Sentence-level emotion mining based on combination of adaptive Meta-level features and sentence syntactic features

Nowadays, because of increasing of text data, recognizing the emotions of text can help to get a better comprehension of context. However, finding emotional information from text is a very complex task because it is needed to automatically understand of human sentences that usually are vague and dependent on context which should be interpreted and represented in different ways. The sense of a word can be inferred by investigating the frequency of occurrence of the word in a large corpus of annotated text. This paper has presented a method for learning adaptive lexicon from existing lexicon resources (static lexicons) to improve performance of emotion detection task for two data sets. The learning of adaptive lexicon would allow us to distinguish between the initial emotion of words in the static lexicons and adaptive emotion of words that is mentioned in context, and we get a better understanding of the emotional orientation of words. Furthermore, this study proposes a novel approach for emotion detection based on the combination of Meta-level features derived from static and adaptive lexicons and sentences syntactic features. To the best of our knowledge, this is the first study that provides a comprehensive analysis of the relative importance of a very diverse feature set for automatic emotion detection. Extensive experiments on ISEAR and Aman data sets show that learning adaptive lexicon enables emotion mining algorithms to be more accurate.