Open Access

ARTICLE

An Optimized Deep Learning Model for Emotion Classification in Tweets

Chinu Singla¹, Fahd N. Al-Wesabi^2,3, Yash Singh Pathania¹, Badria Sulaiman Alfurhood⁴, Anwer Mustafa Hilal^5,*, Mohammed Rizwanullah⁵, Manar Ahmed Hamza⁵, Mohammad Mahzari⁶

1 Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India
2 Department of Computer Science, King Khalid University, Muhayel Aseer, Kingdom of Saudi Arabia
3 Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
4 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
6 Department of English, College of Science & Humanities, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2022, 70(3), 6365-6380. https://doi.org/10.32604/cmc.2022.020480

Received 26 May 2021; Accepted 13 August 2021; Issue published 11 October 2021

Abstract

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly presented in literature survey section. In this paper, hybrid combination of different model's LSTM-CNN have been proposed where LSTM is Long Short Term Memory and CNN represents Convolutional Neural Network. Furthermore, the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used. The main drawback of LSTM is that it's a time-consuming process whereas CNN do not express content information in an accurate way, thus our proposed hybrid technique improves the precision rate and helps in achieving better results. Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

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Citations