Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter Mouton August 17, 2019

Sentiment analysis for Polish

Aleksander Wawer

Abstract

This article is a comprehensive review of freely available tools and software for sentiment analysis of texts written in Polish. It covers solutions which deal with all levels of linguistic analysis: starting from word-level, through phrase-level and up to sentence-level sentiment analysis. Technically, the tools include dictionaries, rule-based systems as well as deep neural networks. The text also describes a solution for finding opinion targets. The article also contains remarks that compare the landscape of available tools in Polish with that for English language. It is useful from the standpoint of multiple disciplines, not only information technology and computer science, but applied linguistics and social sciences.


ORCID 0000-0002-7081-9797


References

Acedański, S. 2010. “A morphosyntactic brill tagger for inflectional languages”. In: Loftsson, H., E. Rögnvaldsson and S. Helgadóttir (eds.), Advances in natural language processing Berlin, Heidelberg: Springer. 3–14.10.1007/978-3-642-14770-8_3Search in Google Scholar

Bojanowski, P., E. Grave, A. Joulin and T. Mikolov. 2016. “Enriching word vectors with subword information”. arXiv preprint arXiv:1607.04606.10.1162/tacl_a_00051Search in Google Scholar

Buczyński, A. and A. Wawer. 2008a. “Shallow parsing in sentiment analysis of product reviews”. Proceedings of the Partial Parsing Workshop at LREC 14–18.Search in Google Scholar

Buczyński, A. and A. Wawer. 2008b. “Automated classification of product review sentiments in Polish”. Proceedings of the Intelligent Information Systems (IIS)Search in Google Scholar

Deng, L. and J. Wiebe. 2015. “MPQA 3.0: An entity/event-level sentiment corpus”. In: Mihalcea, R., J. Yue Chai and A. Sarkar (eds.), HLT-NAACL The Association for Computational Linguistics. 1323–1328.Search in Google Scholar

Haniewicz, K., W. Rutkowski, M. Adamczyk and M. Kaczmarek. 2013. “Towards the lexicon-based sentiment analysis of Polish texts: Polarity lexicon”. In: Bǎdicǎ, C., N. Thanh Nguyen and M. Brezovan (eds.), Computational collective intelligence. Technologies and applications Berlin: Springer. 286–295.10.1007/978-3-642-40495-5_29Search in Google Scholar

Hochreiter, S. and J. Schmidhuber. 1997. “Long short-term memory”. Neural Computation 9(8). 1735–1780. doi:10.1162/neco.1997.9.8.1735.10.1162/neco.1997.9.8.1735Search in Google Scholar

Kędzia, P., M. Piasecki and M. Orlińska. 2015. “Word sense disambiguation based on large scale Polish clarin heterogeneous lexical resources”. Cognitive Studies 15. 269–292.10.11649/cs.2015.019Search in Google Scholar

Kloumann, I. M., C. M. Danforth, K. D. Harris, C. A. Bliss and P. S. Dodds. 2012. “Positivity of the English language”. PloS One 7(1). e29484.10.1371/journal.pone.0029484Search in Google Scholar

Krueger, D., T. Maharaj, J. Kramár, M. Pezeshki, N. Ballas, N. R. Ke, A. Goyal, et al. 2016. “Zoneout: Regularizing rnns by randomly preserving hidden activations”. CoRR abs/1606.01305. <http://arxiv.org/abs/1606.01305>Search in Google Scholar

Lafferty, J. D., A. McCallum and F. C. N. Pereira. 2001. “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”. Proceedings of the Eighteenth International Conference on Machine Learning (ICML ’01). San Francisco, CA: Morgan Kaufmann Publishers Inc. 282–289.Search in Google Scholar

Levy, R. and G. Andrew. 2006. “Tregex and tsurgeon: Tools for querying and manipulating tree data structures”. Proceedings of the Fifth International Conference on Language Resources and Evaluation 2231–2234.Search in Google Scholar

Lew, M. and P. Pęzik. 2017. “A sequential child-combination tree-lstm network for sentiment analysis”. In Vetulani, Z. (ed.), Proceedings of the 8th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics Poznań.Search in Google Scholar

Liu, B. 2012. Sentiment Analysis and Opinion Mining Morgan and Claypool Publishers.10.2200/S00416ED1V01Y201204HLT016Search in Google Scholar

Mikolov, T., K. Chen, G. Corrado and J. Dean. 2013. “Efficient estimation of word representations in vector space”. CoRR abs/1301.3781. <http://arxiv.org/abs/1301.3781>Search in Google Scholar

