skip to main content
survey

Like It or Not: A Survey of Twitter Sentiment Analysis Methods

Published:30 June 2016Publication History
Skip Abstract Section

Abstract

Sentiment analysis in Twitter is a field that has recently attracted research interest. Twitter is one of the most popular microblog platforms on which users can publish their thoughts and opinions. Sentiment analysis in Twitter tackles the problem of analyzing the tweets in terms of the opinion they express. This survey provides an overview of the topic by investigating and briefly describing the algorithms that have been proposed for sentiment analysis in Twitter. The presented studies are categorized according to the approach they follow. In addition, we discuss fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention. Resources that have been used in the Twitter sentiment analysis literature are also briefly presented. The main contributions of this survey include the presentation of the proposed approaches for sentiment analysis in Twitter, their categorization according to the technique they use, and the discussion of recent research trends of the topic and its related fields.

References

  1. Apoorv Agarwal, Boyi Xie, Ilia Vovsha, Owen Rambow, and Rebecca Passonneau. 2011. Sentiment analysis of twitter data. In Proceedings of the Workshop on Languages in Social Media (LSM’11). Association for Computational Linguistics, Stroudsburg, PA, USA, 30--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Fotis Aisopos, George Papadakis, and Theodora Varvarigou. 2011. Sentiment analysis of social media content using n-gram graphs. In Proceedings of the 3rd ACM SIGMM International Workshop on Social Media (WSM’11). ACM, New York, NY, 9--14. DOI:http://dx.doi.org/10.1145/2072609.2072614 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Giambattista Amati, Marco Bianchi, and Giuseppe Marcone. 2014. Sentiment estimation on twitter. In Proceedings of the 5th Italian Information Retrieval Workshop (IIR’14), Vol. 1127. CEUR Workshop Proceedings, 39--50.Google ScholarGoogle Scholar
  4. Xiaoran An, R. Auroop Ganguly, Yi Fang, B. Steven Scyphers, M. Ann Hunter, and G. Jennifer Dy. 2014. Tracking climate change opinions from twitter data. In Proceedings of the Workshop on Data Science for Social Good Held in Conjunction with KDD 2014. ACM, New York, NY.Google ScholarGoogle Scholar
  5. T. Amir Asiaee, Mariano Tepper, Arindam Banerjee, and Guillermo Sapiro. 2012. If you are happy and you know it... tweet. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). ACM, New York, NY, 1602--1606. DOI:http://dx.doi.org/10.1145/2396761.2398481 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Nathan Aston, Jacob Liddle, and Wei Hu. 2014. Twitter sentiment in data streams with perceptron. J. Comput. Commun. 2 (2014), 11--16. DOI:http://dx.doi.org/10.4236/jcc.2014.23002Google ScholarGoogle ScholarCross RefCross Ref
  7. Sitaram Asur and Bernardo A. Huberman. 2010. Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (WI-IAT’10). IEEE Computer Society, Washington, DC, 492--499. DOI:http://dx.doi.org/10.1109/WI-IAT.2010.63 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the 7th International Language Resources and Evaluation Conference (LREC’10). European Language Resources Association (ELRA), 2200--2204.Google ScholarGoogle Scholar
  9. Ricardo Baeza-Yates and Luz Rello. 2011. How bad do you spell? The lexical quality of social media. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media: The Future of the Social Web Workshop (ICWSM’11). AAAI Press.Google ScholarGoogle Scholar
  10. Akshat Bakliwal, Piyush Arora, Senthil Madhappan, Nikhil Kapre, Mukesh Singh, and Vasudeva Varma. 2012. Mining sentiments from tweets. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis (WASSA’12). Association for Computational Linguistics, Stroudsburg, PA, 11--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Francesco Barbieri and Horacio Saggion. 2014. Modelling irony in twitter: Feature analysis and evaluation. In Proceedings of the 9th International Language Resources and Evaluation Conference (LREC’14). European Language Resources Association (ELRA), 4258--4264.Google ScholarGoogle Scholar
