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2020 | OriginalPaper | Chapter

Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques

Authors : Stefan Ruseti, Maria-Dorinela Sirbu, Mihnea Andrei Calin, Mihai Dascalu, Stefan Trausan-Matu, Gheorghe Militaru

Published in: Fourth International Congress on Information and Communication Technology

Publisher: Springer Singapore

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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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Footnotes
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Metadata
Title
Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques
Authors
Stefan Ruseti
Maria-Dorinela Sirbu
Mihnea Andrei Calin
Mihai Dascalu
Stefan Trausan-Matu
Gheorghe Militaru
Copyright Year
2020
Publisher
Springer Singapore
Book
Fourth International Congress on Information and Communication Technology

Print ISBN: 978-981-15-0636-9

Electronic ISBN: 978-981-15-0637-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0637-6_27