Calculating emotional score of words for user emotion detection in messenger logs

This paper utilized sentences containing emoticon from the articles in Yahoo! blogs to automatically detect user´s emotions from messenger logs. Four approaches, topical approach, emotional approach, retrieval approach, and lexicon approach, were proposed. Forty emoticon classes found in Yahoo! blog articles were used for experiments. Two experiments were performed. The first experiment classified sentences into 40 emoticon classes by calculating emotional scores of words. The second experiment took the Yahoo! and MSN messenger logs collected from users as the experimental materials, classified them into 40 emoticon classes by proposed approaches, and mapped 40 emoticon classes to 6 emotion classes to tell the user´s emotion. The best performance of the proposed approaches for user emotion detection was achieved by the topical approach and its micro-average precision 0.48 was satisfactory.