Abstract
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.
Similar content being viewed by others
References
Anitha, N., Anitha, B., Pradeepa, S.: Sentiment classification approaches – a review. Int. J. Innovations Eng. Technol. (IJIET) 3(1) (2013)
Baloglu, A., Aktas, M.S.: An automated framework for mining reviews from blogosphere. Int. J. Adv. Internet Technol. 3(3&4), 234–244 (2010)
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)
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)
Duwairi, R.M.: Sentiment analysis for dialectical Arabic. In: 6th ICICS International Conference on Information and Communication Systems, pp. 166–170 (2015)
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)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, New York (2012)
Mishne, G., Multiple Ranking Strategies for Opinion Retrieval in Blogs. In: Online Proceedings of TREC (2006)
Nilesh, M.S., Deshpande, S., Thakre, V.: Survey of techniques for opinion mining. (IJCA) Int. J. Comput. Appl. (0975–8887) 57(13) (2012)
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)
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)
Ohana, B., Tierney, B.: Sentiment classification of reviews using SentiWordNet. In: 9th IT&T Conference, pp. 22–23 (2009)
Pak, A., Paroubek, P.: Classification en polarité de sentiments avec une représentation textuelle à base de sous-graphes d’arbres de dépendances. TALN (2011)
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)
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)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2, 1–135 (2008)
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)
Rothfels, J., Tibshirani, J.: Unsupervised sentiment classification of English movie reviews using automatic selection of positive and negative sentiment items. CS224N-Final Project (2010)
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)
Sharma, R., Nigam, S., Jain, R.: Opinion mining of movie reviews at document level. IJIT, 3 (2014)
Sindhu, C., ChandraKala, S.: A survey on opinion mining and sentiment polarity classification. IJETAE, 3 (2013)
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)
Tripathi, G., Naganna, S.: Feature selection and classification approach for sentiment analysis. MLAIJ, p. 2201 (2015)
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)
Vinodhini, G., Chandrasekaran, R.M.: Sentiment analysis and opinion mining: a survey. IJARCSSE 2277, 282–292 (2012)
Vinodhini, G., Chandrasekaran, R.M.: Effect of feature reduction in sentiment analysis of online reviews. IJARCET (2013). ISSN 2278–1323
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)
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)
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_28
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Behdenna, S., Barigou, F., Belalem, G. (2016). Sentiment Analysis at Document Level. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_20
Download citation
DOI: https://doi.org/10.1007/978-981-10-3433-6_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3432-9
Online ISBN: 978-981-10-3433-6
eBook Packages: Computer ScienceComputer Science (R0)