基于本体特征的影评细粒度情感分类

针对中文影评情感分类中缺少特征属性及情感强度层面的粒度划分问题,提出一种基于本体特征的细粒度情感分类模型。首先,利用词频逆文档频率(TF-IDF)和TextRank算法提取电影特征,构建本体概念模型。其次,将电影特征属性和普鲁契克多维度情绪模型与双向长短时记忆网络(Bi-LSTM)融合,构建了在特征粒度层面和八分类情感强度下的细粒度情感分类模型。实验中,本体特征分析表明:观影人对故事属性关注度最高,继而是题材、人物、场景、导演等特征;模型性能分析表明:基于特征粒度和八分类情感强度,与应用情感词典、机器学习、Bi-LSTM网络算法在整体粒度和三分类情感强度层面的其他5个分类模型相比,该模型不仅有较高的F1值(0. 93),而且还能提供观影人对电影属性的情感偏好和情感强度参考,实现了中文影评更细粒度的情感分类。

Fine-grained sentiment classification of film reviews based on ontological features

In view of the lack of feature attributes and the granularity division on emotion intensity level in Chinese film reviews,a fine-grained sentiment classification model based on ontological features was proposed. Firstly,Term Frequency-Inverse Document Frequency(TF-IDF)and TextRank algorithm were used to extract movie features and construct ontology conceptual model. Secondly,the film attributes and Plutchik's Wheel of Emotion were combined with Bidirectional Long Short-Term Memory (Bi-LSTM) neural network to build a fine-grained emotion classification model based on feature granularity level and eight-category emotion intensity. In the experiments,the analysis of ontological features shows that the movie viewers pay the most attention to the attributes of the story,followed by the features of theme,character,scene and director;Model performance analysis shows that,based on feature granularity and eight-category emotion intensity, compared with other five classification models using emotion dictionary,machine learning and Bi-LSTM network algorithm at the level of overall granularity and three-category emotion intensity,the proposed model not only has a higher F1 value (0. 93),but also can provide viewers with a reference to emotional preferences and emotional intensities of film attributes, and achieves a more fine-grained emotional classification of Chinese film reviews.