Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks

Yassin Belhareth; Chiraz Latiri

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Paper

Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks

Topics: Application, Research project and Internet Technology; Big Data and the Web; Data Web Mining; Deep Learning

In Proceedings of the 17th International Conference on Web Information Systems and Technologies WEBIST - Volume 1, 242-249, 2021

Authors: Yassin Belhareth ¹ and Chiraz Latiri ²

Affiliations: ¹ 11LIPAH, ENSI, University of Manouba, Tunis, Tunisia ; ² University of Tunis El Manar, Tunis, Tunisia

Keyword(s): Sentiment Analysis, Past Textual Content, Text-mining, Deep-learning.

Abstract: Sentiment analysis in social networks plays an important role in different areas, and one of its main tasks is to determine the polarity of sentiments about many things. In this paper, our goal is to create a supervised machine learning model for predicting the polarity of users’ sentiments, based solely on their textual history, about a predefined topic. The proposed approach is based on neural network architectures: the long short term memory (LSTM) and the convolutional neural networks (CNN). To experiment our system, we have purposely created a collection from SemEval-2017 data. The results revealed that our approach outperforms the comparison approach.

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Paper citation in several formats:

Belhareth, Y. and Latiri, C. (2021). Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-536-4; ISSN 2184-3252, SciTePress, pages 242-249. DOI: 10.5220/0010646600003058

@conference{webist21,
author={Yassin Belhareth. and Chiraz Latiri.},
title={Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST},
year={2021},
pages={242-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010646600003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
TI - Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks
SN - 978-989-758-536-4
IS - 2184-3252
AU - Belhareth, Y.
AU - Latiri, C.
PY - 2021
SP - 242
EP - 249
DO - 10.5220/0010646600003058
PB - SciTePress