Understanding Emotions in Text Using Deep Learning and Big Data

By Ankush Chatterjee, Umang Gupta, Manoj Kumar Chinnakotla, Radhakrishnan Srikanth, Michel Galley, Puneet Agrawal. Computers in Human Behavior, 2019

350 citations recorded by the source corpus.

Abstract Big Data and Deep Learning algorithms combined with enormous computing power have paved ways for significant technological advancements. Technology is evolving to anticipate, understand and address our unmet needs. However, to fully meet human needs, machines or computers must deeply understand human behavior including emotions. Emotions are physiological states generated in humans as a reaction to internal or external events. They are complex and studied across numerous fields including computer science. As humans, on reading “Why don't you ever text me!”, we can either interpret it as a sad or an angry emotion and the same ambiguity exists for machines as well. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. However, in today's online world, humans are increasingly communicating using text messaging applications and digital agents. Hence, it is imperative for machines to understand emotions in textual dialogue to provide emotionally aware responses to users. In this paper, we propose a novel Deep Learning based approach to detect emotions - Happy, Sad and Angry in textual dialogues. The essence of our approach lies in combining both semantic and sentiment based representations for more accurate emotion detection. We use semi-automated techniques to gather large scale training data with diverse ways of expressing emotions to train our model. Evaluation of our approach on real world dialogue datasets reveals that it significantly outperforms traditional Machine Learning baselines as well as other off-the-shelf Deep Learning models.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/22a8824e12e0737f0c3a4d7df63fed0641b98df3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Experimental and Cognitive Psychology

Citations in the corpus

Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020, methodology): “…much attention from researchers in the past few years due to the proliferation of publicly available conversational dataset (Poria et al., 2019a; Chatterjee et al., 2019; Li et al., 2017; Zhou et al., 2018a) and its widespread applications in opinion mining, recommender systems, emotion-aware…”
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances (2019, methodology): “Due to the sequential nature of the utterances in conversations, RNNs are used for context generation in the aforemen- 7 Dataset # dialogues # utterancestrain val test train val test IEMOCAP 120 31 5810 1623 SEMAINE 63 32 4368 1430 EmotionLines 720 80 200 10561 1178 2764 MELD 1039 114 280 9989 1109 ”
HappyBot: Generating Empathetic Dialogue Responses by Improving User Experience Look-ahead (2019, background): “…has been a relatively well understood and researched task (Socher et al., 2013; Felbo et al., 2017; McCann et al., 2017; Xu et al., 2018) that has been deemed necessary for generating empathetic dialogues (Fung et al., 2016; Bertero et al., 2016; Chatterjee et al., 2019a,b; Winata et al., 2019).”
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text (2019, methodology): “Prior to this task, to the best of our knowledge, the methods proposed by Mundra et al. (2017) and Chatterjee et al. (2019) are some of the few methods that tackled the problem of emotion detection in English textual dialogues.”
Text‐based emotion detection: Advances, challenges, and opportunities (2020, background): “52,53 Some works implementing some of these techniques breaking records in text-based ED are discussed in the later sections.”
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (2019, methodology): “In recent years, there is an emerging research trend on emotion detection in conversational videos and multi-turn Tweets using deep learning methods (Hazarika et al., 2018b,a; Zahiri and Choi, 2018; Chatterjee et al., 2019; Zhong and Miao, 2019; Poria et al., 2019).”