Abstract
This paper describes an information extraction and content analysis system. The proposed system is based on a conditional random field algorithm and intended to extract aspect terms mentioned in the text. We use a set of morphological features for machine learning. The system is used for automatic extraction of explicit aspects and also to automatic extraction of all aspects (explicit, implicit and sentiment facts), and tested on two domains: restaurants and automobiles. We show that our system can produce quite a high level of precision which means that the system is capable of recognizing aspect terms rather accurately. The system demonstrates that even a small set of features for conditional random field algorithm can perform competitively and shows good results.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)
Rubtsova, Y.V.: Development and research domain independent sentiment classifier. SPIIRAS Proceedings 5(36), 59–77 (2014)
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. ACL (2002)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational linguistics 35(3), 399–433 (2009)
Zhang, L., Liu, B.: Aspect and entity extraction for opinion mining. In: Data Mining and Knowledge Discovery for Big Data, pp. 1–40 (2014)
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Marrese-Taylor, E., Velásquez, J.D., Bravo-Marquez, F.: A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Systems with Applications 41(17), 7764–7775 (2014)
Loukachevitch, N.V., Blinov, P.D., Kotelnikov, E.V., Rubtsova, Y.V., Ivanov, V.V., Tutubalina, E.: SentiRuEval: testing object-oriented sentiment analysis systems in russian. In: Proceedings of International Conference Dialog (2015)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Natural Language Processing and Text Mining, pp. 9–28 (2007)
Moghaddam, S., Ester, M.: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 665–674 (2011)
Jin, W., Ho, H.H., Srihari, R.K.: Opinionminer: a novel machine learning system for web opinion mining and extraction. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1195–1204 (2009)
Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045. ACL (2010)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of International Conference on Machine Learning (ICML-2001) (2001)
Sutton, C., McCallum, A.: An introduction to conditional random fields for relational learning. In: Introduction to Statistical Relational Learning. MIT Press (2006)
Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812 (2010)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine learning 42(1–2), 177–196 (2001)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56–65. ACL (2010)
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 339–348 (2012)
Pontiki, M., Papageorgiou, H., Galanis, D., Androutsopoulos, I., Pavlopoulos, J., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation, SemEval 2014, pp. 27–35 (2014)
McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002)
Sharoff, S., Kopotev, M., Erjavec, T., Feldman, A., Divjak, D.: Designing and evaluating russian tagsets. In: LREC (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rubtsova, Y., Koshelnikov, S. (2015). Aspect Extraction from Reviews Using Conditional Random Fields. In: Klinov, P., Mouromtsev, D. (eds) Knowledge Engineering and Semantic Web. KESW 2015. Communications in Computer and Information Science, vol 518. Springer, Cham. https://doi.org/10.1007/978-3-319-24543-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-24543-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24542-3
Online ISBN: 978-3-319-24543-0
eBook Packages: Computer ScienceComputer Science (R0)