A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques

Abstract

Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.

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“…However, it noted limited usage in specific areas, such as emergencies. Concurrently, another study by Tiwari et al ( 2023 ) highlighted machine learning and ensemble learning namely bagging-based and boosting-based ensemble techniques as popular and effective mechanisms for sentiment analysis, offering valuable insights into prevalent practices and major publishing platforms. However, the study lacked a detailed comparison of various sentiment analysis techniques.…”

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“…In recent years, large language modeling (LLM) 1-3 has emerged as a transformative approach in natural language processing (NLP) 4,5 , revolutionizing various tasks such as machine translation 6,7 , text generation 8 , and sentiment analysis. 9 The introduction of the transformer model by Vaswani et al . marked a significant milestone in LLM.…”

Sentiment Difficulty in Aspect-Based Sentiment Analysis

“…Algorithms based on deeplearning [19] include RNNs [20,21], LSTMs [22][23][24], and transformers [25,26]. Ensemble-based methods [27] combine multiple classifiers, which can fall into either of the previous categories. In response to the large amount of work on the subject of sentiment analysis, a number of surveys have been carried out recently [27][28][29][30].…”

“…Ensemble-based methods [27] combine multiple classifiers, which can fall into either of the previous categories. In response to the large amount of work on the subject of sentiment analysis, a number of surveys have been carried out recently [27][28][29][30]. However, for several years now, research has been more focused on multimodal, multilingual sentiment analysis and on the finest level of sentiment analysis: the aspect-based level.…”