Abstract
The aim of this paper is to present the use of sentiment analysis in both Polish and English languages. This goal is related to the fact that the authors of this article have observed many sentences in both Polish and English, used in social media and on websites in Poland. The paper presents the principles of various inflectional forms that should be used in the preparation of the training dataset being the subject of the analysis. Therefore, one of the goals of this article is to identify possible problems that an analyst of sentiment analysis machine learning methods may misinterpret. The motivation of the study was to see if the same methods could be used to analyse sentiment in different languages. We decided to evaluate the possibility of using one sentiment evaluation mechanism, assuming the use of similarly prepared training sets. In addition, the article shows the principles and differences between these languages, including in terms of the possibility of gender identification based on the text. We presented the results of a case study that showed how machine learning tools treat unstructured data to find the right sentiment and what problems can be identified when delivering text in these two languages. The conducted study also showed the possibility of using Big Data sources, such as comments in the form of comments on websites or social media, in order to correctly identify the sentiment, which is not always the case if the training set is not prepared properly.
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Maślankowski, J., Majewicz, D. (2022). Multi-language Sentiment Analysis – Lesson Learnt from NLP Case Study. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_4
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