Global Filter–Wrapper method based on class-dependent correlation for text classification

Kermani, Fatemeh Zarisfi; Eslami, Esfandiar; Sadeghi, Faramarz

doi:10.1016/j.engappai.2019.07.003

Cited by 14 publications

(3 citation statements)

References 49 publications

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“…Basiri et al [12] focused on bias detection, while Li et al [21] excelled in text classification. Kermani et al [20] conducted mood analysis, suggesting potential for advanced methodologies. Ceron Andrea et al [7] demonstrated supervised sentiment analysis effectiveness in electoral monitoring.…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Taming Misinformation: Fake Review Detection on Social Media platform using Hybrid Ensemble Technique

Kalbhor,

Goyal,

Sankhla

2024

IJEER

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In today's digital world, we witness exponential growth in the generation of textual content on a daily basis. However, the widespread dissemination of information through social media, online forums, and news websites has given rise to the proliferation of fake views, opinions, and reviews, posing a significant challenge in the battle against misinformation and manipulation. Machine Learning has become increasingly integral to real-world online activities, particularly in the area of Artificial Intelligence. Traditional methods often struggle to keep pace with the relentless creation of internet data. Consequently, short text processing has emerged as a new domain for the application of Machine Learning. This is where sentiment analysis come to the forefront, offering potent tools for discerning the authenticity of online content. Detecting and combating these fabricated sentiments are crucial for preserving the integrity of information and ensuring informed decision-making. This work focus on the previously unexplored area of user comments on review data. By leveraging N-gram technique and hybrid ensemble classification approaches, the research addresses critical issues in fake reviews classification, and sentiment analysis. The aim of this work is to detect fake reviews with limited text using NLP feature extraction, and hybrid ensemble classification algorithms, ultimately contributing to the enhancement of information integrity and decision-making in the digital age.

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Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Taming Misinformation: Fake Review Detection on Social Media platform using Hybrid Ensemble Technique

Kalbhor,

Goyal,

Sankhla

2024

IJEER

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“…Moreover, feature selection is the selection of a subset of relevant features that are highly related to the criterion measure. Feature selection techniques can be classified as filter (Bommert et al, 2020), wrapper (Gokalp et al, 2020) and hybrid (Zarisfi Kermani et al, 2019). Filter methods measure the correlation (Karegowda et al, 2010), Chi-Square (Alshaer et al, 2021) and Information Gain.…”

Section: Text Feature Manipulationmentioning

confidence: 99%

Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era

Kyaw¹,

Tepsongkroh²,

Thongkamkaew³

et al. 2023

Asia Social Issues

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Since trading has been transformed into online platforms, marketing strategies have adapted to digital systems in order to enhance the Customer Relationship Management (CRM) in the E-commerce era. E-commerce systems are the most widely used digital platforms where customer information including personal, and behavioral information, flows as a big data stream. Conducting business intelligent observation on digital big data assists to improve digital marketing policy through the customer intention prediction, decision-making to advertise based on the target group clustering, and customer assist recommendation. To discover the business intelligent, sentiment analysis technology can assist as a solution to understand the customer behavior through the opinion mining where the natural language processing, text analysis, computational linguistics, and biometrics are conducted to analysis the customer information and feedbacks, for smart digital marketing applications. This research observes the applications of sentiment analysis in E-commerce systems as a comprehensive study, and the critical role of discovering business intelligent for smart digital marketing in E-commerce platforms is pointed out according to the technical perspective. Furthermore, the concept of a business intelligent framework integrated with the modelling of decision-making, prediction, and recommendation systems using the contribution of hybrid feature selection which is based on rule-based and machine learning-based sentiment analysis, is proposed for the future innovative smart digital marketing trend.

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“…The mutual information feature selection measures the common information that is found between the terms and the labels (Kermani, et al, 2019;Lim et al, 2017). The common information MI (t, c) is found in between the class c, while the term t is distinct on the level of co-occurrence between a feature f j and a class c i (Li et al, 2017;Lim et al, 2017).…”

Section: Normalized Pointwise Mutual Information Features Selectionmentioning

confidence: 99%

Multilabel Over-Sampling and Under-Sampling With Class Alignment for Imbalanced Multilabel Text Classification

Taha

Tiun

Rahman

et al. 2021

Journal of Information and Communication Technology

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Simultaneous multiple labelling of documents, also known as multilabel text classification, will not perform optimally if the class is highly imbalanced. Class imbalanced entails skewness in the fundamental data for distribution that leads to more difficulty in classification. Random over-sampling and under-sampling are common approaches to solve the class imbalanced problem. However, these approaches have several drawbacks; the under-sampling is likely to dispose of useful data, whereas the over-sampling can heighten the probability of overfitting. Therefore, a new method that can avoid discarding useful data and overfitting problems is needed. This study proposes a method to tackle the class imbalanced problem by combining multilabel over-sampling and under-sampling with class alignment (ML-OUSCA). In the proposed ML-OUSCA, instead of using all the training instances, it draws a new training set by over-sampling small size classes and under-sampling big size classes. To evaluate our proposed ML-OUSCA, evaluation metrics of average precision, average recall and average F-measure on three benchmark datasets, namely, Reuters-21578, Bibtex, and Enron datasets, were performed. Experimental results showed that the proposed ML-OUSCA outperformed the chosen baseline random resampling approaches; K-means SMOTE and KNN-US. Thus, based on the results, we can conclude that designing a resampling method based on the class imbalanced together with class alignment will improve multilabel classification even better than just the random resampling method.

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Global Filter–Wrapper method based on class-dependent correlation for text classification

Cited by 14 publications

References 49 publications

Taming Misinformation: Fake Review Detection on Social Media platform using Hybrid Ensemble Technique

Taming Misinformation: Fake Review Detection on Social Media platform using Hybrid Ensemble Technique

Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era

Multilabel Over-Sampling and Under-Sampling With Class Alignment for Imbalanced Multilabel Text Classification

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