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

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.