A Comparative Analysis of Deep Learning Techniques for Sentiment Analysis Using Social Media Content

Satyendra Sıngh; Krishan Kumar; Brajesh Kumar; Raj Kumar; Nipur Singh

Makale detayları

A Comparative Analysis of Deep Learning Techniques for Sentiment Analysis Using Social Media Content

Yazarlar:

Satyendra Sıngh | Krishan Kumar | Brajesh Kumar | Raj Kumar | Nipur Singh

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Automated Software Engineering | Reinforcement Learning

Dergi:

Turkish Journal of Engineering Vol.9 Issue 4, pp.754-767

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001360059

pISSN:

2587-1366

Anahtar kelimeler:

Özet:

 Sentiment analysis SA is an influential task in natural language processing that aims to understand and categorize the underlying sentiment expressed in text. Due to the fast growth of technology, social media is becoming more familiar in human daily life. Social media is a platform for people to share and express their opinions, experiences, attitudes, reactions, etc. The purpose of sentiment analysis is to identify whether the emotion conveyed in a classified text is positive, negative, neutral, or any other individual sentiment to understand the emotional context of the text. Deep learning techniques have shown remarkable performance in sentiment analysis tasks, outperforming traditional machine learning algorithms. This article presents a comparative analysis of three deep learning models, including multilayer perceptron MLP, 1-dimensional convolutional neural networks 1D-CNN, and long short-term memory LSTM networks, for sentiment analysis of social media contents SMC. The experiments are conducted on publicly available benchmark datasets of US airlines sentiment tweets for binary and ternary classes. Likewise, we explore the impact of various pre-processing techniques, such as punctuation elimination, erasing special symbols, stop word removal, strange word removal, converting a lowercase, stemming, lemmatization, and tokenization in improving the performance of deep learning models for sentiment analysis. The results demonstrate that the LSTM network for binary class dataset achieves a high accuracy rate of 94.67, F1-S value of 95.26 and a low error rate of 5.33 in sentiment analysis tasks, followed by 1D-CNN and MLP. Besides, the MLP technique gains better results in comparison to other methods for the ternary class datasets. The findings of this study contribute to the existing literature by providing insights into the comparative performance of different deep-learning architectures for sentiment analysis and highlighting the importance of pre-processing techniques in achieving accurate sentiment classification.

 Sentiment analysis SA is an influential task in natural language processing that aims to understand and categorize the underlying sentiment expressed in text. Due to the fast growth of technology, social media is becoming more familiar in human daily life. Social media is a platform for people to share and express their opinions, experiences, attitudes, reactions, etc. The purpose of sentiment analysis is to identify whether the emotion conveyed in a classified text is positive, negative, neutral, or any other individual sentiment to understand the emotional context of the text. Deep learning techniques have shown remarkable performance in sentiment analysis tasks, outperforming traditional machine learning algorithms. This article presents a comparative analysis of three deep learning models, including multilayer perceptron MLP, 1-dimensional convolutional neural networks 1D-CNN, and long short-term memory LSTM networks, for sentiment analysis of social media contents SMC. The experiments are conducted on publicly available benchmark datasets of US airlines sentiment tweets for binary and ternary classes. Likewise, we explore the impact of various pre-processing techniques, such as punctuation elimination, erasing special symbols, stop word removal, strange word removal, converting a lowercase, stemming, lemmatization, and tokenization in improving the performance of deep learning models for sentiment analysis. The results demonstrate that the LSTM network for binary class dataset achieves a high accuracy rate of 94.67, F1-S value of 95.26 and a low error rate of 5.33 in sentiment analysis tasks, followed by 1D-CNN and MLP. Besides, the MLP technique gains better results in comparison to other methods for the ternary class datasets. The findings of this study contribute to the existing literature by providing insights into the comparative performance of different deep-learning architectures for sentiment analysis and highlighting the importance of pre-processing techniques in achieving accurate sentiment classification.

