Research Area:  Machine Learning

Abstract:

Sentiment analysis is a well-known topic in Natural Language Processing (NLP). After a lot of research in this sector, the Emotion Analysis of Conversations prompted out as a trending topic. The main idea behind the Emotion Analysis of Conversations is to extract and classify the emotions expressed through a conversation. The scope of this research is adopted from "EmoContext" (SemEval 2019 - Task 3), a competition conducted by Codalab - Microsoft. There are some models that achieve a significant accuracy in this section but they are too much complicated to understand. There is a need for a model that achieves significant accuracy with a simplified approach for many uses. Here, we present a model that has achieved a microF1 score of 0.742 with our simple CNN-BiLSTM model and customized FastText word embedding, which comfortably betters the 3rd Quartile value of 0.7317 and stands up into the top quarter of the leader board of EmoContext competition.

Keywords:  

Author(s) Name:  Jarsigan Vickneswaran; Piruntha Navanesan; Vahesan Vijayaratnam; Uthayasanker Thayasivam

Publisher name:  IEEE

DOI:  10.1109/ICTer51097.2020.9325442

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/document/9325442