Application of the Convolution Neural Network in the Text Sentiment Analysis

Jasmina Novakovic – Belgrade Business and Arts Academy of Applied Studies, Kraljice Marije 73, Zvezdara, 11000, Belgrade, Serbia

Suzana R. Markovic – Belgrade Business and Arts Academy of Applied Studies, Kraljice Marije 73, Zvezdara, 11000, Belgrade, Serbia

Abstract: Sentiment analysis deals with the analysis of the opinions, attitudes and emotions of the people who wrote a certain text. The goal of text sentiment analysis is to detect positive, neutral or negative sentiments from the text. The ability to automatically recognize emotions from text has many practical applications, such as customer sentiment analysis, social media monitoring, and customer feedback analysis. In this paper, we considered the application of neural networks in text sentiment analysis, using the Python programming language and Keras as a deep learning Python library. In the experimental part of the work, we trained a convolution neural network to classify text into different classes depending on the recognized emotions. We obtained a model that we tested on a test data set and considered the classification accuracy of the obtained model.

10th International Scientific Conference ERAZ 2024 – Conference Proceedings: KNOWLEDGE BASED SUSTAINABLE DEVELOPMENT,  hybrid – online, virtually and in person, Lisbon, Portugal, June 6, 2024

ERAZ Conference Proceedings published by: Association of Economists and Managers of the Balkans – Belgrade, Serbia

ERAZ conference partners: Faculty of Logistics, University of Maribor, Maribor (Slovenia); University of National and World Economy – UNWE, Sofia (Bulgaria); Center for Political Research and Documentation (KEPET), Research Laboratory of the Department of Political Science of University of Crete (Greece); Institute of Public Finance – Zagreb (Croatia); Faculty of Tourism and Hospitality Ohrid, University of St. Kliment Ohridski from Bitola (North Macedonia)

ERAZ Conference 2024 Conference Proceedings: ISBN 978-86-80194-86-8, ISSN 2683-5568,

DOI: https://doi.org/10.31410/ERAZ.2024

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission. 

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