TDBot at SemEval-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification Approach

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 335–339 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 335 TDBot at SemEval-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification Approach Sourabh Maity Teradata India Pvt. Ltd. Hyderabad, India sourabh.maity@teradata.com Abstract This paper presents the system developed to detect the contextual emotion (SemEval19 Task 3 (Chatterjee et al., 2019)) from con- versational dialogue. The system models the fact that emotion of a dialogue depends on the context of the conversation and not indepen- dent. It uses multiple layers in the deep learn- ing model where each layer bootstraps with the context of what has already been said in the conversation. 1 Introduction Over the years, we are getting more and more comfortable in text based conversations over the web, leading to increased interest in emotion anal- ysis. Such a conversation is no longer limited be- tween humans, it is now mainstream to use chat bots at various domains, e.g., customer care, HR management, virtual doctor etc. Needless to say that in a conversation human emo- tions needed to be handled with care and empa- thy. Due to this, the task of emotion detection is very important when our aim to use chat bots and voice assistants more effectively. It is more difficult when the conversation is text based, lack of facial expressions and voice modulations make detecting emotions in text a challenging problem (Gupta et al., 2017). In this SemEval19 Task 3 there were total three turns of dialogues; turn1 and turn3 were spoken by one participant of the conversation and turn2 was spoken by another participant as a reply of turn1. We are tasked to detect the emotion of turn3. So, turn1 and turn2 act as context for turn3. There are four different emotions in the data set, namely, happy, sad, angry and others. The prob- lem is modeled as a four class classification prob- lem where each of the emotions listed above is the target class. 2 System Description 2.1 Preprocessing This section describes the preprocessing steps of the system. Few of the steps are standard; the steps are just mentioned and are not discussed in detail. Rather the steps which are critical for the perfor- mance in the task are discussed in detail. Standard steps are: converting all letters to lower case, re- moving numbers, removing white spaces, remov- ing stop words, sparse terms and particular words. The most important preprocessing steps are: Expanding abbreviations: In chat data there are infinite number of possible abbreviations or short- hand uses, most of which are not standard. Those abbreviations can not be left in the data set as is, because there are no embedding for those. In my system , it is chosen to expand the top 10% of such abbreviations and others are ignored. For this, I created a map of abbreviation to expansion manu- ally by inspecting the data set. Few examples: lol → laugh out loud, ur → you …

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