Abstract
Many companies/corporations are interested in the opinion that users share about them in different social media. Sentiment analysis provides us with a powerful tool to discern the polarity of the opinion about a particular object or service, which makes it an important research field nowadays. In this paper we present a method to perform the sentiment analysis of a sentence through its syntactic analysis, by generating a code in Prolog from the parse tree of the sentence, which is automatically generated using natural language processing tools. This is a preliminary work, which provides encouraging results.
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
Kumar Gupta, D., Srikanth Reddy, K., Shweta, Ekbal, A.: PSO-ASent: feature selection using particle swarm optimization for aspect based sentiment analysis. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds.) Natural Language Processing and Information Systems 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, Passau, Germany, Proceedings, vol. 9103, pp. 220–233. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-319-19581-0_20
Dong, L., et al.: A statistical parsing framework for sentiment classification. Comput. Linguist. 41(2), 293–336 (2015)
Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–9 (2013)
Liu, B.: Sentiment analysis: a multifaceted problem. IEEE Intell. Syst. 25(3), 76–80 (2010)
Negi, S., Buitelaar, P.: INSIGHT galway: syntactic and lexical features for aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 346–350 (2014)
Perkins, J.: Python 3 Text Processing with NLTK 3 Cookbook. Packt Publishing, Birmingham (2014)
Pontiki, M. et al.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014)
Pontiki, M. et al.: SemEval-2016 task 5: aspect based sentiment analysis. In ProWorkshop on Semantic Evaluation (SemEval-2016), pp. 19–30. Association for Computational Linguistics (2016)
Serrano-Guerrero, J., et al.: Sentiment analysis: a review and comparative analysis of web services. Inf. Sci. 311, 18–38 (2015)
Thet, T.T., Na, J.C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 823–848 (2010)
Acknowledgements
Supported by the project TIN2015-64776-C3-3-R of the Science and Innovation Ministry of Spain, co-funded by the European Regional Development Fund (ERDF).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Moreno-Garcia, J., Rosado, J. (2018). Using Syntactic Analysis to Enhance Aspect Based Sentiment Analysis. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-91476-3_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91475-6
Online ISBN: 978-3-319-91476-3
eBook Packages: Computer ScienceComputer Science (R0)