Abstract
Sentiment analysis and opinion summarization have become an important research area with the increase of available data on the Web. Since the Internet started containing more and more opinions and reviews for different products, individual users and companies saw the benefits of a priori evaluations based on other users’ experiences; thus, automated analyses centered on customer impressions and experiences emerged as crucial marketing instruments. Our aim is to create a scalable and easily extensible pipeline for building a custom-tailored sentiment analysis model for a specific domain. A corpus of around 200,000 games reviews was extracted, and three state-of-the-art models (i.e., support vector machines, multinomial Naïve-Bayes, and deep neural network) were employed in order to classify the reviews into positive, neutral, and negative. Current results surpass previous experiments based on word counts applied on a similar game reviews dataset, thus arguing for the adequacy of the proposed workflow.
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Acknowledgements
This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI—UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689/“Lib2Life—Revitalizarea bibliotecilor si a patrimoniului cultural prin tehnologii avansate”/“Revitalizing Libraries and Cultural Heritage through Advanced Technologies”, within PNCDI III.
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Ruseti, S., Sirbu, MD., Calin, M.A., Dascalu, M., Trausan-Matu, S., Militaru, G. (2020). Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_27
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DOI: https://doi.org/10.1007/978-981-15-0637-6_27
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