基于字词融合特征的微博情绪识别方法

文本情绪识别是自然语言处理问题中的一项基本任务。该任务旨在通过分析文本判断该文本是否含有情绪。针对该任务,提出了一种基于字词融合特征的微博情绪识别方法。相对于传统方法,所提方法能够充分考虑微博语言的特点,充分利用字词融合特征提升识别性能。具体而言,首先将微博文本分别用字特征和词特征表示；然后利用LSTM模型(或双向LSTM模型)分别从字特征和词特征表示的微博文本中提取隐层特征；最后融合两组隐层特征,得到字词融合特征,从而进行情绪识别。实验结果表明,该方法能够获得更好的情绪识别性能。

Emotion Recognition on Microblog Based on Character and Word Features

Text emotion recognition is an important task in the community of nature language processing.This task aims to predict the involving emotion towards a piece of text.This paper proposed a novel emotion recognition approach based on character and word features.Compared to most traditional approaches,this approach employs both the character and word features by considering the characteristic of microblog text.Specifically,the feature presentations of microblog are extracted respectively from characters and words.Then,a LSTM model (or Bi-directional LSTM model) is employed to extract the hidden feature presentations from the above feature presentations.Third,the two groups of hidden character and word feature representations are merged to perform emotion recognition.Empirical studies demonstrate the effectiveness of the proposed approach for emotion recognition on SINA microblog.