Pang, B. and L. Lee. 2004. “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts”. Proceedings of ACL 271–278.10.3115/1218955.1218990Search in Google Scholar

Pang, B., L. Lee and S. Vaithyanathan. 2002. “Thumbs up? Sentiment classification using machine learning techniques”. CoRR cs.CL/0205070. <http://arxiv.org/abs/cs.CL/0205070>10.3115/1118693.1118704Search in Google Scholar

Przepiórkowski, A. and A. Buczyński. 2007. “Spade: Shallow Parsing and Disambiguation Engine”. Proceedings of the 3rd Language and Technology Conference (LTC’07). Poznań.Search in Google Scholar

Radziszewski, A. 2013. “A tiered crf tagger for Polish”. In: Bembenik, R., Ł. Skonieczny, H. Rybiński, M. Kryszkiewicz and M. Niezgódka (eds.), Intelligent tools for building a scientific information platform: Advanced architectures and solutions Berlin: Springer. 215–230.10.1007/978-3-642-35647-6_16Search in Google Scholar

Rill, S., J. Scheidt, J. Drescher, O. Schütz, D. Reinel and F. Wogenstein. 2012. “A generic approach to generate opinion lists of phrases for opinion mining applications”. Proceedings of the first international workshop on issues of sentiment discovery and opinion mining (WISDOM ’12). New York: ACM. 7:1–7:8.10.1145/2346676.2346683Search in Google Scholar

Ryciak, N. 2017. “Polish language sentiment analysis with tree-structured long short-term memory network”. In: Vetulani, Z. (ed.), Proceedings of the 8th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics Poznań.Search in Google Scholar

Socher, R., A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng and C. Potts. 2013. “Recursive deep models for semantic compositionality over a sentiment tree-bank”. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing Stroudsburg, PA: Association for Computational Linguistics. 1631–1642.Search in Google Scholar

Tai, K. S., R. Socher and C. D. Manning. 2015a. “Improved semantic representations from tree-structured long short-term memory networks”. <http://arxiv.org/abs/1503.00075>10.3115/v1/P15-1150Search in Google Scholar

Wawer, A. 2012. “Mining co-occurrence matrices for SO-PMI paradigm word candidates”. Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL’12 SRW). Avignon: Association for Computational Linguistics. 74–80.Search in Google Scholar

Wawer, A. 2015. “Towards domain-independent opinion target extraction”. IEEE International Conference on Data Mining Workshop, ICDMW 2015 Atlantic City, NJ, November 14–17, 2015. IEEE. 1326–1331. doi:10.1109/ICDMW.2015.255.10.1109/ICDMW.2015.255Search in Google Scholar

Wawer, A. 2016. “OPFI: A tool for opinion finding in Polish”. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) Paris,: European Language Resources Association (ELRA).Search in Google Scholar

Wawer, A. and M. Ogrodniczuk. 2017. “Results of the poleval 2017 competition: Sentiment analysis shared task”. 8th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and LinguisticsSearch in Google Scholar

Wawer, A. and D. Rogozińska. 2012. “How much supervision? Corpus-based lexeme sentiment estimation”. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW) 724–730. doi:10.1109/ICDMW.2012.11910.1109/ICDMW.2012.119Search in Google Scholar

Wilson, T., P. Hoffmann, S. Somasundaran, J. Kessler, J. Wiebe, Y. Choi, C. Cardie, E. Riloff and S. Patwardhan. 2005. “OpinionFinder: A system for subjectivity analysis”. Proceedings of HLT/EMNLP on Interactive Demonstrations (HLT-Demo ’05), Stroudsburg, PA: Association for Computational Linguistics. 34–35.10.3115/1225733.1225751Search in Google Scholar

Zaśko-Zielińska, M., M. Piasecki and S. Szpakowicz. 2015. “A large wordnet-based sentiment lexicon for Polish”. Proceedings of the International Conference Recent Advances in Natural Language Processing Hissar, Bulgaria: INCOMA Ltd. 721– 730. <http://www.aclweb.org/anthology/R15-1092>Search in Google Scholar

Żak, P. and T. Korbak. 2017. “Fine-tuning tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank”. In: Vetulani, Z. (ed.), Proceedings of the 8th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics. Poznań.Search in Google Scholar

Published Online: 2019-08-17
Published in Print: 2019-06-26

© 2019 Faculty of English, Adam Mickiewicz University, Poznań, Poland