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Evaluating Acceptance of Video Games using Convolutional Neural Networks for Sentiment Analysis of User Reviews

Published: 12 September 2019 Publication History

Abstract

Video game and interactive media are currently among the most profitable industries in the world. In this competitive marketing, game producers are interested in designing products with aspects that increase user acceptance, such as a well written story, stable servers for multiplayer games, and fluid combat mechanics. Although user-expressed feelings about game aspects seem to correlate with user acceptance, sentiment analysis is under-exploited for video games user acceptance evaluation. In this poster, we propose an approach to evaluate the user acceptance of video games by using convolutional neural networks for aspect-based sentiment analysis of user text reviews.

References

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Zhao Jianqiang, Gui Xiaolin, and Zhang Xuejun. 2018. Deep Convolution Neural Networks for Twitter Sentiment Analysis. IEEE Access 6 (2018), 23253--23260.
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Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP '14). 1532--1543.
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Aliaksei Severyn and Alessandro Moschitti. 2015. Twitter Sentiment Analysis with Deep Convolutional Neural Networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). 959--962.
[4]
Mukesh Kumar Singh, Nikhil Batti, and Miram Mcgaugh. 2016. Text mining and sentiment analysis on video game user reviews using SAS Enterprise Miner. In Proceedings of the 24th Annual, Southeast SAS Users Group Conference (SESUG '16). 13.
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Björn Strååt and Harko Verhagen. 2017. Using User Created Game Reviews for Sentiment Analysis: A Method for Researching User Attitudes. In Proceedings of the 1st Workshop on Games-Human Interaction (GHItaly '17).
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SuperData. 2018. 2017 Year in Review: Digital Games and Interactive Media. Technical Report. SuperData Research Holdings Inc. https://www.superdataresearch. com/market-data/market-brief-year-in-review/

Cited By

View all
  • (2024)Exploring Players’ Perspectives: A Comprehensive Topic Modeling Case Study on Elden RingInformation10.3390/info1509057315:9(573)Online publication date: 18-Sep-2024
  • (2021)Sentiment Analysis using various Machine Learning and Deep Learning TechniquesJournal of the Nigerian Society of Physical Sciences10.46481/jnsps.2021.308(385-394)Online publication date: 29-Nov-2021

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Published In

cover image ACM Conferences
HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media
September 2019
326 pages
ISBN:9781450368858
DOI:10.1145/3342220
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2019

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Author Tags

  1. game acceptance
  2. machine learning
  3. neural network
  4. opinion mining
  5. sentiment analysis
  6. video game

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HT '19
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HT '19 Paper Acceptance Rate 20 of 68 submissions, 29%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2024)Exploring Players’ Perspectives: A Comprehensive Topic Modeling Case Study on Elden RingInformation10.3390/info1509057315:9(573)Online publication date: 18-Sep-2024
  • (2021)Sentiment Analysis using various Machine Learning and Deep Learning TechniquesJournal of the Nigerian Society of Physical Sciences10.46481/jnsps.2021.308(385-394)Online publication date: 29-Nov-2021

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