A Survey on Sentiment Classification Methods and Challenges (original) (raw)

Deep Learning-based Sentiment Classification: A Comparative Survey

IEEE Access

Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DLbased SC approaches.

A Survey of Deep Learning for Sentiment Analysis

2019

Deep learning has detonated in the public responsiveness, primarily as predictive and analytical products pervade our world, in the form of innumerable humancentered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and mock-up self-driving vehicle systems. In contrast, researchers across disciplines have been including into their research to solve various natural language processing issues. In this paper we seek to provide a thorough exploration of Deep learning and its applications like sentimental analysis and natural language processing (NLP). Deep learning has an edge over the traditional machine learning algorithms, like support vector machine (SVM) and Naïve Bayes, for sentiment analysis because of its potential to overcome the challenges faced by sentiment analysis and handle the diversities involved, without the expensive demand for manual feature engineering. Deep learning models promise one thing given sufficient amount ...

Sentiment analysis with machine learning and deep learning: A survey of techniques and applications

International Journal of Science and Research Archive, 2024

Sentiment analysis is the task of automatically identifying the sentiment expressed in text. It has become increasingly important in many applications such as social media monitoring, product reviews analysis, and customer feedback evaluation. With the advent of deep learning techniques, sentiment analysis has seen significant improvements in performance and accuracy. This paper presents a comprehensive survey of machine learning and deep learning methods for sentiment analysis at the document, sentence, and aspect levels. We first provide an overview of traditional machine learning approaches to sentiment analysis and their limitations. We then look into various machine learning and deep learning architectures that have been successfully applied to this task. Additionally, we discuss the challenges of dealing with different data modalities, such as visual and multimodal data, and how both techniques have been adapted to address these challenges. Furthermore, we explore the applications of sentiment analysis in diverse domains, including social media, product reviews, and healthcare. Finally, we highlight the current limitations of deep learning approaches for sentiment analysis and outline potential future research directions. This survey aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art deep learning techniques for sentiment analysis and their practical applications.

Traditional and Deep Learning Approaches for Sentiment Analysis: A Survey

Advances in Science, Technology and Engineering Systems Journal

Presently, individuals generate tremendous volumes of information on the internet. As a result, sentiment analysis is a critical tool for automating a deep understanding of usergenerated information. Of late, deep learning algorithms have shown endless promises for a variety of sentiment analysis. The purpose of sentiment analysis is to categorize different descriptions as good, bad, or impartial based on context data. Numerous studies have been concentrated on sentiment analysis in addition to the ability to examine thoughts, views, and reactions. In this paper, we review classical and deep learning approaches that have been applied to various sentiment analysis tasks and their evolution over last years and provide performance analysis of different sentiment analysis models on particular datasets. In the end, we will highlight current challenges and suggested solutions that can be considered in future work to achieve better performance.

The social media sentiment analysis framework: deep learning for sentiment analysis on social media

International Journal of Electrical and Computer Engineering (IJECE), 2024

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.

Sentence-Level Sentiment Classification A Comparative Study Between Deep Learning Models

Journal of ICT Standardization

Sentiment classification provides a means of analysing the subjective information in the text and subsequently extracting the opinion. Sentiment analysis is the method by which people extract information from their opinions, judgments and emotions about entities. In this paper we propose a comparative study between the most deep learning models used in the field of sentiment analysis; L-NFS (Linguistique Neuro Fuzzy System), GRU (Gated Recurrent Unit), BiGRU (Bidirectional Gated Recurrent Unit), LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory) and BERT(Bidirectional Encoder Representation from Transformers), we used for this study a large Corpus contain 1.6 Million tweets, as devices we train our models with GPU (graphics processing unit) processor. As result we obtain the best Accuracy and F1-Score respectively 87.36% and 0.87 for the BERT Model.

Traditional or deep learning for sentiment analysis : A review

Multidiszciplináris tudományok, 2022

Getting the context out of the text is the main objective of sentiment analysis. Today's digital world provides us with many data raw forms: Twitter, Facebook, blogs, etc. Researchers need to convert this raw data into useful information for performing analysis. Many researchers devoted their precious time to get the text's polarity using deep learning and conventional machine learning methods. In this paper, we reviewed both the approaches to gain insight into the work done. This paper will help the researchers to choose the best methods for classifying the text. We pick some of the best articles and critically analyze them in different parameters like dataset used, feature extraction technique, accuracy, and resource utilization.

A Survey on Deep Learning Techniques for Sentiment Analysis

International Journal of Advanced Computer Technology, 2021

Social media is a rich source of information nowadays. If we look into social media, sentiment analysis is one of the challenging problems. Sentiment analysis is a substantial area of research in the field of Natural Language Processing. This survey paper reviews and provides the comparative study of deep learning approaches CNN, RNN, LSTM and ensemble-based methods.

IRJET- Sentiment analysis Using Machine Learning and Deep Learning: A Survey

IRJET, 2021

In recent years, internet strengthened itself as an influential platform that has changed the methods of business and communication. Sentiment Analysis (SA) become active research topic in the field of Natural Language Processing (NLP). It is the discipline that analyzes sentiment of people's opinions, attitude and emotions towards different entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. The growth of web and social media such as blogs, business reviews, and social networks have fueled awareness in Sentiment Analysis (SA). There are several methods to analyze sentiments and all methods have numerous challenges, lacks and limitations. Therefore, this area still demands attention of researchers as well as industrialists. The main objective of this survey is to demonstrate an overview of Sentiment Analysis (SA) techniques and highlights limitations from previous studies. Our contribution will provide quick review of latest papers and help the researchers to choose appropriate for their future work. Finally, we attempt to compare the approaches and conclude that which approach can provide high accuracy and highlights more useful algorithms for the SA.

Sentiment Analysis Using Deep Learning Techniques: A Review

International Journal of Advanced Computer Science and Applications

The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.