SSN_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural Network
B. Senthil Kumar, D. Thenmozhi, Chandrabose Aravindan, Srinethe Sharavanan · 2019
Emotion identification is a process of identifying the emotions automatically from text, speech or images.Emotion identification from textual conversations is a challenging problem due to absence of gestures, vocal intonation and facial expressions.It enables conversational agents, chat bots and messengers to detect and report the emotions to the user instantly for a healthy conversation by avoiding emotional cues and miscommunications.We have adopted a Seq2Seq deep neural network to identify the emotions present in the text sequences.Several layers namely embedding layer, encoding-decoding layer, softmax layer and a loss layer are used to map the sequences from textual conversations to the emotions namely Angry, Happy, Sad and Others.We have evaluated our approach on the EmoContext@SemEval2019 dataset and we have obtained the micro-averaged F1 scores as 0.595 and 0.6568 for the pre-evaluation dataset and final evaluation test set respectively.Our approach improved the base line score by 7% for final evaluation test set.