Abstract
Sentiment analysis is a natural language processing task where the goal is to classify the sentiment polarity of the expressed opinions, although the aim to achieve the highest accuracy in sentiment classification for one particular language, does not truly reflect the needs of business. Sentiment analysis is often used by multinational companies operating on multiple markets. Such companies are interested in consumer opinions about their products and services in different countries (thus in different languages). However, most of the research in multi-language sentiment classification simply utilizes automated translation from minor languages to English (and then conducting sentiment analysis for English). This paper aims to contribute to the multi-language sentiment classification problem and proposes a language independent approach which could provide a good level of classification accuracy in multiple languages without using automated translations or language-dependent components (i.e. lexicons). The results indicate that the proposed approach could provide a high level of sentiment classification accuracy, even for multiple languages and without the language dependent components.
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Kincl, T., Novák, M., Přibil, J. (2016). Sentiment Classification in Multiple Languages: Fifty Shades of Customer Opinions. In: Bilgin, M., Danis, H., Demir, E., Can, U. (eds) Business Challenges in the Changing Economic Landscape - Vol. 2. Eurasian Studies in Business and Economics, vol 2/2. Springer, Cham. https://doi.org/10.1007/978-3-319-22593-7_19
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DOI: https://doi.org/10.1007/978-3-319-22593-7_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22592-0
Online ISBN: 978-3-319-22593-7
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