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首页> 外文期刊>Journal of Advanced Computatioanl Intelligence and Intelligent Informatics >Joint Aspect Discovery, Sentiment Classification, Aspect-Level Ratings and Weights Approximation for Recommender Systems by Rating Supervised Latent Topic Model
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Joint Aspect Discovery, Sentiment Classification, Aspect-Level Ratings and Weights Approximation for Recommender Systems by Rating Supervised Latent Topic Model

机译：通过评级监督潜在主题模型，联合方面发现，情感分类，方面级别评级和重量逼近推荐系统的逼近

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摘要

Textual product reviews posted by previous shoppers have been serving as an important source of information that helps on-line shoppers to make their decisions. However, reading through all the reviews of a product is usually a time-demanding and frustrating task, especially when those reviews deliver conflicting information. Therefore, it is of great practical value to develop techniques to automatically generate brief but accurate summaries for the numerous reviews on shopping websites. There are currently two main research streams in review mining: one is joint aspect discovery and sentiment classification, the other one is aspect-level ratings and weights approximation. There exist a number of models in each of the two areas. However, no previous work that aims to solve the two problems simultaneously has been proposed. In this paper we propose Rating Supervised Latent Topic Model to integrate the two problems into an unified optimisation problem. In the proposed model, we employ a latent topic model for aspect discovery and sentiment classification and use a regression model to approximate aspect-level ratings and weights based on the output of the topic model. We test the proposed model on a review dataset crawled from Amazon.com. The preliminary experiment results show that the proposed model outperforms a number of state-of-the-art models by a considerable margin.
机译：以前的购物者发布的文本产品评论一直作为一个重要信息来源，帮助在线购物者做出决定。然而，阅读产品的所有审查通常是一个令人艰难的和令人沮丧的任务，特别是当这些评论提供冲突的信息时。因此，开发自动为购物网站众多审查的简要而准确的摘要是巨大的实用价值。目前有两个主要的研究流在审查采矿中：一个是联合方面发现和情绪分类，另一个是宽方级评级和权重近似。两个区域中的每一个都存在许多模型。然而，没有提出以前的工作，旨在同时解决这两个问题。在本文中，我们提出了评级监督潜在主题模型，将这两个问题集成到统一优化问题中。在所提出的模型中，我们采用了一个潜在主题模型，用于方面发现和情绪分类，并使用回归模型基于主题模型的输出来近似方面级评级和权重。我们在从Amazon.com爬行的评论数据集中测试所提出的模型。初步实验结果表明，所提出的模型通过相当多的边缘优于最先进的模型。

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