The Impact of Pre-processing and Feature Selection on Text Classification

Nafis, Nur Syafiqah Mohd; Awang, Suryanti

doi:10.1007/978-981-15-1289-6_25

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…During stemming, the smaller numbers of characters are neglected from the words by utilizing the stemming process. The lemmatization approach converts the words into a meaningful form without eliminating any characters [25].…”

Section: Stemmingmentioning

confidence: 99%

ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

et al. 2023

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Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.

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Section: Stemmingmentioning

confidence: 99%

ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

et al. 2023

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“…x is term vector for sample i and, i y is the class label of sample i. Weighted vector or SVM score is defined by the sum square of the weight vector W of the SVMs using formula (4).…”

Section: B Train Features Using Svm-rfe For Feature Rankingmentioning

confidence: 99%

“…It also helps in improving classification accuracy, reducing computational time, and providing a better understanding of the model studied. Several researchers proved that feature selection gave an impact on their classification problems [4][5][6]. Feature selection approaches can be categorized into the filter, wrapper, and embedded.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

Nafis

Awang

2020

eijs

Self Cite

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Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document.

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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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The Impact of Pre-processing and Feature Selection on Text Classification

Cited by 5 publications

References 6 publications

ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

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

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