Sentiment Analysis: A Perspective on its Past, Present and Future

Full Text (PDF, 588KB), PP.1-14

Views: 0 Downloads: 0


Akshi Kumar 1,* Teeja Mary Sebastian 1

1. Department of Computer Engineering, Delhi Technological University, Delhi, India

* Corresponding author.


Received: 4 Jan. 2012 / Revised: 3 Apr. 2012 / Accepted: 11 Jul. 2012 / Published: 8 Sep. 2012

Index Terms

Sentiment Analysis, Opinion, Web 2.0, Tasks, Levels, Applications, Issues


The proliferation of Web-enabled devices, including desktops, laptops, tablets, and mobile phones, enables people to communicate, participate and collaborate with each other in various Web communities, viz., forums, social networks, blogs. Simultaneously, the enormous amount of heterogeneous data that is generated by the users of these communities, offers an unprecedented opportunity to create and employ theories & technologies that search and retrieve relevant data from the huge quantity of information available and mine for opinions thereafter. Consequently, Sentiment Analysis which automatically extracts and analyses the subjectivities and sentiments (or polarities) in written text has emerged as an active area of research. This paper previews and reviews the substantial research on the subject of sentiment analysis, expounding its basic terminology, tasks and granularity levels. It further gives an overview of the state- of – art depicting some previous attempts to study sentiment analysis. Its practical and potential applications are also discussed, followed by the issues and challenges that will keep the field dynamic and lively for years to come.

Cite This Paper

Akshi Kumar, Teeja Mary Sebastian, "Sentiment Analysis: A Perspective on its Past, Present and Future", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.10, pp.1-14, 2012. DOI:10.5815/ijisa.2012.10.01


[1]Tim O’Reilly, Web 2.0 Compact Definition: Trying Again (O’Reilly Media, Sebastopol), Accessed 22 Mar 2007

[2]Tang H, Tan S, and Cheng X. A survey on sentiment detection of reviews. Expert Systems with Applications: An International Journal, September 2009, 36(7):10760–10773.

[3]Liu B. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second edition, 2010

[4]Pang, B and Lee L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieva,l 2008,(1-2),1–135

[5]Dave K., Lawrence S, and Pennock D.M. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of of the 12th international conference on World Wide Web(WWW), 2003, pp.:519–528

[6]Wiebe, J., Wilson, T., Bruce, R., Bell, M., and Martin, M. Learning subjective language. Computational Linguistics, 2004, 30(3):277–308

[7]Theresa Wilson, Janyce Wiebe, and Rebecca Hwa. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of AAAI, 2004,pages 761–769.

[8]Pang, B., Lee, L., and Vaithyanathan.S. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2002, (EMNLP):79–86.

[9]Pang B. and Lee L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, Proceedings of the Association for Computational Linguistics (ACL),2005:115–124

[10]Anderson, P. What is Web 2.0? Ideas, technologies and implications for education. Technical report, JISC,2007

[11]Mishne G. and Glance N. Predicting movie sales from blogger sentiment. In AAAI Symposium on Computational Approaches to Analyzing Weblogs (AAAI-CAAW),2006: 155–158.

[12]Liu, Y., Huang, J., An, A., and Yu, X. ARSA: A sentiment-aware model for predicting sales performance using blogs. In Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR),2007

[13]Melville, P., Gryc, W., and Lawrence, R.D. Sentiment analysis of blogs by combining lexical knowledge with text classification. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.2009: 1275-1284.

[14]Go, A., Bhayani, R., Huang, L. Twitter sentiment classification using distant supervision. Technical report, Stanford Digital Library Technologies Project.2009.

[15]Pak A. and Paroubek P. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, .2010:1320-1326.

[16]Barbosa, L. and Feng, J. Robust Sentiment Detection on Twitter from Biased and Noisy Data. COLING 2010: Poster Volume, 36-44.

[17]O'Connor B., Balasubramanyan R., Routledge B.R., Smith N. A. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. AAAI. 2010

[18]Hatzivassiloglou, V. and Wiebe, J. Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of the International Conference on Computational Linguistics (COLING), 2000.

