基于协同过滤Attention机制的情感分析模型

该文主要研究在评论性数据中用户个性及产品信息对数据情感类别的影响。在影响数据情感类型的众多因素中,该文认为评价的主体即用户以及被评价的对象等信息对评论数据的情感至关重要。该文提出一种基于协同过滤Attention机制的情感分析方法(LSTM-CFA),使用协同过滤(CF)算法计算出用户兴趣分布矩阵,再将矩阵利用SVD分解后加入层次LSTM模型,作为模型注意力机制提取文档特征、实现情感分类。实验表明LSTM-CFA方法能够高效提取用户个性与产品属性信息,显著提升了情感分类的准确率。

Sentiment Analysis Based on Collaborative Filter Attention Mechanism

This paper investigates the influence of users personality and product information on data emotion category in review data. Among the many factors that affect the emotional data type, the subject of the evaluation, that is, the user and the object of the evaluation, are emotionally important to the commentary data. In this paper, an emotional analysis model (LSTM-CFA) based on cooperative filtering attention mechanism is proposed. The user interest distribution matrix is calculated by using the collaborative filtering (CF) algorithm. After the matrix is decomposed with SVD, the matrix is added to the hierarchical LSTM model as an attention mechanism in order to achieve emotion classification. Experiments show that the LSTM-CFA model can extract the information of users personality and product attribute efficiently, to improve the accuracy of emotion classification.