Abstract
Since the past few years, we have been talking about opinion extraction, also known as opinion mining. It is a computational study of opinions and sentiments expressed in a text format. A lot of web resources contain user’s opinions, e.g. social networks, micro blogging platforms, and Blogs. People frequently make their opinions available in these sources. It is important for a company to study the opinions of these customers in order to improve its services or the quality of these products. In this paper, we are interested in studying the opinions of users about a product and extracting their polarity (positive, negative or neutral), for example studying the opinion of users about the Nokia or Huawei brand. We collected data from Twitter because it is a rich data sources for opinion mining. We propose a new ontological approach able to classify the opinion of user’s expressed in their tweets using Natural Language Processing (NLP) tools. This classification used a supervised Machine Learning Classifier: Support Vector Machine (SVM).
This is a preview of subscription content, log in via an institution.
Notes
- 1.
Refers to a problem setting in which one is given a small set of labeled data and a large set of unlabeled data, and the task is to induce a classifier.
- 2.
It is a piece of software that reads text in some languages and assigns part of speech to each word (and other token) such as noun, verb, adjective, etc.
References
Agarwal, B., et al.: One-class support vector machine for sentiment analysis of movie review documents. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 9(12), 2039–2042 (2015)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)
Dalal, M.K., Zaveri, M.A.: Opinion mining from online user reviews using fuzzy linguistic hedges. Appl. Comput. Intell. Soft Comput. 2014, 2 (2014)
Duwairi, R.M., Qarqaz, I.: Arabic sentiment analysis using supervised classification. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 579–583. IEEE (2014)
Ghosh, M., Kar, A.: Unsupervised linguistic approach for sentiment classification from online reviews using SentiWordNet 3.0. Int. J. Eng. Res. Technol. 2 (2013). ESRSA Publications
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics (1997)
Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of Twitter posts. Expert Syst. Appl. 40(10), 4065–4074 (2013)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Mars, A., Gouider, M.S., Saïd, L.B.: A new big data framework for customer opinions polarity extraction. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015–2016. CCIS, vol. 613, pp. 518–531. Springer, Cham (2016). doi:10.1007/978-3-319-34099-9_40
Palanisamy, P., Yadav, V., Elchuri, H.: Serendio: simple and practical lexicon based approach to sentiment analysis. In: Proceedings of Second Joint Conference on Lexical and Computational Semantics, pp. 543–548. Citeseer (2013)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Park, S.-M., Baik, D.-K.: Personal ontology-based sentiment analysis system for mobile devices. In: Proceedings of the International Conference on Semantic Web and Web Services (SWWS), the Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p. 42 (2013)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112. Association for Computational Linguistics (2003)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)
Vaitheeswaran, G., Arockiam, L.: Hybrid based approach to enhance the accuracy of sentiment analysis on tweets. Int. J. Sci. Eng. Comput. Technol. 6(6), 185 (2016)
Zainuddin, N., Selamat, A., Ibrahim, R.: Improving Twitter aspect-based sentiment analysis using hybrid approach. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS, vol. 9621, pp. 151–160. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49381-6_15
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
Mars, A., Hamem, S., Gouider, M.S. (2017). New Ontological Approach for Opinion Polarity Extraction from Twitter. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-67077-5_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67076-8
Online ISBN: 978-3-319-67077-5
eBook Packages: Computer ScienceComputer Science (R0)