiment Analysis in Discussion Forums , 2012, SocInfo.

[36]  Alistair Kennedy,et al.  SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS , 2006, Comput. Intell..

[37]  Graeme Hirst,et al.  A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing , 2014, ACL.

[38]  Klaus Krippendorff,et al.  Content Analysis: An Introduction to Its Methodology , 1980 .

[39]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

[40]  Ronen Feldman,et al.  Techniques and applications for sentiment analysis , 2013, CACM.

[41]  Charles E. Osgood,et al.  FROM YANG AND YIN TO and OR but , 1973 .

[42]  José Manuel Perea Ortega,et al.  Semantic orientation for polarity classification in Spanish reviews , 2013, Expert Syst. Appl..

[43]  Noah A. Smith,et al.  Modeling User Arguments, Interactions, and Attributes for Stance Prediction in Online Debate Forums , 2015, SDM.

[44]  Saif Mohammad,et al.  Generating High-Coverage Semantic Orientation Lexicons From Overtly Marked Words and a Thesaurus , 2009, EMNLP.

[45]  János Csirik,et al.  The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text , 2010, CoNLL Shared Task.

[46]  M. Taboada,et al.  Sentiment Classification Techniques for Tracking Literary Reputation , 2006 .

[47]  E. Traugott (Inter)subjectivity and (inter)subjectification: A reassessment , 2010 .

[48]  Mário J. Silva,et al.  Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-) , 2009, TSA@CIKM.

[49]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[50]  Nicholas Asher,et al.  Measuring the Effect of Discourse Structure on Sentiment Analysis , 2013, CICLing.

[51]  Baoxin Li,et al.  Inferring Sentiment from Web Images with Joint Inference on Visual and Social Cues: A Regulated Matrix Factorization Approach , 2021, ICWSM.

[52]  Ellen Riloff,et al.  Sarcasm as Contrast between a Positive Sentiment and Negative Situation , 2013, EMNLP.

[53]  Christopher Potts On the negativity of negation , 2010 .

[54]  R. Janney,et al.  Toward a pragmatics of emotive communication , 1994 .

[55]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.

[56]  Susan Hunston,et al.  Book Reviews: Pattern Grammar: A Corpus-Driven Approach to the Lexical Grammar of English , 2000, CL.

[57]  Gholamreza Haffari,et al.  The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis , 2013, ACL.

[58]  Annie Zaenen,et al.  Contextual Valence Shifters , 2006, Computing Attitude and Affect in Text.

[59]  Zhuo Jing-Schmidt,et al.  Negativity bias in language: A cognitive-affective model of emotive intensifiers , 2007 .

[60]  Miguel A. Alonso,et al.  A syntactic approach for opinion mining on Spanish reviews , 2013, Natural Language Engineering.

[61]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[62]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[63]  Jan Svartvik,et al.  A __ comprehensive grammar of the English language , 1988 .

[64]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[65]  Dan I. Moldovan,et al.  Retrieving implicit positive meaning from negated statements , 2013, Natural Language Engineering.

[66]  Ulli Waltinger,et al.  GermanPolarityClues: A Lexical Resource for German Sentiment Analysis , 2010, LREC.

[67]  Maite Taboada,et al.  Analyzing Appraisal Automatically , 2004 .

[68]  A. Utsumi Verbal irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony☆ , 2000 .

[69]  Susan Hunston,et al.  Corpus Approaches to Evaluation: Phraseology and Evaluative Language , 2010 .

[70]  Laurence R. Horn A Natural History of Negation , 1989 .

[71]  Kang Liu,et al.  Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu , 2015, CL.

[72]  Martyn Stewart The language of praise and criticism in a student evaluation survey , 2015 .

[73]  Mitsuru Ishizuka,et al.  HILDA: A Discourse Parser Using Support Vector Machine Classification , 2010, Dialogue Discourse.

[74]  Elizabeth Closs Traugott,et al.  Subjectivity and subjectivisation: Subjectification in grammaticalisation , 1995 .

[75]  Lee Gillam,et al.  Sentiments on a Grid: Analysis of Streaming News and Views , 2006, LREC.

[76]  Noah A. Smith,et al.  A Penny for Your Tweets: Campaign Contributions and Capitol Hill Microblogs , 2013, ICWSM.

[77]  Mike Y. Chen,et al.  Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web , 2001 .

[78]  A. Verhagen Constructions of Intersubjectivity: Discourse, Syntax, and Cognition , 2007 .

[79]  Alex Lascarides,et al.  Edinburgh Research Explorer Using automatically labelled examples to classify rhetorical relations: an assessment , 2022 .

[80]  Rada Mihalcea,et al.  Multilingual Subjectivity Analysis Using Machine Translation , 2008, EMNLP.

[81]  Ellen Riloff,et al.  Creating Subjective and Objective Sentence Classifiers from Unannotated Texts , 2005, CICLing.

[82]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[83]  Claire Cardie,et al.  Automatically Generating Annotator Rationales to Improve Sentiment Classification , 2010, ACL.

