Recognition of Emotion from Emoticon with Text in Microblog Using LSTM

With the advent of internet technology and social media, patterns of social communication in daily lives have changed whereby people use different social networking platforms. Microblog is a new platform for sharing opinions by means of emblematic expressions, which has become a resource for research on emotion analysis. Recognition of emotion from microblogs (REM) is an emerging research area in machine learning as the graphical emotional icons, known as emoticons, are becoming widespread with texts in microblogs. Studies hitherto have ignored emoticons for REM, which led to the current study where emoticons are translated into relevant emotional words and a REM method is proposed preserving the semantic relationship between texts and emoticons. The recognition is implemented using a Long-Short-Term Memory (LSTM) for the classification of emotions. The proposed REM method is verified on Twitter data and the recognition performances are compared with existing methods. The higher recognition accuracy unveils the potential of the emoticon-based REM for Microblogs applications.