  12. Luciano Barbosa and Junlan Feng. 2010. Robust sentiment detection on twitter from biased and noisy data. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (COLING’10). Association for Computational Linguistics, Stroudsburg, PA, 36--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Yoshua Bengio, Aaron Courville, and Pierre Vincent. 2013. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 8 (2013), 1798--1828. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Adam Bermingham and Alan Smeaton. 2010. Classifying sentiment in microblogs: Is brevity an advantage? In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). ACM, New York, NY, 1833--1836. DOI:http://dx.doi.org/10.1145/1871437.1871741 Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Albert Bifet and Eibe Frank. 2010. Sentiment knowledge discovery in twitter streaming data. In Proceedings of the 13th International Conference on Discovery Science (DS’10). Springer-Verlag, Berlin, 1--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Johan Bollen, Huina Mao, and Xiaojun Zeng. 2011. Twitter mood predicts the stock market. J. Comput. Sci. 2, 1 (2011), 1--8. DOI:http://dx.doi.org/10.1016/j.jocs.2010.12.007Google ScholarGoogle ScholarCross RefCross Ref
  17. Johan Bollen and Alberto Pepe. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM’11). AAAI Press, 450--453.Google ScholarGoogle Scholar
  18. Nibir Nayan Bora. 2012. Summarizing public opinions in tweets. Int. Jo. Comput. Ling. Appl. 3, 1 (2012), 41--55.Google ScholarGoogle Scholar
  19. Margaret M. Bradley and Peter J. Lang. 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. Technical Report. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.Google ScholarGoogle Scholar
  20. Felipe Bravo-Marquez, Marcelo Mendoza, and Barbara Poblete. 2013. Combining strengths, emotions and polarities for boosting twitter sentiment analysis. In Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM’13). ACM, New York, NY, 2:1--2:9. DOI:http://dx.doi.org/10.1145/2502069.2502071 Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Samuel Brody and Nicholas Diakopoulos. 2011. Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: Using word lengthening to detect sentiment in microblogs. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP’11). Association for Computational Linguistics, Stroudsburg, PA, USA, 562--570. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Tawunrat Chalothorn and Jeremy Ellman. 2013. Tjp: Using twitter to analyze the polarity of contexts. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics, 375--379.Google ScholarGoogle Scholar
  23. Sam Clark and Richard Wicentowski. 2013. SwatCS: Combining simple classifiers with estimated accuracy. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics, 425--429.Google ScholarGoogle Scholar
  24. Anqi Cui, Min Zhang, Yiqun Liu, and Shaoping Ma. 2011. Emotion tokens: Bridging the gap among multilingual twitter sentiment analysis. In Proceedings of the 7th Asia Conference on Information Retrieval Technology (AIRS’11). Springer-Verlag, Berlin, 238--249. DOI:http://dx.doi.org/10.1007/978-3-642-25631-8_22 Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Luigi Curini, Stefano Iacus, and Luciano Canova. 2014. Measuring idiosyncratic happiness through the analysis of twitter: An application to the Italian case. Social Indicators Research 121, 2 (2014), 525--542. DOI:http://dx.doi.org/10.1007/s11205-014-0646-2Google ScholarGoogle ScholarCross RefCross Ref
  26. Nádia F. da Silva, Eduardo R. Hruschka, and Estevam R. Hruschka. 2014. Tweet sentiment analysis with classifier ensembles. Decision Supp. Syst. 66 (July 2014), 170--179. DOI:http://dx.doi.org/10.1016/j.dss.2014.07.003 Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010. Enhanced sentiment learning using twitter hashtags and smileys. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (COLING’10). Association for Computational Linguistics, Stroudsburg, PA, 241--249. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. William Deitrick and Wei Hu. 2013. Mutually enhancing community detection and sentiment analysis on twitter networks. J. Data Anal. Inform. Process. 1, 3 (2013), 19--29.Google ScholarGoogle ScholarCross RefCross Ref
