一种基于支持向量机和主题模型的评论分析方法

在移动应用软件中，用户评论是一种重要的用户反馈途径.用户可能提到一些移动应用使用中的问题，比如系统兼容性问题、应用崩溃等.随着移动应用软件的广泛流行，用户提供大量无结构化的反馈评论.为了从用户抱怨评论中提取有效信息，提出一种基于支持向量机和主题模型的评论分析方法RASL（review analysis method based on SVM and LDA）以帮助开发人员更好、更快地了解用户反馈.首先对移动应用的中、差评提取特征，然后使用支持向量机对评论进行多标签分类.随后使用LDA主题模型（latent dirichlet allocation）对各问题类型下的评论进行主题提取与代表句提取.从两个移动应用中爬取5 141条用户原始评论，并对这些评论分别用RASL方法和ASUM方法进行处理，得到两个新的文本.与经典方法ASUM相比，RASL方法的困惑度更低、可理解性更佳，包含更完整的原始评论信息，冗余信息也更少.

Review Analysis Method Based on Support Vector Machine and Latent Dirichlet Allocation

In mobile apps (applications), the app reviews by users have become an important feedback resource. Users may raise some issues when they use apps, such as system compatibility issues, application crashes, and so on. With the development of mobile apps, users provide a large number of unstructured feedback comments. In order to extract effective information from user complaint comments, a review analysis method is proposed based on support vector machine (SVM) and latent dirichlet allocation (LDA) (RASL) which can help developers to understand user feedback better and faster. Firstly, features are extracted from the user neutral reviews and negative reviews, and then the support vector machine (SVM) is used to label comments on multiple tags. Next, the LDA topic model is used to get topic extraction and representative sentence extraction which are performed on the comments under each question type. 5141 original reviews are crawled from two mobile apps. Then the proposed method (RASL) and ASUM are used to process these comments to get new texts. In comparison with the classical approach ASUM, RASL has less perplexity, better understandability, more complete original review information, and less redundant information.