Modelling and Sentiment Analysis of Game Reviews

游戏评论建模与情感分析

• 批准号: 2743745
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2743745
• 关键词: Modelling; Sentiment; Analysis; Game; Reviews

Nowadays, companies have access to an unprecedented amount of data from their users. However, extracting the knowledge that these companies need has proven to be a challenging task, and the gaming industry is not an exception. Traditional classification algorithms, such as Logistic regression and Naïve Bayes, as well as more recent ones that use (Deep) Neural Networks, such as Recurrent Neural Networks (RNNs) based algorithms, and the SotA algorithms, such as attention models, are three classes of algorithms that are frequently used for such applications. While the traditional algorithms are the most interpretable, the latter two have superior performance. During my PhD, I aim to investigate each of these algorithms for modelling and sentiment analysis of game reviews on social media. These results are aimed to be used by the game industry to better analyse their users' feedback and utilise various NLP tools to enhance their user experience.

如今，公司可以从用户那里获取前所未有的大量数据，然而，事实证明，提取这些公司所需的知识是一项具有挑战性的任务，游戏行业也不例外。朴素贝叶斯以及最近使用（深度）神经网络的算法（例如基于循环神经网络 (RNN) 的算法）和 SotA 算法（例如注意力模型）是经常用于此类目的的三类算法虽然传统算法是最容易解释的，但在攻读博士学位期间，我的目标是研究后一种算法，用于对社交媒体上的游戏评论进行建模和情感分析。游戏行业可以更好地分析用户的反馈并利用各种 NLP 工具来增强用户体验。