Filter feature selection methods for text classification: a review

Hong, Minghui; Wang, Heyong

doi:10.1007/s11042-023-15675-5

Cited by 4 publications

(2 citation statements)

References 118 publications

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“…To improve interpretability, reduce overfitting, and lower dimensionality, thereby enhancing model performance (Ming and Heyong, 2024), we utilized SelectFrom-Model from the sklearn.feature selection library as a simple feature selection method, focusing solely on n-grams features. This meta-transformer selects features based on their importance weights, simplifying models by highlighting the most informative features, thus promoting efficiency and improving interpretability.…”

Section: Methodsmentioning

confidence: 99%

Exploring Multidimensional Aspects of Hope Speech Computationally: A Psycholinguistic and Emotional Perspective

Arif,

Tash,

Jamshidi

et al. 2024

Preprint

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Hope is a vital coping mechanism, enabling individuals to effectively confront life's challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study explores the potential of using this data to classify various hope types via machine learning algorithms. Notably, models such as LightGBM and CatBoost exhibit promising performance, outperforming traditional methods and rivaling deep learning techniques.

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Section: Methodsmentioning

confidence: 99%

Exploring Multidimensional Aspects of Hope Speech Computationally: A Psycholinguistic and Emotional Perspective

Arif,

Tash,

Jamshidi

et al. 2024

Preprint

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“…Generally, four types of FS commonly exist including filter, wrapper, embedded, and hybrid approaches [17]. Simple yet effective, the filter approach uses statistical analysis to compute each feature's score, selecting the features with the highest scores for the classification process.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Relevance Frequency Analysis Based Feature Selection for Classification of Short Text Data

Arumugam

2024

CSSE

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Text mining presents unique challenges in extracting meaningful information from the vast volumes of digital documents. Traditional filter feature selection methods often fall short in handling the complexities of short text data. To address this issue, this paper presents a novel approach to feature selection in text classification, aiming to overcome challenges posed by high dimensionality and reduced accuracy in the face of increasing digital document volumes. Unlike traditional filter feature selection techniques, the proposed method, Multivariate Relevance Frequency Analysis, offers a tailored solution for diverse text data types. By integrating positive, negative, and dependency relevance computations, the proposed approach effectively prunes features, enhancing classification performance. Extensive experimental analysis has been performed for the proposed model and compared with several standard existing feature selection models on five datasets involving short and long texts using four standard classifiers. The results indicate that the proposed model has the highest macro-F1 score of 94% for the SMS dataset, 78.1% for the SLS dataset, 89.4% for the AYSC dataset, 71.32% for the Reuters dataset, and 98.63% for the 20Newsgroup dataset. The statistical analysis also indicates that the proposed model provides better performance with both short texts such as messages and reviews as well as long texts containing documents, with superior performance for short-text data. The comparative analysis shows that the proposed model offers better performance than many other standard filtration models.

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Text classification based on optimization feature selection methods: a review and future directions

Alyasiri,

Cheah,

Zhang

et al. 2024

Multimed Tools Appl

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Filter feature selection methods for text classification: a review

Cited by 4 publications

References 118 publications

Exploring Multidimensional Aspects of Hope Speech Computationally: A Psycholinguistic and Emotional Perspective

Exploring Multidimensional Aspects of Hope Speech Computationally: A Psycholinguistic and Emotional Perspective

A Multivariate Relevance Frequency Analysis Based Feature Selection for Classification of Short Text Data

Text classification based on optimization feature selection methods: a review and future directions

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