一种基于情感特征表示的跨语言文本情感分析模型

基于深度学习的跨语言情感分析模型需要借助预训练的双语词嵌入(Bilingual Word Embedding,BWE)词典获得源语言和目标语言的文本向量表示.为了解决BWE词典较难获得的问题,该文提出一种基于词向量情感特征表示的跨语言文本情感分析方法,引入源语言的情感监督信息以获得源语言情感感知的词向量表示,使得词向量...

A Cross-lingual Sentiment Analysis Model Based on Sentiment-aware Feature Representation

In cross-lingual sentiment analysis, pre-trained Bilingual Word Embedding (BWE) dictionaries are leveraged to generate text vector representations of source and target languages. In order to obtain a qualified BWE dictionary, a novel model is proposed to utilize the affective features in source language as supervised information for word representation generation. The representations we pre-trained contain both semantic and emotional information , suitable for sentiment prediction in target language. In our cross-lingual sentiment analysis experiments, the source language is English, and the target languages include Chinese, French, German, Japanese, Korean and Thai. The results show that the accuracy of our proposed model is about 9.3% higher than Machine Translation (MT) based method, and 8.7% higher than parallel method without sentiment-aware representations. As expected, the experiments on English and German sentiment classification achieved best performance, for both languages belong to the Germanic language group and are more similar in grammar and semantics.