Thai Language Tweet Emotion Prediction based on Use of Emojis

Thai Language can be handled/considered in the same group of Chinese and Japanese where no explicit spaces exist between words. This article presents a work on the emotional identification of tweets based on the use of emojis which focuses on a Thai language context. The use of emojis in user tweets indicates the writer’s emotions. The first phase of this study was to collect Thai tweets, clean them, and then to make a primary classification of the emojis into groups using K-mean clustering. These group clusters are used as target outputs for the prediction of emoji classes. It was found that 22 is the appropriate K for considering 70 emojis for a collected set of tweets. The corpus includes any level of Thai language usage, which means that the processed data can consist of suffixes, slang, and unknown word from tokenization process. The vector representation advances the unknown accent. In sum, this research created a corpus of short messages collected from Twitter which were grouped into 22 emoji-classes. The corpus includes 7,825,857 messages prepared for classification based on emotions by applying 2 biLSTM layers. A table of emojis is proposed based on Ekman’s six basic emotions: anger, disgust, fear, joy, sadness, and surprise were evaluated in both objective and subjective tests. The results show that word vectors work well for the classification of emotions through the use of emojis.