基于多媒体信息的双向LSTM情感分析方法

Sentiment Analysis Based on Bi-LSTM with Multimedia Information

随着信息技术的飞速发展,智慧政务的建设在中国如火如荼地展开。为了更好地服务社会,获取舆论的情感倾向变得至关重要。然而,由于媒体数据的多样性,例如讨论话题、文本正文、正文回复以及文本字数限制等原因,人们不仅要对文本正文进行分析,还必须对文本回复、讨论话题等多样文本信息,以及诸如表情符号、社交关系等因素进行建模。遗憾的是,很少有研究工作针对推文文本的回复及多媒体信息进行建模。本文针对推文正文回复、话题以及多媒体信息,提出一种新的双向长短时记忆网络CBi-LSTM (Content Bi-LSTM)对舆论进行情感分析。实验表明,文本信息和多媒体信息的融合能显著提高情感分析的准确性。

With the rapid development of information technology, the construction of intelligent government is in full swing in China. In order to serve society better, sentiment analysis is to be important in the future. However, due to the variety of the media data, such as the content of tweet, the title of topics, the reply and the limitation of the content, we not only need to analyze the content of tweets, but require to pay attention to some media information, such as reply and topics. While few work study media information to model context, this paper proposes a new model CBi-LSTM (Content Bi-LSTM) to study sentiment classification of multimedia information, such as reply, topic and so on. Experiments show that the fusion of text information and multimedia information can improve sentiment classification remarkably.