Review on sentiment analysis for text classification techniques from 2010 to 2021

Ullah, Arif; Khan, Siraj; Nawi, Nazri Mohd

doi:10.1007/s11042-022-14112-3

Cited by 15 publications

(6 citation statements)

References 232 publications

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“…In this section, we present the different modified sections that took place in SVM by different research in different years. Table 2 (see in Appendix) presents the different categories of the result like the year, function, improvement, and objective of many researchers' modification in SVM for their research purpose [69]- [79]. In this research, we collect all the information related to SVM.…”

Section: Resultsmentioning

confidence: 99%

“…Common kernels are provided but it is also possible to specify the custom kernels. The addition of kernel functions together to achieve even more complex hyperplanes as 𝐾1 + 𝐾2 = 𝑐𝑜𝑚𝑝𝑙𝑒𝑥 ℎ𝑦𝑝𝑒𝑟 − 𝑝𝑙𝑎𝑛𝑒𝑠, where K1 and K2 represents the first and second kernel functions respectively [79]. Cons: the disadvantages of SVMs include: − SVM does not directly provide supervised probability estimates.…”

Section: Resultsmentioning

confidence: 99%

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Generalization of linear and non-linear support vector machine in multiple fields: a review

Khan,

Aznaoui

et al. 2023

Comput Sci Inf Technol

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Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Generalization of linear and non-linear support vector machine in multiple fields: a review

Khan,

Aznaoui

et al. 2023

Comput Sci Inf Technol

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“…Hybrid uses a combination of machine learning and lexicon methods to leverage their strengths and overcome their weaknesses. Hybrid can be ensemble-based or feature-based, depending on the way of combining the methods [8][9][10].…”

Section: Literature Reviewmentioning

confidence: 99%

MACCHIEF—Machine learning-based Algorithm Classification for Complaint Handling and Improved Efficiency in Firms

Trung,

Toh,

Mishra

et al. 2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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This research emphasizes the vital role of machine learning-driven consumer complaint management in information enterprises facing a surge in customer feedback across channels. By automating complaint categorization, analysis, and response, machine learning streamlines operations and uncovers invaluable customer insights. The study introduces a novel classification model, with LGBMClassifier and LinearSVC algorithms standing out for achieving 76.78% and 79.37% accuracy, respectively. This approach enhances complaint resolution, customer satisfaction, and enterprise competitiveness. The integration of machine learning offers a practical solution to consumer complaint challenges, with future prospects including adaptability to evolving preferences and leveraging natural language processing for deeper sentiment analysis.

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“…The data of the users consist of a huge number of writings in the form of text comments. This constructive information from social media is an emerging area for research [2]. The term "Deep Learning" (DL) pertains to the latest advancement in technology and current research emphasis in the field of Machine Learning.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ahmad¹,

Umar²

2023

IJCAI

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Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, positive, and neutral) from financial textual data using machine learning and deep learning algorithms. The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results of the MNB and LR obtained a good and very good rate of accuracy respectively. Likewise, the results of RNN, LSTM, and GRU obtained an excellent rate of accuracy. It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate. Povzetek: Raziskava analizira finančna besedila z uporabo klasičnega strojnega učenja in globokega učenja z namenom iskanja sentimenta (negativno, pozitivno, nevtralno).

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Review on sentiment analysis for text classification techniques from 2010 to 2021

Cited by 15 publications

References 232 publications

Generalization of linear and non-linear support vector machine in multiple fields: a review

Generalization of linear and non-linear support vector machine in multiple fields: a review

MACCHIEF—Machine learning-based Algorithm Classification for Complaint Handling and Improved Efficiency in Firms

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

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