  29. Xiaowen Ding, Bing Liu, and Philip S. Yu. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM’08). ACM, New York, NY, 231--240. DOI:http://dx.doi.org/10.1145/1341531.1341561 Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Stroudsburg, PA, 49--54.Google ScholarGoogle ScholarCross RefCross Ref
  31. Paul Ekman. 1992. An argument for basic emotions. Cogn. Emotion 6, 3/4 (1992), 169--200. DOI:http://dx.doi.org/10.1080/02699939208411068Google ScholarGoogle ScholarCross RefCross Ref
  32. Andrea Esuli and Fabrizio Sebastiani. 2006. SENTIWORDNET: A publicly available lexical resource for opinion mining. In Proceedings of the 5th International Language Resources and Evaluation Conference (LREC’06). European Language Resources Association (ELRA), 417--422. DOI:http://dx.doi.org/10.1.1.61.7217Google ScholarGoogle Scholar
  33. Song Feng, Ritwik Bose, and Yejin Choi. 2011. Learning general connotation of words using graph-based algorithms. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP’11). Association for Computational Linguistics, Stroudsburg, PA, 1092--1103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Wei Gao and Fabrizio Sebastiani. 2015. Tweet sentiment: From classification to quantification. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (ASONAM’15). ACM, New York, NY, 97--104. DOI:http://dx.doi.org/10.1145/2808797.2809327 Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Manoochehr Ghiassi, James Skinner, and David Zimbra. 2013. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40, 16 (2013), 6266--6282. DOI:http://dx.doi.org/10.1016/j.eswa.2013.05.057 Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Anastasia Giachanou and Fabio Crestani. 2016. Opinion retrieval in twitter: Is proximity effective? In Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC’16). ACM, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Anastasia Giachanou, Morgan Harvey, and Fabio Crestani. 2016. Topic-specific stylistic variations for opinion retrieval on twitter. In Proceedings of the 38th European Conference on Advances in Information Retrieval (ECIR’16). Springer International Publishing, Berlin, 466--478. DOI:http://dx.doi.org/10.1007/978-3-319-30671-1_34Google ScholarGoogle ScholarCross RefCross Ref
  38. Raymond W. Gibbs. 1986. On the psycholinguistics of sarcasm. J. Exper. Psychol. Gen. 115 (1986), 3--15. Issue 1. DOI:http://dx.doi.org/10.1037/0096-3445.115.1.3Google ScholarGoogle ScholarCross RefCross Ref
  39. Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter Sentiment Classification Using Distant Supervision. Technical Report. Standford.Google ScholarGoogle Scholar
  40. Hussam Hamdan, Frederic Béchet, and Patrice Bellot. 2013. Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13), Vol. 2. Association for Computational Linguistics, 455--459.Google ScholarGoogle Scholar
  41. Ming Hao, Christian Rohrdantz, Halldór Janetzko, Umeshwar Dayal, Daniel A. Keim, Lars-Erik Haug, and Mei-Chun Hsu. 2011. Visual sentiment analysis on twitter data streams. In Proceedings of the 2011 IEEE Symposium on Visual Analytics Science and Technology (VAST’11). IEEE, 277--278.Google ScholarGoogle ScholarCross RefCross Ref
  42. Ammar Hassan, Ahmed Abbasi, and Daniel Zeng. 2013. Twitter sentiment analysis: A bootstrap ensemble framework. In Proceedings of the International Conference on Social Computing (SocialCom’13). IEEE Computer Society, 357--364. DOI:http://dx.doi.org/10.1109/SocialCom.2013.56 Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Xia Hu, Jiliang Tang, Huiji Gao, and Huan Liu. 2013a. Unsupervised sentiment analysis with emotional signals. In Proceedings of the 22nd International Conference on World Wide Web (WWW’13). ACM, New York, NY, 607--618. DOI:http://dx.doi.org/10.1145/2488388.2488442 Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Xia Hu, Lei Tang, Jiliang Tang, and Huan Liu. 2013b. Exploiting social relations for sentiment analysis in microblogging. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM’13). ACM, New York, NY, 537--546. DOI:http://dx.doi.org/10.1145/2433396.2433465 Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Ozan Irsoy and Claire Cardie. 2014. Opinion mining with deep recurrent neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). Association for Computational Linguistics, Stroudsburg, PA, 720--728.Google ScholarGoogle ScholarCross RefCross Ref
