基于加权融合字词向量的中文在线评论情感分析

随着社交网络平台的广泛使用,涌现出大量蕴涵丰富情感信息的在线评论文本,分析评论中表达的情感对企业、平台等具有重要意义。为了解决目前针对在线评论短文本情感分析中存在特征提取能力弱以及忽略短文本本身情感信息的问题,提出一种基于文本情感值加权融合字词向量表示的模型——SVW-BERT模型。首先,基于字、词级别向量融合表示文本向量,最大程度获取语义表征,同时考虑副词、否定词、感叹句及疑问句对文本情感的影响,通过权值计算得到文本的情感值,构建情感值加权融合字词向量的中文短文本情感分析模型。通过网络平台在线评论数据集对模型的可行性和优越性进行验证。实验结果表明,字词向量融合特征提取语义的能力更强,同时情感值加权句向量考虑了文本本身蕴涵的情感信息,达到了提升情感分类能力的效果。

Chinese online comments sentiment analysis based on weighted char-word mixture word representation

The widespread use of social networking platforms has led to the emergence of emotionally rich online comment texts, analyzing the emotions expressed in comments is of great significance to companies, platforms, etc. In order to solve the current problem of weak feature extraction ability and ignoring the emotional information of short text in online comment short text sentiment analysis, this paper proposed a model based on text sentiment value weighted char-word mixture word representation-SVW-BERT. First, based on the fusion of character and word level vectors to represent text vectors for maximizing semantic representation. At the same time, considering the influence of adverbs, negative words, exclamation sentences and interrogative sentences on the sentiment of the text, used the weight to calculate the sentiment value of the text, constructing sentiment analysis model of Chinese short text based on text sentiment value weighted char-word mixture word representation. Through the network platform online reviews data set to validate the feasibility and the advantages of the model. The experimental results show that the char-word mixture word representation is stronger in semantic extraction, and the sentiment value weighted sentence vector considers the sentiment information contained in the text itself, which achieves the effect of improving the ability of sentiment classification.