Modelling and Sentiment Analysis of Game Reviews

如今，公司可以从用户那里获得前所未有的海量数据。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。传统的分类算法，如Logistic回归和朴素贝叶斯，以及使用(深度)神经网络的较新算法，如基于递归神经网络(RNN)的算法，以及SOTA算法，如注意力模型，是经常用于此类应用的三类算法。虽然传统算法是最容易解释的，但后两种算法具有更好的性能。在我的博士学位期间，我的目标是研究这些用于对社交媒体上的游戏评论进行建模和情感分析的算法。这些结果旨在供游戏行业更好地分析用户的反馈，并利用各种NLP工具来增强他们的用户体验。

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.