基于用户兴趣词典和LSTM的个性化情感分类方法

User Interest Dictionary and LSTM Based Method for Personalized Emotion Classification

微博是一个可以分享生活、发表看法、发泄情感的社交平台,由于数据量大且易于获取,微博数据已被广泛用于网络用户情感分析。传统对微博进行情感预测的研究没有考虑用户的用词喜好、语言风格等个性化因素的影响,使得情感分类结果的准确性不高。首先通过分析用户兴趣特征构建用户兴趣词典,在此基础上提出基于用户兴趣词典的情感分类模型;然后利用长短期记忆网络(Long Short-Term Memory,LSTM)分类准确性高的特点训练一个通用的LSTM分类模型;最后利用支持向量机融合不同模型以得到最终的情感分类结果。实验结果表明,与支持向量机、朴素贝叶斯等传统分类器相比,基于用户兴趣词典与LSTM的个性化情感分类方法在分类精度上有较大提升;与LSTM、循环神经网络等深度学习方法相比,该方法在保证运行效率的前提下能获得更高的分类精度。

Microblog is a social platform that people can share life,express opinions and vent emotions.Due to the large amount of data and easy access,the Microblog data has been widely used in emotion prediction for the web users.The traditional research on emotion classification of Microblog simply stays on the meaning of words,without considering the influence from the individuation of each person’s language preference and style,which results a lower accuracy of the emotion classification.Firstly,this paper constructs a user interest dictionary by analyzing user interest characteristics and proposes a user interest dictionary basedemotion classification model.Secondly,by using the advantage of high classification accuracy of Long Short-Term Memory(LSTM),this paper trains a common LSTM based classification model.Finally,this paper fuses different models by using Support Vector Machine to obtain the final emotion classification results.The experimental results show that,compared with traditional classifiers such as SVM and Naive Bayesian,the personalized emotion classification method based on user interest dictionary and LSTM has a great improvement on classification accuracy.Compared with typical deep learning methods like LSTM andRecurrent Neural Network,the proposed method can obtain higher classification accuracy while ensuring the execution efficiency.