  46. Olivier Janssens, Steven Verstockt, Erik Mannens, Sofie Van Hoecke, and Rik Van De Walle. 2014. Influence of weak labels for emotion recognition of tweets. In Proceedings of the 2nd International Conference in Mining Intelligence and Knowledge Exploration (MIKE 2014). Springer, Berlin, 108--118. DOI:http://dx.doi.org/10.1007/978-3-319-13817-6_12Google ScholarGoogle ScholarCross RefCross Ref
  47. Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. Target-dependent twitter sentiment classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1 (HLT’11). Association for Computational Linguistics, Stroudsburg, PA, 151--160. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Farhan Hassan Khan, Saba Bashir, and Usman Qamar. 2014. TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Supp. Syst. 57 (Jan. 2014), 245--257. DOI:http://dx.doi.org/10.1016/j.dss.2013.09.004 Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Vinh Ngoc Khuc, Chaitanya Shivade, Rajiv Ramnath, and Jay Ramanathan. 2012. Towards building large-scale distributed systems for twitter sentiment analysis. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC’12). ACM, New York, NY, 459. DOI:http://dx.doi.org/10.1145/2245276.2245364 Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Soo-Min Kim and Eduard Hovy. 2004. Determining the sentiment of opinions. In Proceedings of the 20th International Conference on Computational Linguistics (COLING’04). Association for Computational Linguistics, Stroudsburg, PA, Article 1367. DOI:http://dx.doi.org/10.3115/1220355.1220555 Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. 2014. Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50 (2014), 723--762. Google ScholarGoogle ScholarCross RefCross Ref
  52. Nadin Kökciyan, Arda Çelebi, Arzucan Özgür, and Suzan Üsküdarl. 2013. BOUNCE: Sentiment classification in twitter using rich feature sets. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics, 554--561.Google ScholarGoogle Scholar
  53. Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, and Nick Bassiliades. 2013. Ontology-based sentiment analysis of twitter posts. Expert Syst. Appl. 40, 10 (2013), 4065--4074. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Peter Korenek and Marián Šimko. 2014. Sentiment analysis on microblog utilizing appraisal theory. World Wide Web 17, 4 (July 2014), 847--867. DOI:http://dx.doi.org/10.1007/s11280-013-0247-z Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Efthymios Kouloumpis, Theresa Wilson, and Johanna Moore. 2011. Twitter sentiment analysis: The good the bad and the omg!. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM’11). AAAI Press, 538--541.Google ScholarGoogle Scholar
  56. Akshi Kumar and Teeja Mary Sebastian. 2012. Sentiment analysis on twitter. Int. J. Comput. Sci. Issues 9, 4 (2012), 372--378.Google ScholarGoogle Scholar
  57. Christine Liebrecht, Florian Kunneman, and Antal van den Bosch. 2013. The perfect solution for detecting sarcasm in tweets #not. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA’13). Association for Computational Linguistics, Stroudsburg, PA, 29--37.Google ScholarGoogle Scholar
  58. Jimmy Lin and Alek Kolcz. 2012. Large-scale machine learning at twitter. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD’12). ACM, New York, NY, 793--804. DOI:http://dx.doi.org/10.1145/2213836.2213958 Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5, 1 (May 2012), 1--167. DOI:http://dx.doi.org/10.2200/S00416ED1V01Y201204HLT016Google ScholarGoogle ScholarCross RefCross Ref
  60. Bing Liu and Lei Zhang. 2012. A survey of opinion mining and sentiment analysis. In Mining Text Data. Springer, New York, NY, 415--463.Google ScholarGoogle Scholar