[84]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[85]  Janyce Wiebe,et al.  RECOGNIZING STRONG AND WEAK OPINION CLAUSES , 2006, Comput. Intell..

[86]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[87]  Vasileios Hatzivassiloglou,et al.  Predicting the Semantic Orientation of Adjectives , 1997, ACL.

[88]  K. Bretonnel Cohen,et al.  Sentiment Analysis of Suicide Notes: A Shared Task , 2012, Biomedical informatics insights.

[89]  S. Thompson “Object complements” and conversation towards a realistic account , 2002 .

[90]  Kun Fu,et al.  Joint model for subsentence‐level sentiment analysis with Markov logic , 2015, J. Assoc. Inf. Sci. Technol..

[91]  Jennifer Foster,et al.  Sentiment Analysis of Political Tweets: Towards an Accurate Classifier , 2013 .

[92]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[93]  János Csirik,et al.  The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.

[94]  Janyce Wiebe,et al.  Learning Subjective Language , 2004, CL.

[95]  Janyce Wiebe,et al.  Tracking Point of View in Narrative , 1994, Comput. Linguistics.

[96]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

[97]  Brendan T. O'Connor,et al.  Learning to Extract International Relations from Political Context , 2013, ACL.

[98]  David Bamman,et al.  Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.

[99]  Ahmed Rafea,et al.  Improving Document-Level Sentiment Classification Using Contextual Valence Shifters , 2012, NLDB.

[100]  Sanjiv Ranjan Das Yahoo! for Amazon : Opinion Extraction from Small Talk on the Web , 2001 .

[101]  Diego Reforgiato Recupero,et al.  AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis , 2008, IEEE Intelligent Systems.

[102]  Monica Amor Felix Gonzalez‐Torres 1957–1996 , 1995 .

[103]  Peter R. R. White,et al.  The language of evaluation , 2005 .

[104]  Johanna Nichols,et al.  Evidentiality: The Linguistic Coding of Epistemology , 1986 .

[105]  Dieter Stein,et al.  Subjectivity and subjectivisation: Subjective meanings and the history of inversions in English , 1995 .

[106]  Christopher Potts,et al.  Did It Happen? The Pragmatic Complexity of Veridicality Assessment , 2012, CL.

[107]  Xiaojun Wan,et al.  Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis , 2008, EMNLP.

[108]  F. Palmer,et al.  Mood and modality , 1986 .

[109]  Vanessa Wei Feng,et al.  RST-style Discourse Parsing and Its Applications in Discourse Analysis , 2015 .

[110]  Roser Morante,et al.  Modality and Negation: An Introduction to the Special Issue , 2012, CL.

[111]  Manfred Stede,et al.  Sentiment Analysis: What's Your Opinion? , 2014, Text Mining.

[112]  Maite Taboada,et al.  Discourse markers and coherence relations: Comparison across markers, languages and modalities , 2012 .

[113]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

[114]  Claire Cardie,et al.  Recognizing and Organizing Opinions Expressed in the World Press , 2003, New Directions in Question Answering.

[115]  L. L. Shaw,et al.  Differentiating affect, mood, and emotion: Toward functionally based conceptual distinctions. , 1992 .

[116]  Ellen Riloff,et al.  Learning subjective nouns using extraction pattern bootstrapping , 2003, CoNLL.

[117]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[118]  Shafiq R. Joty,et al.  CODRA: A Novel Discriminative Framework for Rhetorical Analysis , 2015, CL.

[119]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[120]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[121]  S. R. El-Beltagy,et al.  Open issues in the sentiment analysis of Arabic social media: A case study , 2013, 2013 9th International Conference on Innovations in Information Technology (IIT).

[122]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[123]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[124]  Janyce Wiebe,et al.  Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis , 2009, CL.

[125]  Arjun Mukherjee,et al.  Detecting Campaign Promoters on Twitter Using Markov Random Fields , 2014, 2014 IEEE International Conference on Data Mining.

[126]  Rada Mihalcea,et al.  Sense-level Subjectivity in a Multilingual Setting , 2011, Comput. Speech Lang..

[127]  Alistair Kennedy,et al.  Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters , 2005 .

[128]  W. Louw Irony in the Text or Insincerity in the Writer? — The Diagnostic Potential of Semantic Prosodies , 1993 .

[129]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[130]  Ann Banfield,et al.  Unspeakable Sentences : Narration and Representation in the Language of Fiction , 1982 .

[131]  Rob Malouf,et al.  A Preliminary Investigation into Sentiment Analysis of Informal Political Discourse , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[132]  Miguel A. Alonso,et al.  On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages , 2015, J. Assoc. Inf. Sci. Technol..

[133]  Diego Reforgiato Recupero,et al.  Sentiment Analysis: Adjectives and Adverbs are Better than Adjectives Alone , 2007, ICWSM.

[134]  Rongrong Ji,et al.  Large-scale visual sentiment ontology and detectors using adjective noun pairs , 2013, ACM Multimedia.

[135]  Simon Clematide,et al.  Evaluation and Extension of a Polarity Lexicon for German , 2010 .