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Sentiment Analysis of Lithuanian Texts Using Deep Learning Methods

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Book cover Information and Software Technologies (ICIST 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 920))

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Abstract

We describe experiments in sentiment analysis of the Lithuanian texts using the deep learning methods: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Methods used with pre-trained Lithuanian neural word embeddings are tested with different pre-processing techniques: emoticons restoration, stop words removal, diacritics restoration/elimination. Despite the selected pre-processing technique, CNN was always outperformed by LSTM. Better results (reaching an accuracy of 0.612) were achieved with the undiacritized texts and undiacritized word embeddings. However, these results are still worse if compared to the ones obtained using Support Vector Machines or Naive Bayes Multinomial and with the frequencies of words as features.

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Notes

  1. 1.

    Since f-score values demonstrated the same trend as the accuracy, we do not present them.

References

  1. Agarwal, B., Mittal, N., Bansal, P., Garg, S.: Sentiment analysis using common-sense and context information. Comput. Intell. Neurosci. 2015, 1–9 (2015)

    Article  Google Scholar 

  2. Akhtar, M.S., Kumar, A., Ekbal, A., Bhattacharyya, P.: A hybrid deep learning architecture for sentiment analysis. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 482–493 (2016)

    Google Scholar 

  3. Asghar, M.Z., Khan, A., Ahmad, Sh., Qasim, M., Khan, I.A.: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLOS ONE 12(2), e0171649 (2017)

    Article  Google Scholar 

  4. Augustyniak, Ł., Szymański, P., Kajdanowicz, T., Tuligłowicz, W.: Comprehensive study on lexicon-based ensemble classification sentiment analysis. Entropy 18(1), 4 (2015)

    Article  Google Scholar 

  5. Baziotis, Ch., Pelekis, N., Doulkeridis, Ch.: DataStories at SemEval-2017 task 4: deep LSTM with attention for message-level and topic-based sentiment analysis. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval 2017 (2017)

    Google Scholar 

  6. Cliché, M.: BB_twtr at SemEval-2017 task 4: twitter sentiment analysis with CNNs and LSTMs. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval 2017 (2017)

    Google Scholar 

  7. Cortis, K., et al.: SemEval-2017 task 5: fine-grained sentiment analysis on financial microblogs and news. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval-2017 (2017)

    Google Scholar 

  8. Deeplearning4j Development Team: Deeplearning4j: Open-source distributed deep learning for the JVM, apache software foundation license 2.0 (2017)

    Google Scholar 

  9. Ferrara, E., Yang, Z.: Quantifying the effect of sentiment on information diffusion in social media. PeerJ Comput. Sci. 1, e26 (2015)

    Article  Google Scholar 

  10. Ghosal, D., Bhatnagar, Sh., Akhtar, Md.Sh., Ekbal, A., Bhattacharyya, P.: IITP at SemEval-2017 task 5: an ensemble of deep learning and feature based models for financial sentiment analysis. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval-2017 (2017)

    Google Scholar 

  11. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing, 1–6 (2009)

    Google Scholar 

  12. Guan, Z., Chen, L., Zhao, W., Zheng, Y., Tan, Sh., Cai, D.: Weakly-supervised deep learning for customer review sentiment classification. In: International Joint Conference on Artificial Intelligence, pp. 3719–3725 (2016)

    Google Scholar 

  13. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  14. Jiang, M., Lan, M., Wu, Y.: ECNU at SemEval-2017 task 5: an ensemble of regression algorithms with effective features for fine-grained sentiment analysis in financial domain. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval-2017 (2017)

    Google Scholar 

  15. Kanavos, A., Nodarakis, N., Sioutas, S., Tsakalidis, A., Tsolis, D., Tzimas, G.: Large scale implementations for twitter sentiment classification. Algorithms 10(1), 33 (2017)

    Article  MathSciNet  Google Scholar 

  16. Kapočiūtė-Dzikienė, J., Davidsonas, A., Vidugirienė, A.: Character-based machine learning vs. language modeling for diacritics restoration. Inf. Technol. Control 46(4), 508–520 (2017)

    Google Scholar 

  17. Kapočiūtė-Dzikienė, J., Krupavičius, A., Krilavičius, T.: A comparison of approaches for sentiment classification on lithuanian internet comments. In: Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing, pp. 2–11 (2013)

    Google Scholar 

  18. Katoh, K. Ninomiya, T.: Deep learning for large-scale sentiment analysis using distributed representations. In SEMAPRO 2015: The 9th International Conference on Advances in Semantic Processing, pp. 92–96 (2015)