  61. Kun-Lin Liu, Wu-Jun Li, and Minyi Guo. 2012. Emoticon smoothed language models for twitter sentiment analysis. In Proceedings of the 26th AAAI Conference on Artificial Intelligence Emoticon. AAAI Press, Palo Alto, CA, 1678--1684. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Zhunchen Luo, Miles Osborne, and Ting Wang. 2013a. An effective approach to tweets opinion retrieval. World Wide Web 18, 3 (2013), 545--566. DOI:http://dx.doi.org/10.1007/s11280-013-0268-7 Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Zhunchen Luo, Jintao Tang, and Ting Wang. 2013b. Propagated opinion retrieval in twitter. In Proceedings of the 14th International Conference on Web Information Systems Engineering (WISE’13). Springer, Berlin, 16--28. DOI:http://dx.doi.org/10.1007/978-3-642-41154-0_2Google ScholarGoogle ScholarCross RefCross Ref
  64. Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1 (HLT’11). Association for Computational Linguistics, Stroudsburg, PA, 142--150. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Eugenio Martínez-Cámara, Teresa M. Martín-Valdivia, Alfonso L. Ureña López, and Arturo Montejo-Ráez. 2012. Sentiment analysis in twitter. Nat, Lang, Eng, 20, 01 (Nov. 2012), 1--28. DOI:http://dx.doi.org/10.1017/S1351324912000332Google ScholarGoogle Scholar
  66. Eugenio Martínez-Cámara, Arturo Montejo-Ráez, Teresa M. Martín-Valdivia, and Alfonso L. Ureña López. 2013. Sinai: Machine learning and emotion of the crowd for sentiment analysis in microblogs. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics.Google ScholarGoogle Scholar
  67. Diana Maynard and Mark Greenwood. 2014. Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In Proceedings of the 9th International Language Resources and Evaluation Conference (LREC’14). European Language Resources Association (ELRA), 4238--4243.Google ScholarGoogle Scholar
  68. Akshay Minocha and Navjyoti Singh. 2012. Generating domain specific sentiment lexicons using the web directory. Adv, Comput,: Int, J, 3, 5 (2012), 45--51.Google ScholarGoogle Scholar
  69. Lewis Mitchell, Morgan R. Frank, Kameron Decker Harris, Peter Sheridan Dodds, and Christopher M. Danforth. 2013. The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS One 8, 5 (2013), e64417.Google ScholarGoogle ScholarCross RefCross Ref
  70. Saif M. Mohammad. 2012. #Emotional tweets. In Proceedings of the 1st Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval’12). Association for Computational Linguistics, 246--255. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Saif M. Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. 2013. NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval'13). Association for Computational Linguistics, 321--327.Google ScholarGoogle Scholar
  72. Preslav Nakov, Zornitsa Kozareva, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, and Theresa Wilson. 2013. Semeval-2013 task 2: Sentiment analysis in twitter. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics, 312--320.Google ScholarGoogle Scholar
  73. Sascha Narr, Hulfenhaus Michael, and Sahin Albayrak. 2012. Language-independent twitter sentiment analysis. In Proceedings of the Knowledge Discovery and Machine Learning at LWA 2012 (KDML’12).Google ScholarGoogle Scholar
  74. Nasir Naveed, Thomas Gottron, Jérôme Kunegis, and Arifah Che Alhadi. 2011. Searching microblogs: Coping with sparsity and document quality. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM’11). ACM, New York, NY, 183--188. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Finn Å Nielsen. 2011. A new ANEW: Evaluation of a word list for sentiment analysis of microblogs. In Proceedings of the ESWC2011 Workshop on ‘Making Sense of Microposts’: Big Things Come in Small Packages, Volume 718 (ESWC’11). CEUR Workshop Proceedings, 93--98.Google ScholarGoogle Scholar
  76. Brendan O’Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM’10). AAAI Press.Google ScholarGoogle Scholar
  77. Reynier Ortega, Adrian Fonseca, and Andres Montoyo. 2013. SSA-UO: Unsupervised twitter sentiment analysis. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics, 501--507.Google ScholarGoogle Scholar
  78. Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, Nathan Schneider, and Noah A. Smith. 2013. Improved part-of-speech tagging for online conversational text with word clusters. In Proceedings of Conference of the North American Chapter of the Association of Computational Linguistics on Human Language Technologies (NAACL-HLT 2013). The Association for Computational Linguistics, 380--390.Google ScholarGoogle Scholar