[19]Riloff E. and Wiebe J., Learning extraction patterns for subjective expressions.Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2003.

[20]Choi, Y., Cardie, C., Riloff, E., and Patwardhan, S., Identifying sources of opinions with conditional random fields and extraction patterns. Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005.

[21]Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., and Jurafsky, D., Automatic extraction of opinion propositions and their holders. Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, 2004.

[22]Yi, J., Nasukawa, T., Niblack, W., &Bunescu, R.,Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. Proceedings of the 3rd IEEE international conference on data mining (ICDM 2003):427–434

[23]Hu, M. and Liu, B. Mining opinion features in customer reviews. In Proceedings of AAAI, 2004: 755–760.

[24]Popescu A-M. and Etzioni O., Extracting product features and opinions from reviews, Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP),2005

[25]Turney P. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the Association for Computational Linguistics (ACL), 2005: 417–424.

[26]Yu H. and Hatzivassiloglou V., Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences.In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.

[27]Liu, B., Hu, M., & Cheng, J. Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th international world wide web conference (WWW-2005). ACM Press: 10–14.

[28]Wilson T., Wiebe J., and Hoffmann P.,Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP)2005: 347–354

[29]Esuli, A., & Sebastiani, F. Determining the semantic orientation of terms through gloss classification.In Proceedings of CIKM-05, the ACM SIGIR conference on information and knowledge management, Bremen, DE, 2005.

[30]Aue, A. and Gamon, M., Customizing sentiment classifiers to new domains: A case study. Proceedings of Recent Advances in Natural Language Processing (RANLP),2005.

[31]Kim, S. and Hovy, E., Determining the sentiment of opinions.In Proceedings of the International Conference on Computational Linguistics (COLING) ,2004

[32]Kamps, J., Marx, M., Mokken, R.J., de Rijke, M., Using WordNet to measure semantic orientation of adjectives. In Language Resources and Evaluation (LREC),2004.

[33]Hatzivassiloglou, V. and McKeown, K., Predicting the semantic orientation of adjectives. In Proceedings of the Joint ACL/EACL Conference,2004: 174–181

[34]Kumar, A. & Sebastian, T.M., Machine learning assisted Sentiment Analysis. Proceedings of International Conference on Computer Science & Engineering (ICCSE’2012), 123-130, 2012.

[35]Cambria, Erik. Roelandse, Martijn. ed. Sentic Computing: Techniques, Tools and Applications. Berlin: Springer-Verlag., 2012.

[36]Kaji, N. and Kitsuregawa, M., Building lexicon for sentiment analysis from massive collection of html documents. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2007. 

[37]Das, S. and Chen, M., Yahoo! for Amazon: Extracting market sentiment from stock message boards. In Proceedings of the Asia Pacific Finance Association Annual Conference (APFA),2 001.

[38]Morinaga S., Yamanishi K., Tateishi K., and Fukushima T., Mining product reputations on the web.In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2002: 341–349, Industry track

[39]Turney, P. and Littman, M., Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS),2003, 21(4):315–346.

[40]Pang B. and Lee L., A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the Association for Computational Linguistics (ACL), 2004: 271–278.

[41]Gamon, M,. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the International Conference on Computational Linguistics (COLING), 2004.

[42]Nigam, K. and Hurst, M.,.Towards a robust metric of opinion.The AAAI Spring Symposium on Exploring Attitude and Affect in Text,2004

[43]Airoldi, E. M., Bai, X., and Padman, R., Markov blankets and meta-heuristic search: sentiment extraction from unstructured text. Lecture Notes in Computer Science,2006, 3932: 167–187.

[44]Cesarano, C., Dorr, B., Picariello, A., Reforgiato, D., Sagoff, A., Subrahmanian, V.: OASYS: An Opinion Analysis System. AAAI Press In: AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs (CAAW 2006): 21–26.

[45]K¨onig, A. C. & Brill, E., Reducing the human overhead in text categorization. In proceedings of the 12th ACM SIGKDD conference on knowledge discovery and data mining,2006, pp: 598–603.

