Open Access

ARTICLE

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

by Sami Ullah¹, Muhammad Ramzan Talib^1,*, Toqir A. Rana^2,3, Muhammad Kashif Hanif¹, Muhammad Awais⁴

1 Department of Computer Science, Government College University Faisalabad, 38000, Pakistan
2 Department of Computer Science and IT, The University of Lahore, 54590, Pakistan
3 School of Computer Sciences, Universiti Sains Malaysia (USM), 11800, Penang, Malaysia
4 Department of Software Engineering, Government College University Faisalabad, 38000, Pakistan

* Corresponding Author: Muhammad Ramzan Talib. Email:

Computers, Materials & Continua 2022, 72(2), 2323-2339. https://doi.org/10.32604/cmc.2022.025543

Received 27 November 2021; Accepted 30 December 2021; Issue published 29 March 2022

Abstract

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

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Keywords

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