SSN_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural Network

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 318–323 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 318 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, S. Srinethe Department of CSE, SSN College of Engineering, India {senthil,theni d,aravindanc}@ssn.edu.in srinethe16108@cse.ssn.edu.in Abstract Emotion identification is a process of iden- tifying 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 conversa- tional agents, chat bots and messengers to de- tect and report the emotions to the user in- stantly for a healthy conversation by avoid- ing emotional cues and miscommunications. We have adopted a Seq2Seq deep neural net- work to identify the emotions present in the text sequences. Several layers namely embed- ding 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 Oth- ers. 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. 1 Introduction Emotion identification is a process of identifying the emotions automatically from different modali- ties. Several research work have been presented on detecting emotions from text (Rao, 2016; Abdul- Mageed and Ungar, 2017; Samy et al., 2018; Al- Balooshi et al., 2018; Gaind et al., 2019), speech (Arias et al., 2014; Amer et al., 2014; Lim et al., 2016), images (Shan et al., 2009; Ko, 2018; Ayvaz et al., 2017; Faria et al., 2017; Mohammadpour et al., 2017) and video (Matsuda et al., 2018; Hos- sain and Muhammad, 2019; Kahou et al., 2016). Emotion understanding from video may be eas- ier by analyzing the body language, speech vari- ations and facial expressions. However, identifi- cation of emotions from textual conversations is a challenging problem due to absence of above factors. Emotions in text are not only identified by its cue words such as happy, good, bore, hurt, hate and fun, but also the presence of interjec- tions (e.g. “whoops”), emoticons (e.g. “:)”), id- iomatic expressions (e.g. “am in cloud nine”), metaphors (e.g. “sending clouds”) and other de- scriptors mark the existence of emotions in the conversational text. Recently, the growth of text messaging applications for communications re- quire emotion detection from conversation tran- scripts. This helps conversational agents, chat bots and messengers to avoid emotional cues and mis- communications by detecting the emotions during conversation. EmoContext@SemEval2019 shared task (Chatterjee et al., 2019) goal is to encourage more research in the field of contextual emotion detection in textual conversations. The shared task focuses on identifying emotions namely Angry, Happy, Sad and Others from conversation with three turns. Since, emotion detection is a classifi- cation problem, …

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