کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6862155 | 1439264 | 2017 | 37 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model](/preview/png/6862155.png)
چکیده انگلیسی
There is a growing need for automated tools which make predictions about the positivity or negativity of sentiment conveyed by text. Such tools have a number of important applications in game user research. They are useful for understanding users generally, as they may give Big Data researchers access to a new source of information about player learning environments. Sentiment analysis methods are also applicable to the detection of toxicity, and the identification of players or player messages that are a potential threat to the player experience. A major challenge in sentiment analysis, however, is developing portable models that can be applied to new domains with relatively little effort. In the present study we extend a lexicon-based sentiment extractor, SO-CAL, to the analysis of instant messages across 1000 games of StarCraft 2. We show that, with updates to dictionary entries that are tailored to the classification task at hand, SO-CAL constitutes a respectable classifier of sentiment and toxicity that is robust across differences in player region and league. We verify the performance of our toxicity detector against a sample of 2025 additional games. Our results support the proposal that lexicon-based sentiment extraction is a useful and portable method of sentiment analysis, and that it can be deployed to identify toxicity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 137, 1 December 2017, Pages 149-162
Journal: Knowledge-Based Systems - Volume 137, 1 December 2017, Pages 149-162
نویسندگان
Joseph J Thompson, Betty HM Leung, Mark R Blair, Maite Taboada,