Abstract
Sentiment analysis is a very active and nowadays highly addressed research area. One of the problem in sentiment analysis is text classification in terms of its attitude, especially in reviews or comments from social media. In general, this problem can be solved by two different approaches: machine learning methods and based on lexicons. Methods based on lexicons require properly prepared lexicons which usually are obtained manually from experts and it costs a lot in terms of time and resources. This paper aims at automatic lexicon creation for sentiment analysis. There are proposed the methods based on Loopy Belief Propagation that starting from small set of seed words with a priori known sentiment value propagates the sentiment to whole Wordnet.
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Acknowledgements.
The work was partially supported by European Union, the ENGINE grant, agreement no 316097 (FP7) and by The National Science Centre, the decision no. DEC-2013/09/B/ST6/02317. The work was partially financed as part of the investment in the CLARIN-PL research infrastructure funded by the Polish Ministry of Science and Higher Education.
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Kulisiewicz, M., Kajdanowicz, T., Kazienko, P., Piasecki, M. (2015). On Sentiment Polarity Assignment in the Wordnet Using Loopy Belief Propagation. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_38
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DOI: https://doi.org/10.1007/978-3-319-19644-2_38
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