Abstract
Sentiments of words can differ from one corpus to another. Inducing general sentiment lexicons for languages and using them cannot, in general, produce meaningful results for different domains. In this paper, we combine contextual and supervised information with the general semantic representations of words occurring in the dictionary. Contexts of words help us capture the domain-specific information and supervised scores of words are indicative of the polarities of those words. When we combine supervised features of words with the features extracted from their dictionary definitions, we observe an increase in the success rates. We try out the combinations of contextual, supervised, and dictionary-based approaches, and generate original vectors. We also combine the word2vec approach with hand-crafted features. We induce domain-specific sentimental vectors for two corpora, which are the movie domain and the Twitter datasets in Turkish. When we thereafter generate document vectors and employ the support vector machines method utilising those vectors, our approaches perform better than the baseline studies for Turkish with a significant margin. We evaluated our models on two English corpora as well and these also outperformed the word2vec approach. It shows that our approaches are cross-domain and portable to other languages.
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
Similar content being viewed by others
References
Akın, A.A., Akın, M.D.: Zemberek, an open source NLP framework for Turkic languages. Structure 10, 1–5 (2007)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari, N., et al. (eds.) LREC European Language Resources Association (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Boyd-Graber, J., Resnik, P.: Holistic sentiment analysis across languages: multilingual supervised latent Dirichlet allocation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 45–55. Association for Computational Linguistics (2010)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1023/A:1022627411411
Ertugrul, A.M., Önal, I., Acartürk, C.: Does the strength of sentiment matter? A regression based approach on Turkish social media. In: Natural Language Processing and Information Systems - 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, 21–23 June 2017, Proceedings, pp. 149–155 (2017). https://doi.org/10.1007/978-3-319-59569-6_16
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., Lehmann, S.: Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: EMNLP, pp. 1615–1625. Association for Computational Linguistics (2017)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. In: Processing, pp. 1–6 (2009)
Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 1510(726), 345–420 (2016)
Hamilton, W.L., Clark, K., Leskovec, J., Jurafsky, D.: Inducing domain-specific sentiment lexicons from unlabeled corpora. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 595–605. Association for Computational Linguistics (2016)
Li, F., Huang, M., Zhu, X.: Sentiment analysis with global topics and local dependency. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), pp. 1371–1376. Association for Computational Linguistics (2010)
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009). https://doi.org/10.1145/1645953.1646003
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, pp. 142–150. Association for Computational Linguistics (2011)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR 1301(3781), 1–12 (2013)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, May 2010
Sak, H., Güngör, T., Saraçlar, M.: Morphological disambiguation of Turkish text with perceptron algorithm. In: Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2007), pp. 107–118. CICLing Press (2007). https://doi.org/10.1007/978-3-540-70939-8_10
Sak, H., Güngör, T., Saraçlar, M.: Turkish language resources: morphological parser, morphological disambiguator and web corpus. In: Nordström, B., Ranta, A. (eds.) GoTAL 2008. LNCS (LNAI), vol. 5221, pp. 417–427. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85287-2_40
Tang, D., Wei, F., Qin, B., Liu, T., Zhou, M.: Coooolll: a deep learning system for Twitter sentiment classification. In: Proceedings of the 8th International Workshop on Semantic Evaluation, SemEval@COLING 2014, Dublin, Ireland, 23–24 August 2014, pp. 208–212 (2014)
Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010)
Acknowledgments
This work was supported by Boǧaziçi University Research Fund Grant Number 6980D, and by Turkish Ministry of Development under the TAM Project number DPT2007K12-0610. Cem Rifki Aydin has been supported by TüBİTAK BIDEB 2211E.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Aydın, C.R., Güngör, T., Erkan, A. (2023). Generating Word and Document Embeddings for Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-24340-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24339-4
Online ISBN: 978-3-031-24340-0
eBook Packages: Computer ScienceComputer Science (R0)