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

Yassin Belhareth, Chiraz Latiri

2021

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.

Paper Citation

in Harvard Style

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 - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 242-249. DOI: 10.5220/0010646600003058

in Bibtex Style

@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 - Volume 1: WEBIST,},
year={2021},
pages={242-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010646600003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks
SN - 978-989-758-536-4
AU - Belhareth Y.
AU - Latiri C.
PY - 2021
SP - 242
EP - 249
DO - 10.5220/0010646600003058