用机器学习分析游戏视频评论情绪，帮开发者了解玩家真实反馈。
Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content
- 用YouTube API抓取游戏视频评论，经文本处理后用三种算法分类。
- SVM表现最佳，不同数据集准确率均高于其他算法。
- 揭示玩家偏好与批评趋势，适合游戏开发与社交舆情研究者参考。
游戏产业的快速发展推动了对用户情感理解的需求，尤其在YouTube等社交平台上的评论。本研究基于视频游戏内容，利用YouTube API收集相关评论，通过TextBlob进行预处理，并采用朴素贝叶斯、逻辑回归和支持向量机（SVM）进行分类。实验结果表明，SVM在多个数据集上表现最优，分类准确率最高。分析覆盖多款热门游戏视频，揭示了玩家偏好与批评趋势。研究强调先进情感分析对捕捉用户评论中细微情绪的重要性，为游戏设计优化与用户体验提升提供有效反馈。未来将结合更复杂的自然语言处理技术并拓展数据源，进一步完善游戏领域的情感分析。
原文摘要 · Abstract (English)
The rapid evolution of the gaming industry, driven by technological advancements and a burgeoning community, necessitates a deeper understanding of user sentiments, especially as expressed on popular social media platforms like YouTube. This study presents a sentiment analysis on video games based on YouTube comments, aiming to understand user sentiments within the gaming community. Utilizing YouTube API, comments related to various video games were collected and analyzed using the TextBlob sentiment analysis tool. The pre-processed data underwent classification using machine learning algorithms, including Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). Among these, SVM demonstrated superior performance, achieving the highest classification accuracy across different datasets. The analysis spanned multiple popular gaming videos, revealing trends and insights into user preferences and critiques. The findings underscore the importance of advanced sentiment analysis in capturing the nuanced emotions expressed in user comments, providing valuable feedback for game developers to enhance game design and user experience. Future research will focus on integrating more sophisticated natural language processing techniques and exploring additional data sources to further refine sentiment analysis in the gaming domain.
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