Sentiment Analysis with Different Deep Learning Methods

Zixiang Chen

2024

Abstract

Sentiment analysis is a task of natural language processing that seeks to identify and produce the feelings or viewpoints conveyed in written or spoken communication. It has various applications, such as social media analysis, product reviews, customer service, chatbots, recommender systems, etc. In this paper, the author evaluates the method on a large-scale dataset of fine foods reviews from Amazon, and compares it with several models namely CNN, RNN and LSTM. The paper evaluates the outcomes of each model on three metrics: Recall, Precision and F1 Score. The findings indicate that LSTM outperforms both TextCNN and RNN across all metrics, making it the most effective model for this task. The paper also discusses the possible reasons for the superiority of LSTM, such as its capacity to record context and long-term dependencies. The paper also analyzes the advantages and disadvantages of TextCNN and RNN, such as their speed, simplicity, and robustness. The paper provides empirical evidence for the effectiveness of different models for sentiment analysis.

Paper Citation

in Harvard Style

Chen Z. (2024). Sentiment Analysis with Different Deep Learning Methods. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 154-159. DOI: 10.5220/0012832800004547

in Bibtex Style

@conference{icdse24,
author={Zixiang Chen},
title={Sentiment Analysis with Different Deep Learning Methods},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={154-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012832800004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Sentiment Analysis with Different Deep Learning Methods
SN - 978-989-758-690-3
AU - Chen Z.
PY - 2024
SP - 154
EP - 159
DO - 10.5220/0012832800004547
PB - SciTePress