Emotion recognition for sentences with unknown expressions based on semantic similarity by using Bag of Concepts

In studies of emotion estimation from text, varieties of methods have been attempted such as emotional expression dictionary or sentence structure dictionary and emotion corpus. However, most of these methods targeted the expressions included in the existing morphological analysis dictionaries, as a result, they did not pay enough attention to unknown words, especially newly coined words. In Japan, the growth of Internet communication sites such as weblogs and social networking sites brought younger people especially in teens and in their 20s to create new words and use them very often. We prepared an emotion corpus by collecting weblog article texts including new words, analyzed the corpus statistically, and proposed a method to estimate emotions of the texts. Most slang such as Youth Slang is too ambiguous in sense classification to be registered into the existing dictionaries such as thesaurus. To cope with these words, we created a large scale of Twitter corpus to calculate sense similarity between words. We proposed to convert unknown to sense class id to process the words that were not included in learning data. We defined this as a method using Bag of Concepts as feature. As a result of the evaluation experiment using several classifies, the proposed method was proved robustness for unknown expression.