    Google Scholar 

  19. Kim, Y.: Convolutional neural networks for sentence classification. In: Empirical Methods in Natural Language Processing, EMNLP, pp. 1746–1751 (2014)

    Google Scholar 

  20. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)

    Article  Google Scholar 

  21. Lu, Y., Sakamoto, K., Shibuki, H., Mori, T.: Are deep learning methods better for twitter sentiment analysis? In: The 7th International Annual Engineering Seminar, InAES (2017)

    Google Scholar 

  22. McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)

    Article  Google Scholar 

  23. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR, abs/1301.3781 (2013)

    Google Scholar 

  24. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: The 27th Annual Conference on Neural Information Processing Systems 2013, pp. 3111–3119 (2013)

    Google Scholar 

  25. Nogueira dos Santos, C., Maira, G.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING), pp. 69–78 (2014)

    Google Scholar 

  26. Pak, A., Paroubek, P.: Twitter for sentiment analysis: when language resources are not available. In: Proceedings of Database and Expert Systems Applications, DEXA 2011, pp. 111–115 (2011)

    Google Scholar 

  27. Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the Association for Computational Linguistics (ACL), pp. 115–124 (2005)

    Google Scholar 

  28. Pang, B., Lee, L., Vaithyanathan, Sh.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 79–86 (2002)

    Google Scholar 

  29. Paredes-Valverde, M.A., Colomo-Palacios, R., Salas-Zárate, M.d.P., Valencia-García, R.: Sentiment analysis in spanish for improvement of products and services: a deep learning approach. Sci. Program. 2017, 1–6 (2017)

    Article  Google Scholar 

  30. Rajput, Q., Haider, S., Ghani, S.: Lexicon-based sentiment analysis of teachers’ evaluation. Appl. Comput. Intell. Soft Comput. 2016, 1–12 (2016)

    Article  Google Scholar 

  31. Ranco, G., Bordino, I., Bormetti, G., Caldarelli, G., Lillo, F., Treccani, M.: Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics. PLOS ONE 11(1), e0146576 (2016)

    Article  Google Scholar 

  32. Ravichandran, M., Kulanthaivel, G., Chellatamilan, T.: Intelligent topical sentiment analysis for the classification of E-learners and their topics of interest. Sci. World J. 2015, 1–8 (2015)

    Article  Google Scholar 

  33. Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, ACL 2005, pp. 43–48 (2005)

    Google Scholar 

  34. Rosenthal, S., Farra, N., Nakov, P.: SemEval-2017 task 4: sentiment analysis in twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval 2017 (2017)

    Google Scholar 

  35. Salas-Zárate, M.d.P., et al.: Sentiment analysis on tweets about diabetes: an aspect-level approach. Comput. Math. Methods Med. 2017, 1–9 (2017)

    Article  Google Scholar 

  36. Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015, pp. 959–962 (2015)

    Google Scholar 

  37. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)

    Article  Google Scholar 

  38. Spencer, J., Uchyigit, G.: Sentimentor: sentiment analysis on twitter data. In: The 1st International Workshop on Sentiment Discovery from Affective Data, pp. 56–66 (2012)

    Google Scholar 

  39. Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I.: Finki at SemEval-2016 task 4: deep learning architecture for twitter sentiment analysis. In: SemEval@NAACL-HLT (2016)

    Google Scholar 

  40. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  41. 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, ACL 2002, pp. 417–424 (2002)

    Google Scholar 

  42. Wang, X., Zhang, H., Yuan, S., Wang, J., Zhou, Y.: Sentiment processing of social media information from both wireless and wired network. EURASIP J. Wirel. Commun. Netw. 2016(1), 146 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Vytautas Magnus University, K. Donelaičio 58, 44248, Kaunas, Lithuania

    Jurgita Kapočiūtė-Dzikienė

  2. Kaunas University of Technology, K. Donelaičio 73, 44029, Kaunas, Lithuania

    Robertas Damaševičius

  3. Silesian University of Technology, Kaszubska 23, 44-101, Gliwice, Poland

    Marcin Woźniak

Authors
  1. Jurgita Kapočiūtė-Dzikienė
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  2. Robertas Damaševičius
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  3. Marcin Woźniak
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Correspondence to Jurgita Kapočiūtė-Dzikienė .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Giedrė Vasiljevienė

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Kapočiūtė-Dzikienė, J., Damaševičius, R., Woźniak, M. (2018). Sentiment Analysis of Lithuanian Texts Using Deep Learning Methods. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_43

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  • DOI: https://doi.org/10.1007/978-3-319-99972-2_43

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