 Sentiment analysis SA is an influential task in natural language processing that aims to understand and categorize the underlying sentiment expressed in text. Due to the fast growth of technology, social media is becoming more familiar in human daily life. Social media is a platform for people to share and express their opinions, experiences, attitudes, reactions, etc. The purpose of sentiment analysis is to identify whether the emotion conveyed in a classified text is positive, negative, neutral, or any other individual sentiment to understand the emotional context of the text. Deep learning techniques have shown remarkable performance in sentiment analysis tasks, outperforming traditional machine learning algorithms. This article presents a comparative analysis of three deep learning models, including multilayer perceptron MLP, 1-dimensional convolutional neural networks 1D-CNN, and long short-term memory LSTM networks, for sentiment analysis of social media contents SMC. The experiments are conducted on publicly available benchmark datasets of US airlines sentiment tweets for binary and ternary classes. Likewise, we explore the impact of various pre-processing techniques, such as punctuation elimination, erasing special symbols, stop word removal, strange word removal, converting a lowercase, stemming, lemmatization, and tokenization in improving the performance of deep learning models for sentiment analysis. The results demonstrate that the LSTM network for binary class dataset achieves a high accuracy rate of 94.67, F1-S value of 95.26 and a low error rate of 5.33 in sentiment analysis tasks, followed by 1D-CNN and MLP. Besides, the MLP technique gains better results in comparison to other methods for the ternary class datasets. The findings of this study contribute to the existing literature by providing insights into the comparative performance of different deep-learning architectures for sentiment analysis and highlighting the importance of pre-processing techniques in achieving accurate sentiment classification.

 Sentiment analysis SA is an influential task in natural language processing that aims to understand and categorize the underlying sentiment expressed in text. Due to the fast growth of technology, social media is becoming more familiar in human daily life. Social media is a platform for people to share and express their opinions, experiences, attitudes, reactions, etc. The purpose of sentiment analysis is to identify whether the emotion conveyed in a classified text is positive, negative, neutral, or any other individual sentiment to understand the emotional context of the text. Deep learning techniques have shown remarkable performance in sentiment analysis tasks, outperforming traditional machine learning algorithms. This article presents a comparative analysis of three deep learning models, including multilayer perceptron MLP, 1-dimensional convolutional neural networks 1D-CNN, and long short-term memory LSTM networks, for sentiment analysis of social media contents SMC. The experiments are conducted on publicly available benchmark datasets of US airlines sentiment tweets for binary and ternary classes. Likewise, we explore the impact of various pre-processing techniques, such as punctuation elimination, erasing special symbols, stop word removal, strange word removal, converting a lowercase, stemming, lemmatization, and tokenization in improving the performance of deep learning models for sentiment analysis. The results demonstrate that the LSTM network for binary class dataset achieves a high accuracy rate of 94.67, F1-S value of 95.26 and a low error rate of 5.33 in sentiment analysis tasks, followed by 1D-CNN and MLP. Besides, the MLP technique gains better results in comparison to other methods for the ternary class datasets. The findings of this study contribute to the existing literature by providing insights into the comparative performance of different deep-learning architectures for sentiment analysis and highlighting the importance of pre-processing techniques in achieving accurate sentiment classification.

Makale detayları

A Comparative Analysis of Deep Learning Techniques for Sentiment Analysis Using Social Media Content

Yazarlar:

Satyendra Sıngh | Krishan Kumar | Brajesh Kumar | Raj Kumar | Nipur Singh

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Automated Software Engineering | Reinforcement Learning

Dergi:

Turkish Journal of Engineering Vol.9 Issue 4, pp.754-767

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001360059

pISSN:

2587-1366

Natural Language Processing

Sentiment Analysis

Social Media

Deep Learning

Sayı

Makale detayları

A Comparative Analysis of Deep Learning Techniques for Sentiment Analysis Using Social Media Content

Yazarlar: Satyendra Sıngh | Krishan Kumar | Brajesh Kumar | Raj Kumar | Nipur Singh

Yayıncı: Murat YAKAR

Ana Başlıklar: Automated Software Engineering | Reinforcement Learning

Dergi: Turkish Journal of Engineering Vol.9 Issue 4, pp.754-767

Belge Tipi: Makale

Yayın Tarihi: 8/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001360059

pISSN: 2587-1366

Natural Language Processing

Sentiment Analysis

Social Media

Deep Learning

Sayı

Yazarlar:

Satyendra Sıngh | Krishan Kumar | Brajesh Kumar | Raj Kumar | Nipur Singh

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Automated Software Engineering | Reinforcement Learning

Dergi:

Turkish Journal of Engineering Vol.9 Issue 4, pp.754-767

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001360059

pISSN:

2587-1366