  79. Alexander Pak and Patrick Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the 7th on International Language Resources and Evaluation Conference (LREC’10). European Language Resources Association (ELRA), 1320--1326.Google ScholarGoogle Scholar
  80. Georgios Paltoglou and Kevan Buckley. 2013. Subjectivity annotation of the microblog 2011 realtime adhoc relevance judgments. In Proceedings of the 35th European Conference on Advances in Information Retrieval (ECIR’13). Springer-Verlag, Berlin, 344--355. DOI:http://dx.doi.org/10.1007/978-3-642-36973-5_29 Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Georgios Paltoglou and Anastasia Giachanou. 2014. Opinion retrieval: Searching for opinions in social media. In Professional Search in the Modern World: COST Action IC1002 on Multilingual and Multifaceted Interactive Information Access. Springer International Publishing, CBerlin, 193--214. DOI:http://dx.doi.org/10.1007/978-3-319-12511-4_10Google ScholarGoogle Scholar
  82. Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1--2 (2008), 1--135. DOI:http://dx.doi.org/10.1561/15000000011 Google ScholarGoogle ScholarDigital LibraryDigital Library
  83. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10 (EMNLP’02). Association for Computational Linguistics, Stroudsburg, PA, 79--86. DOI:http://dx.doi.org/10.3115/1118693.1118704 Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Saša Petrović, Miles Osborne, and Victor Lavrenko. 2010. The Edinburgh twitter corpus. In Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media (WSA’10). Association for Computational Linguistics, Stroudsburg, PA, 25--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  85. Robert Plutchik. 1980. Emotion: Theory, research, and experience: Vol. 1. theories of emotion. In Approaches to Emotion, R. Plutchik and H. Kellerman (Eds.). Academic Press, New York, NY, 3--33.Google ScholarGoogle Scholar
  86. Jonathon Read. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL Student Research Workshop (ACLstudent’05). Association for Computational Linguistics, Stroudsburg, PA, 43--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  87. Hilke Reckman, Cheyanne Baird, Jean Crawford, Richard Crowell, Linnea Micciulla, Saratendu Sethi, and Fruzsina Veress. 2013. Rule-based detection of sentiment phrases using SAS sentiment analysis. In 2nd Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: 7th International Workshop on Semantic Evaluation, Vol. 2. Association for Computational Linguistics, 513--519.Google ScholarGoogle Scholar
  88. Robert Remus. 2013. ASVUniOfLeipzig: Sentiment analysis in twitter using data-driven machine learning techniques. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13). Association for Computational Linguistics, 450--454.Google ScholarGoogle Scholar
  89. Antonio Reyes, Paolo Rosso, and Tony Veale. 2013. A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47, 1 (March 2013), 239--268. DOI:http://dx.doi.org/10.1007/s10579-012-9196-x Google ScholarGoogle ScholarDigital LibraryDigital Library
  90. Kirk Roberts, Michael A. Roach, Joseph Johnson, Josh Guthrie, and Sanda M. Harabagiu. 2012. EmpaTweet: Annotating and detecting emotions on twitter. In Proceedings of the 8th International Language Resources and Evaluation Conference (LREC’12). European Language Resources Association (ELRA), 3806--3813.Google ScholarGoogle Scholar
  91. Carlos Rodríguez-Penagos, Jordi Atserias, Joan Codina-Filbà, David García-narbona, Jens Grivolla, Patrik Lambert, and Roser Saurí. 2013. FBM: Combining lexicon-based ML and heuristics for social media polarities. In Proceedings of the 7th International Workshop on Semantic Evaluation - 2nd Joint Conference on Lexical and Computational Semantics (SemEval’13), Vol. 2. Association for Computational Linguistics, 483--489.Google ScholarGoogle Scholar
  92. Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif M. Mohammad, Alan Ritter, and Veselin Stoyanov. 2015. Semeval-2015 task 10: Sentiment analysis in twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). Association for Computational Linguistics, 451--463.Google ScholarGoogle ScholarCross RefCross Ref
