Sentiment Analysis at Document Level

Sentiment analysis becomes a very active research area in the text mining field. It aims to extract people’s opinions, sentiments, and subjectivity from the texts. Sentiment analysis can be performed at three levels: at document level, at sentence level and at aspect level. An important part of research effort focuses on document level sentiment classification, including works on opinion classification of reviews. This survey paper tackles a comprehensive overview of the last update of sentiment analysis at document level. The main target of this survey is to give nearly full image of sentiment analysis techniques at this level. In addition, some future research issues are also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Similar content being viewed by others

Sentiment Analysis and Opinion Mining

Chapter © 2017

Sentiment Analysis Techniques for Social Media Data: A Review

Chapter © 2020

Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications

Chapter © 2022

References

  1. Anitha, N., Anitha, B., Pradeepa, S.: Sentiment classification approaches – a review. Int. J. Innovations Eng. Technol. (IJIET) 3(1) (2013) Google Scholar
  2. Baloglu, A., Aktas, M.S.: An automated framework for mining reviews from blogosphere. Int. J. Adv. Internet Technol. 3(3&4), 234–244 (2010) Google Scholar
  3. Bhatia, P., Ji, Y., Eisenstein, J.: Better document-level sentiment analysis from RST Discourse Parsing. In: Empirical Methods in Natural Language Processing, pp. 2212–2218. EMNLP, Lisbon (2015) Google Scholar
  4. Chen, Y.F., Miao, D.Q., Li, W., Zhang, Z.F.: Semantic orientation computing based on concepts. J. CAAI Trans. Intell. Syst. 6(6), 489–494 (2011) Google Scholar
  5. Duwairi, R.M.: Sentiment analysis for dialectical Arabic. In: 6th ICICS International Conference on Information and Communication Systems, pp. 166–170 (2015) Google Scholar
  6. Govindarajan, M.: Sentiment analysis of movie reviews using hybrid method of Naive Bayes and Genetic Algorithm. Int. J. Adv. Comput. Res. 3(4), 139–146 (2013) Google Scholar
  7. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, New York (2012) BookGoogle Scholar
  8. Mishne, G., Multiple Ranking Strategies for Opinion Retrieval in Blogs. In: Online Proceedings of TREC (2006) Google Scholar
  9. Nilesh, M.S., Deshpande, S., Thakre, V.: Survey of techniques for opinion mining. (IJCA) Int. J. Comput. Appl. (0975–8887) 57(13) (2012) Google Scholar
  10. Nguyen, D.Q., Nguyen, D.Q., Vu, T., Pham, S.B.: Sentiment classification on polarity reviews: an empirical study using rating-based features. In: 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 128–135, Maryland (2014) Google Scholar
  11. Oard, D.W., Elsayed, T., Wang, J., Wu, Y., Zhang P., Abels, E.G., Lin, J.J., Soergel, D.: TREC 2006 at Maryland: Blog, Enterprise, Legal and QA Tracks. TREC (2006) Google Scholar
  12. Ohana, B., Tierney, B.: Sentiment classification of reviews using SentiWordNet. In: 9th IT&T Conference, pp. 22–23 (2009) Google Scholar
  13. Pak, A., Paroubek, P.: Classification en polarité de sentiments avec une représentation textuelle à base de sous-graphes d’arbres de dépendances. TALN (2011) Google Scholar
  14. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Empirical Methods in Natural Language Processing, pp. 79–86. EMNLP (2002) Google Scholar
  15. Pang, B., Lee, L.: A Sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: 42th Annual Meeting of the Associatoin for Computational Linguistics ACL, pp. 271–278 (2004) Google Scholar
  16. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2, 1–135 (2008) ArticleGoogle Scholar
  17. Rafrafi, A., Guigue, V., Gallinari, P.: Réseau de neurones profond et SVM pour la classification des sentiments. In: COnférence en Recherche d’Information et Applications CORIA, pp. 121–133 (2011) Google Scholar
  18. Rothfels, J., Tibshirani, J.: Unsupervised sentiment classification of English movie reviews using automatic selection of positive and negative sentiment items. CS224N-Final Project (2010) Google Scholar
  19. Rushdi‐Saleh, M., Martín‐Valdivia, M.T., Ureña‐López, L.A., Perea‐Ortega, J.M.: OCA: opinion corpus for Arabic. J. ASIS&T 62, 2045–2054 (2011) Google Scholar
  20. Sharma, R., Nigam, S., Jain, R.: Opinion mining of movie reviews at document level. IJIT, 3 (2014) Google Scholar
  21. Sindhu, C., ChandraKala, S.: A survey on opinion mining and sentiment polarity classification. IJETAE, 3 (2013) Google Scholar
  22. Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.N., Potts, C.: Recursive deep models for semantic compositionality over a sentiment tree bank. In: Empirical Methods for Natural Language Processing. EMNLP (2013) Google Scholar
  23. Tripathi, G., Naganna, S.: Feature selection and classification approach for sentiment analysis. MLAIJ, p. 2201 (2015) Google Scholar
  24. Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: 40th annual meeting of the Association for Computational Linguistics, pp. 417–424. ACL, Philadelphia (2002) Google Scholar
  25. Vinodhini, G., Chandrasekaran, R.M.: Sentiment analysis and opinion mining: a survey. IJARCSSE 2277, 282–292 (2012) Google Scholar
  26. Vinodhini, G., Chandrasekaran, R.M.: Effect of feature reduction in sentiment analysis of online reviews. IJARCET (2013). ISSN 2278–1323 Google Scholar
  27. Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: 50th Annual Meeting of the Association for Computational Linguistics, pp. 90–94. ACL (2012) Google Scholar
  28. Zhang, Q., Wang, B., Wu, L., Huang, X.: FDU at TREC 2007: opinion retrieval of blog track. In: Voorhees, E.M., Buckland, L.P. (eds), TREC 2007, vol. Special Publication, 500–274 (2007) Google Scholar
  29. Zhang, Z., Miao, D., Wei, Z., Wang, L.: Document-level sentiment classification based on behavior-knowledge space method. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS (LNAI), vol. 7713, pp. 330–339. Springer, Berlin, Heidelberg (2012). doi:10.1007/978-3-642-35527-1_28ChapterGoogle Scholar
  30. Zhang, L., Hua, K., Wang, H., Qian, G., Zhang, L.: Sentiment analysis on reviews of mobile users. In: 11th International Conference on Mobile Systems and Pervasive Computing, Procedia Computer Science, vol. 34, pp. 458–465 (2014) Google Scholar
  31. http://www.cs.cornell.edu/people/pabo/movie-review-data/
  32. https://www.projet-doxa.fr/index.php

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Sciences, University of Oran 1 Ahmed Ben Bella, PB 1524, El M’Naouer, 31000, Oran, Algeria Salima Behdenna, Fatiha Barigou & Ghalem Belalem
  1. Salima Behdenna