Affiliation:
1. Dipartimento di Matematica e Informatica, University of Catania, 95125 Catania, Italy
Abstract
Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.
Subject
Computer Networks and Communications,Human-Computer Interaction
Reference52 articles.
1. Techniques and applications for sentiment analysis;Feldman;Commun. ACM,2013
2. Opinion mining and sentiment analysis;Pang;Found. Trends Inf. Retr.,2008
3. Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums;Abbasi;ACM Trans. Inf. Syst.,2008
4. Beineke, P., Hastie, T., Manning, C., and Vaithyanathan, S. (2004, January 22–24). An exploration of sentiment summarization. Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, Palo Alto, CA, USA.
5. Wiebe, J.M., Bruce, R.F., and O’Hara, T.P. (1999, January 20–26). Development and use of a gold-standard data set for subjectivity classifications. Proceedings of the Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, MD, USA.