  93. Sara Rosenthal, Preslav Nakov, Alan Ritter, and Veselin Stoyanov. 2014. SemEval-2014 task 9: Sentiment analysis in twitter. In Proceedings of the 8th International Workshop on Semantic Evaluation. Association for Computational Linguistics and Dublin City University.Google ScholarGoogle ScholarCross RefCross Ref
  94. Hassan Saif, Miriam Fernández, and Harith Alani. 2014. Automatic stopword generation using contextual semantics for sentiment analysis of twitter. In Proceedings of the ISWC 2014 Posters & Demonstrations Track at the 13th International Semantic Web Conference (ISWC’’14). CEUR-WS.org, 281--284. Google ScholarGoogle ScholarDigital LibraryDigital Library
  95. Hassan Saif, Miriam Fernández, Yulan He, and Harith Alani. 2013. Evaluation datasets for twitter sentiment analysis a survey and a new dataset, the STS-gold. In Proceedings of the 1st International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM’13). CEUR-WS.org.Google ScholarGoogle Scholar
  96. Hassan Saif, Miriam Fernández, Yulan He, and Harith Alani. 2014. On stopwords, filtering and data sparsity for sentiment analysis of twitter. In Proceedings of the 9th International Language Resources and Evaluation Conference (LREC’14). European Language Resources Association (ELRA), 810--817.Google ScholarGoogle Scholar
  97. Hassan Saif, Yulan He, and Harith Alani. 2012a. Alleviating data sparsity for twitter sentiment analysis. In Workshop on Making Sense of Microposts (#MSM2012): Big Things Come in Small Packages at the 21st International Conference on the World Wide Web (WWW’12). CEUR-WS.org, 2--9.Google ScholarGoogle Scholar
  98. Hassan Saif, Yulan He, and Harith Alani. 2012b. Semantic sentiment analysis of twitter. In Proceedings of the 11th International Conference on The Semantic Web - Volume Part I (ISWC’12). Springer-Verlag, Berlin, 508--524. DOI:http://dx.doi.org/10.1007/978-3-642-35176-1_32 Google ScholarGoogle ScholarDigital LibraryDigital Library
  99. Hassan Saif, Yulan He, Miriam Fernández, and Harith Alani. 2016. Contextual semantics for sentiment analysis of twitter. Inform. Process. Manag. 52, 1 (2016), 5--19. DOI:http://dx.doi.org/10.1016/j.ipm.2015.01.005 Google ScholarGoogle ScholarDigital LibraryDigital Library
  100. David A. Shamma, Lyndon Kennedy, and Elizabeth F. Churchill. 2009. Tweet the debates: Understanding community annotation of uncollected sources. In Proceedings of the First SIGMM Workshop on Social Media (WSM’09). ACM, New York, NY, 3--10. DOI:http://dx.doi.org/10.1145/1631144.1631148 Google ScholarGoogle ScholarDigital LibraryDigital Library
  101. Valentina Sintsova, Claudiu Musat, and Pearl Pu. 2014. Semi-supervised method for multi-category emotion recognition in tweets. In 2014 IEEE International Conference on Data Mining Workshop (ICDM’14). IEEE, 393--402. DOI:http://dx.doi.org/10.1109/ICDMW.2014.146Google ScholarGoogle ScholarCross RefCross Ref
  102. Michael Speriosu, Nikita Sudan, Sid Upadhyay, and Jason Baldridge. 2011. Twitter polarity classification with label propagation over lexical links and the follower graph. In Proceedings of the First Workshop on Unsupervised Learning in NLP (EMNLP’11). Association for Computational Linguistics, Stroudsburg, PA, 53--63. Google ScholarGoogle ScholarDigital LibraryDigital Library
  103. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods for sentiment analysis. Comput. Ling. 37, 2 (2011), 267--307. Google ScholarGoogle ScholarDigital LibraryDigital Library
  104. Yen-Jen Tai and Hung-Yu Kao. 2013. Automatic domain-specific sentiment lexicon generation with label propagation. In Proceedings of the International Conference on Information Integration and Web-Based Applications & Services (IIWAS’’13). ACM, New York, NY, 53--62. DOI:http://dx.doi.org/10.1145/2539150.2539190 Google ScholarGoogle ScholarDigital LibraryDigital Library
  105. Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. 2011. User-level sentiment analysis incorporating social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11). ACM, New York, NY, 1397--1405. DOI:http://dx.doi.org/10.1145/2020408.2020614 Google ScholarGoogle ScholarDigital LibraryDigital Library
