Sentiment Analysis Over Tweets Using Gated Recurrent Unit

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Published: 2023-09-09

Page: 336-344


Agu Edward O.

Department of Computer Science, Federal University Wukari, Nigeria.

Bako Jeremy Zevini *

Department of Bursary, Taraba University Jalingo, Nigeria.

Andrew Ishaku Wreford

Department of Computer Science, Federal University Wukari, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

Sentiment analysis, a fundamental task in natural language processing, plays a pivotal role in deciphering the emotional tone conveyed within textual content. In this study, we delve into the realm of sentiment analysis, focusing specifically on the application of a Gated Recurrent Unit (GRU) model  to the 2016 Donald Trump tweets dataset. By employing the GRU model with carefully chosen parameter settings, we aim to unravel the sentiment dynamics inherent in the dataset. Subsequently, we present the outcomes of our experiment, where we assess the model's performance using critical  metrics such as accuracy, precision, and recall. Notably, our findings reveal an accuracy rate of   92%, attesting to the model's proficiency in accurately categorizing sentiments. Moreover, precisionand recall both achieve impressive scores of 94%, underscoring the model's adeptness in recognizing  both positive and negative sentiments. Through this investigation, we illuminate the capability of the  GRU model to conduct sentiment analysis effectively, shedding light on the emotional undercurrents within the 2016 Donald Trump dataset and enhancing our understanding of sentiments during the election period.

Keywords: Sentiment analysis, tweets, gated recurrent unit (GRU), digitalization, networks


How to Cite

O. , A. E., Zevini, B. J., & Wreford, A. I. (2023). Sentiment Analysis Over Tweets Using Gated Recurrent Unit . Asian Journal of Pure and Applied Mathematics, 5(1), 336–344. Retrieved from https://globalpresshub.com/index.php/AJPAM/article/view/1855

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References

Zhang B, Provost EM. Multimodal Behavior Analysis in the Wild. Computational Research Methods and Techniques; 2019.

Khan R, Rustam F, Kanwal K, Mehmood A, Choi GS. US Based COVID-19 Tweets Sentiment Analysis Using TextBlob and Supervised Machine Learning Algorithms; 2021.

Available:https://doi.org/10.3390/mca23010011

D’Aniello G, Gaeta M, La Rocca I. KnowMIS-ABSA: An overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis; 2022.

Available:https://doi.org/10.1007/s10462-021-10134-9

Kemunto L, Owaa J, Raburu P. Student discipline and its influence on occupational stress among Secondary School Teachers in Kenya. Asian Research Journal of Current Science. 2021;144-160.

Sadiki L. Tunisia's Peripheral Cities: Marginalization and Protest Politics in a Democratizing Country. The Middle East Journal. 2021;75(1):77-98.

Dey N, Borah S, Ashour AS. Social Network Analytics. Computational Research Methods and Techniques; 2019.

Vajjala S, Majumder B, Gupta A, Surana H. Practical natural language processing: a comprehensive guide to building real-world NLP systems. O'Reilly Media; 2020.

Dang CN, Moreno-García MN, De la Prieta F. Hybrid deep learning models for sentiment analysis. Complexity. 2021;1-16.

Zainuddin N, Selamat A, Ibrahim R. Hybrid sentiment classification on Twitter aspect-based sentiment analysis. Applied Intelligence. 2018;48(5):1218-1232.

Ruz GA, Henríquez PA, Mascareño A. Sentiment analysis of Twitter data during critical events through Bayesian network classifiers. Future Generation Computer Systems. 2020;106:92-104.

Ray B, Avishek G, Ram S. An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews; 2020.

Available:www.elsevier.com/locate/asoc

Islam J, Zhang Y. Visual Sentiment Analysis for Social Images Using Transfer Learning Approach, 2016 IEEE Int. Conf. Big Data Cloud Comput. (BDCloud), Soc. Comput. Netw. (Social Com), Sustain. Computation Communication. 2016;124-130:2016.

Minghai C, Sen W, Paul PL, Tadas B, Amir Z, Louis-Philippe M. Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning. In Proceedings of 19th ACM International Conference on Multimodal Interaction (ICMI’17). 2017 ;163-173.

Available:https://doi.org/10.1145/3136755.3136801

Faithful CO, Joseph MW, Joseph KM. Support Vector Machine for Sentiment Analysis of Nigerian Bank's Financial Tweets. Journal of Data Analysis and Information Processing. 2019;7:153-173.

ISSN: 2327-7203.

Available:https://www.scirp.org/journal/jdaip

Štrimaitis R, Stefanoviˇc P, Ramanauskaite˙ S, Slotkiene A. Financial Context News Sentiment Analysis for the Lithuanian Language. Appl. Sci. 2021;11:4443.

Available:https:// doi.org/10.3390/app1110444. Available online: https://www.kaggle.com/

(Accessed on 16 March 2021).

Wang X, Chen X, Tang M, Yang T, Wang Z. Aspect-level sentiment analysis based on position features using multilevel interactive bidirectional GRU and attention mechanism. Discrete Dynamics in Nature and Society. 2020;2020:1-13.

Shah D, Li Y, Hadaegh A. Twitter based sentiment analysis of each presidential candidate using long short-term memory. International Journal of Computer Science and Security. 2021;15:87-96.