[46]Kennedy, A. and Inkpen, D., Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2, Special Issue on Sentiment Analysis), 2006: 110–125.

[47]Thomas M, Pang B., and Lee L., Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2006: 327–335.

[48]Blitzer, J., McDonald, R., and Pereira, F., Domain adaptation with structural correspondence learning. In Empirical Methods in Natural Language Processing (EMNLP), 2006.

[49]Godbole, N., Srinivasaiah, M., and Skiena, S., Large-scale sentiment analysis for news and blogs. Proceedings of the International Conference in Weblogs and Social Media, 2007.

[50]Annett, M. and Kondrak, G. A comparison of sentiment analysis techniques: Polarizing movie blogs. Advances in Artificial Intelligence,2008, 5032:25–35.

[51]Zhou, L. and Chaovalit, P., Ontology-supported polarity mining.Journal of the American Society for Information Science and Technology,2008, 69:98–110.

[52]Hou, F. and Li, G.-H., Mining chinese comparative sentences by semantic role labeling. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, 2008.

[53]Ferguson, P., O’Hare, N., Davy, M., Bermingham, A., Tattersall, S., Sheridan, P., Gurrin, C., and Smeaton, A. F., Exploring the use of paragraph-level annotations for sentiment analysis in financial blogs.1st Workshop on Opinion Mining and Sentiment Analysis (WOMSA),2009.

[54]Tan, S., Cheng, Z., Wang, Y., and Xu, H., Adapting naive bayes to domain adaptation for sentiment analysis. Advances in Information Retrieval, 2009, 5478:337–349.

[55]Wilson, T., Wiebe, J., and Hoffmann, P., Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Computational Linguistics, 2009, 35(5):399–433

[56]Heerschop, B., Hogenboom, A., and Frasincar, F. Sentiment lexicon creation from lexical resources, Springer, In 14th International Conference on Business Information Systems (BIS 2011), volume 87 of Lecture Notes in Business Information Processing: 185–196. 

[57]Chen Y. Y. and Lee K. V., User-Centered Sentiment Analysis on Customer Product Review. World Applied Sciences Journal 12 (Special Issue on Computer Applications & Knowledge Management),2011: 32-38

[58]Mullen T. and Malouf R.,Taking sides: User classification for informal online political discourse. Internet Research, 2008, 18:177–190.

[59]Tumasjan A., Sprenger T.O., Sandner P.G., Welpe I. M., Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. AAAI ,2010

[60]Terveen L., Hill W., Amento B., McDonald D., and Creter J., PHOAKS: A system for sharing recommendations. In Communications of the Association for Computing Machinery (CACM),2007, 40(3):59–62

[61]Taboada M., Gillies M. A., and McFetridge P., Sentiment classification techniques for tracking literary reputation.In LREC Workshop: Towards Computational Models of Literary Analysis, 2006: 36–43.

[62]Piao S., Ananiadou S., Tsuruoka Y., Sasaki Y., and McNaught J., .Mining opinion polarity relations of citations. In International Workshop on Computational Semantics 84 (IWCS), 2007:366–371. 

[63]Kumar, A. & Ahmad, N. ComEx Miner: Expert Mining in Virtual Communities, International Journal of Advanced Computer Science and Applications (IJACSA), Vol.3, No. 6, June 2012, The Science and Information Organization Inc, USA.

[64]Seki Y., Eguchi K., Kando N., and Aono M., Multi-document summarization with subjectivity analysis at DUC 2005. In Proceedings of the Document Understanding Conference (DUC).

[65]Spertus E., Smokey: Automatic recognition of hostile message. In Proceedings of Innovative Applications of Artificial Intelligence (IAAI),1997: 1058–1065.

[66]Davidov, D., Tsur, O., and Rappoport, A., Semi-supervised recognition of sarcastic sentences in twitter and amazon. In Conference on Natural Language Learning (CoNLL), 2010.

[67]Denecke, K.., Using SentiWordNet for Multilingual Sentiment Analysis .Proc. of the IEEE 24th International Conference on Data Engineering Workshop (ICDEW 2008), IEEE Press:507-512.