  106. Duyu Tang, Bing Qin, and Ting Liu. 2015a. Learning semantic representations of users and products for document level sentiment classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (ACL’15). The Association for Computer Linguistics, 1014--1023.Google ScholarGoogle ScholarCross RefCross Ref
  107. Duyu Tang, Bing Qin, Ting Liu, and Yuekui Yang. 2015b. User modeling with neural network for review rating prediction. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI’15). AAAI Press, 1340--1346. Google ScholarGoogle ScholarDigital LibraryDigital Library
  108. Duyu Tang, Furu Wei, Bing Qin, Ting Liu, and Ming Zhou. 2014a. Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th International Workshop on Semantic Evaluation. Association for Computational Linguistics and Dublin City University, 208--212.Google ScholarGoogle ScholarCross RefCross Ref
  109. Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, and Bing Qin. 2014b. Learning sentiment-specific word embedding. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL’14). The Association for Computer Linguistics, 1555--1565.Google ScholarGoogle Scholar
  110. Mike Thelwall, Kevan Buckley, and Georgios Paltoglou. 2012. Sentiment strength detection for the social web. J. Am. Soc. Inform. Sci. Technol. 63, 1 (2012), 163--173. Google ScholarGoogle ScholarDigital LibraryDigital Library
  111. Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, and Arvid Kappas. 2010. Sentiment strength detection in short informal text. J. Am. Soc. Inform. Sci. Technol. 61, 12 (2010), 2544--2558. Google ScholarGoogle ScholarDigital LibraryDigital Library
  112. Mikalai Tsytsarau and Themis Palpanas. 2012. Survey on mining subjective data on the web. Data Min. Knowl. Discov. 24, 3 (2012), 478--514. Google ScholarGoogle ScholarDigital LibraryDigital Library
  113. Peter D. Turney. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL’02). Association for Computational Linguistics, Stroudsburg, PA, 417--424. Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. Duy-Tin Vo and Yue Zhang. 2015. Target-dependent twitter sentiment classification with rich automatic features. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015). AAAI Press, 1347--1353. Google ScholarGoogle ScholarDigital LibraryDigital Library
  115. Xiaolong Wang, Furu Wei, Xiaohua Liu, Ming Zhou, and Ming Zhang. 2011. Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM’11). ACM, New York, NY, 1031--1040. DOI:http://dx.doi.org/10.1145/2063576.2063726 Google ScholarGoogle ScholarDigital LibraryDigital Library
  116. Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2006. Annotating expressions of opinions and emotions in language. Lang. Res. Eval. 39, 2--3 (2006), 165--210. DOI:http://dx.doi.org/10.1007/s10579-005-7880-9Google ScholarGoogle Scholar
  117. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05). Association for Computational Linguistics, Stroudsburg, PA, 347--354. DOI:http://dx.doi.org/10.3115/1220575.1220619 Google ScholarGoogle ScholarDigital LibraryDigital Library
  118. Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu, and Bing Liu. 2011. Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis. Technical Report.Google ScholarGoogle Scholar
  119. Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan, and Xiaoming Li. 2011. Comparing twitter and traditional media using topic models. In Proceedings of the 33rd European Conference on Advances in Information Retrieval (ECIR’11). Springer-Verlag, Berlin, 338--349. DOI:http://dx.doi.org/10.1007/978-3-642-20161-5_34Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Like It or Not: A Survey of Twitter Sentiment Analysis Methods

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Computing Surveys
      ACM Computing Surveys  Volume 49, Issue 2
      June 2017
      747 pages
      ISSN:0360-0300
      EISSN:1557-7341
      DOI:10.1145/2966278
      • Editor:
      • Sartaj Sahni
      Issue’s Table of Contents

      Copyright © 2016 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 30 June 2016
      • Revised: 1 April 2016
      • Accepted: 1 April 2016
      • Received: 1 July 2015
      Published in csur Volume 49, Issue 2

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • survey
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader