Abstract
The possibility for people to leave comments in blogs and forums on the Internet allows to study their attitude (in terms of valence or even of specific feelings) on various topics. For some digital libraries this may be a precious opportunity to understand how their content is perceived by their users and, as a consequence, to suitably direct their future strategic choices. So, libraries might want to enrich their sites with the possibility, for their users, to provide feedback on the items they have consulted. Of course, manually analyzing all the available comments would be infeasible. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at automatically analyzing and classifying text documents based on the underlying opinions expressed by their authors.
Significant problems in building an automatic system for this purpose are given by the complexity of natural language, by the need of dealing with several languages, and by the choice of relevant features and of good approaches to building the models. Following the interesting results obtained for Italian by a system based on a Text Categorization approach, this paper proposes further experiments to check whether reliable predictions can be obtained, both for opinions and for feelings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basile, V., Nissim, M.: Sentiment analysis on Italian tweets. In: Proceeding of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 100–107 (2013)
Basile, V., Bolioli, A., Nissim, M., Patti, V., Rosso, P.: Overview of the evalita 2014 sentiment polarity classification task. In: Proceedings of the 4th Evaluation Campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2014), pp. 50–57. Pisa University Press (2014)
Carbonell, J.: Subjective Understanding: Computer Models of Belief Systems (1979)
Cherry, C., Mohammad, S.M., de Bruijn, B.: Binary classifiers and latent sequence models for emotion detection in suicide notes. 5, 147–154 (2012)
Danisman, T., Alpkocak, A.: Feeler: emotion classification of text using vector space model. In: Proceedings of the AISB 2008 Convention, Communication, Interaction and Social Intelligence (2008)
Das, S., Chen, M.: Yahoo! for amazon: extracting market sentiment from stock message boards. In: Proceedings of the Asia Pacific Finance Association Annual Conference (APFA) (2001)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of WWW, pp. 519–528 (2003)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic indexing. J. Am. Soc. Inform. Sci. Technol. 41, 391–407 (1990)
Ekman, P.: Sixteen enjoyable emotions. Emotion Researcher 18, 6–7 (2003)
Ferilli, S., De Carolis, B., Esposito, F., Redavid, D.: Sentiment analysis as a text categorization task: a study on feature and algorithm selection for Italian language. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), 36678 2015, pp. 1–10 (2015)
Lewis, D.D.: An evaluation of phrasal and clustered representations on a text categorization task. In: Proceedings of SIGIR-92, 15th ACM International Conference on Research and Development in Information Retrieval, pp. 37–50 (1992)
Mishne, G., Glance, N.: Predicting movie sales from blogger sentiment. In: Proceedings ofAAAI-CAAW-2006, the Spring Symposia on Computational Approaches to Analyzing Weblogs (2006)
Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the web. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002) (2002)
Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the Conference on Knowledge Capture (K-CAP) (2003)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Porter, M.F.: Snowball A language for stemming algorithms, October 2001
Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of International Conference on New Methods in Language Processing, pp. 44–49 (1994)
Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Proceedings of the SIGIR Workshop on Operational Text Classification (OTC) (2001)
Tulyakov, S., Jaeger, S., Govindaraju, V., Doermann, D.: Review of classifier combination methods. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. SCI, vol. 90, pp. 361–386. Springer, Heidelberg (2008)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics (ACL), pp. 417–424 (2002)
Wiebe, J.M.: Learning subjective adjectives from corpora. In: Proceedings of National Conference on Artificial Intelligence (AAAI-2000) (2000)
Wiebe, J.M., Bruce, R.F., O’Hara, T.P.: Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the Association for Computational Linguistics (ACL-1999) (1999)
Wilks, Y., Bien, J.: Beliefs, points of view and multiple environments. In: Proceedings of the International NATO Symposium on Artificial and Human Intelligence, pp. 147–171. Elsevier North-Holland Inc. (1984)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of ICML-97, 14th International Conference on Machine Learning, pp. 412–420 (1997)
Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of the IEEE International Conference on Data Mining (ICDM) (2003)
Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2003) (2003)
Acknowledgment
This work was partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia@Service’.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ferilli, S., De Carolis, B., Redavid, D., Esposito, F. (2017). Towards Sentiment and Emotion Analysis of User Feedback for Digital Libraries. In: Agosti, M., Bertini, M., Ferilli, S., Marinai, S., Orio, N. (eds) Digital Libraries and Multimedia Archives. IRCDL 2016. Communications in Computer and Information Science, vol 701. Springer, Cham. https://doi.org/10.1007/978-3-319-56300-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-56300-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56299-5
Online ISBN: 978-3-319-56300-8
eBook Packages: Computer ScienceComputer Science (R0)