Extracting Gamers' Opinions from Reviews

2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2016-09-01 DOI:10.1109/SYNASC.2016.044

Dorinela Sirbu, Ana Secui, M. Dascalu, S. Crossley, Stefan Ruseti, Stefan Trausan-Matu

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引用次数: 8

Abstract

Opinion mining and sentiment analysis are a trending research domain in Natural Language Processing focused on automatically extracting subjective information, feelings, opinions, ideas or emotions from texts. Our study is centered on identifying sentiments and opinions, as well as other latent linguistic dimensions expressed in on-line game reviews. Over 9500 entertainment game reviews from Amazon were examined using a Principal Component Analysis applied to word-count indices derived from linguistic resources. Eight affective components were identified as being the most representative semantic and sentiment-oriented dimensions for our dataset. These components explained 51.2% of the variance of all reviews. A Multivariate Analysis of Variance showed that five of the eight components demonstrated significant differences between positive, negative and neutral game reviews. These five components used as predictors in a Discriminant Function Analysis, were able to classify game reviews into positive, negative and neutral ratings with a 55% accuracy.

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从评论中提取玩家意见

观点挖掘和情感分析是自然语言处理领域的一个趋势研究领域，其重点是从文本中自动提取主观信息、感觉、观点、想法或情感。我们的研究集中于识别在线游戏评论中表达的情感和观点，以及其他潜在的语言维度。我们使用主成分分析(Principal Component Analysis)对来自亚马逊的9500多条娱乐游戏评论进行了分析，该分析应用于源自语言资源的单词计数指数。八个情感成分被确定为我们数据集中最具代表性的语义和情感导向维度。这些成分解释了所有评论中51.2%的差异。多元方差分析显示，8个成分中有5个在积极、消极和中立的游戏评价之间表现出显著差异。在判别函数分析(Discriminant Function Analysis)中，这5个成分能够以55%的准确率将游戏评论划分为正面、负面和中性评级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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