Abstract: Taking the emotional dependency tuple (EDT) as the basic structure of Chinese emotional expression, the news text theme emotion recognition task was divided into three progressive sub-tasks: topics identification, emotional tendentiousness analysis, subjective and objective classification. TF-IDF method was improved before identifying the topic, and then the cross-entropy-based method was combined to extract themes feature words. The topic representation of the news title was taken into consideration at the same time, and the title words were put into the theme feature set. The similarity between sentence and the topic feature vector was calculated based on the vector space model. Some statistical rules such as sentence position, sentence length and sentence's similarity with title were added on this foundation to get topic sentences. Finally, the emotional dependency tuple discriminant model was established to calculate sentences emotion and the subjective and objective judgment rule were used to filter out the tendency key sentence. The approaching to the best results of experiment based on COAE 2014 evaluation data shows that the classification method based on the EDT has high classification performance.

Key words: emotional dependency tuple, tendency key sentence, sentiment analysis, theme emotional