Emotion Detection using Data Driven Models

TL;DR

This paper explores emotion detection in text using data-driven models, combining public datasets and applying TFIDF, Keras embeddings, classical ML, and CNNs, achieving notable accuracy improvements.

Contribution

It introduces a combined dataset approach and compares classical machine learning with deep learning models for emotion classification.

Findings

Logistic Regression achieved 75.6% accuracy.

CNN model achieved 45.25% accuracy.

Datasets used are publicly available and released for research.

Abstract

Text is the major method that is used for communication now a days, each and every day lots of text are created. In this paper the text data is used for the classification of the emotions. Emotions are the way of expression of the persons feelings which has an high influence on the decision making tasks. Datasets are collected which are available publically and combined together based on the three emotions that are considered here positive, negative and neutral. In this paper we have proposed the text representation method TFIDF and keras embedding and then given to the classical machine learning algorithms of which Logistics Regression gives the highest accuracy of about 75.6%, after which it is passed to the deep learning algorithm which is the CNN which gives the state of art accuracy of about 45.25%. For the research purpose the datasets that has been collected are released.