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Compare Machine Learning Models in Text Classification Using Steam User Reviews

Authors:

Junren LaiAuthors Info & Claims

ICSED '21: Proceedings of the 2021 3rd International Conference on Software Engineering and Development

Pages 40 - 45

https://doi.org/10.1145/3507473.3507480

Published: 13 February 2022 Publication History

Abstract

Text Classification and Sentiment Analysis of game reviews are viewed as important parts in not only academic fields but also in game studies. In this paper, with more than 400 thousand game reviews on Steam platform, we preprocess the data using different libraries (sklearn, nltk, and spaCy) and use them as inputs to build three sentiment classification models based on different algorithms (Naive Bayes, SVM, and Random Forest). In contrast to previous studies that only focus on different sentiment analysis models, our paper also highlights the use of different APIs to preprocess the data and their corresponding model performance. The results show that no matter which API we choose, Random Forest models always perform the best. However, in terms of training time, Naive Bayes is the fastest. This work can be used to apply grid search for researchers to automatically find the optimum API before conducting sentiment analysis in the future.

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Cited By

Sindhu PPeruri GYalavarthi M(2024)An empirically based object-oriented testing using Machine learningEAI Endorsed Transactions on Internet of Things10.4108/eetiot.534410Online publication date: 8-Mar-2024
https://doi.org/10.4108/eetiot.5344
Abdul-Rahman SAli MBakar AMutalib S(2024)Enhancing churn forecasting with sentiment analysis of steam reviewsSocial Network Analysis and Mining10.1007/s13278-024-01337-314:1Online publication date: 31-Aug-2024
https://doi.org/10.1007/s13278-024-01337-3

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ICSED '21: Proceedings of the 2021 3rd International Conference on Software Engineering and Development

November 2021

75 pages

ISBN:9781450385213

DOI:10.1145/3507473

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 February 2022

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ICSED 2021

ICSED 2021: 2021 3rd International Conference on Software Engineering and Development

November 19 - 21, 2021

Xiamen, China

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Cited By

Sindhu PPeruri GYalavarthi M(2024)An empirically based object-oriented testing using Machine learningEAI Endorsed Transactions on Internet of Things10.4108/eetiot.534410Online publication date: 8-Mar-2024
https://doi.org/10.4108/eetiot.5344
Abdul-Rahman SAli MBakar AMutalib S(2024)Enhancing churn forecasting with sentiment analysis of steam reviewsSocial Network Analysis and Mining10.1007/s13278-024-01337-314:1Online publication date: 31-Aug-2024
https://doi.org/10.1007/s13278-024-01337-3

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