Research on Feature Mining Algorithm Based on Product Reviews

Wang, Hao; Yang, Yuan; Kou, Jie; Zhang, Xinhui; Wang, Chao; Duan, Jianyong

doi:10.1109/icaica.2019.8873491

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…In the case of TF-IDF vectorization, some others have introduced the use of the TF-IDF vectorization model to capture the semantic similarity of news articles to identify unifiable groups by using the k-means algorithm to cluster the vectorized news articles to produce the best results in terms of cluster purity [6]. As seen, [19] conducted feature reduction by the TF-IDF method, and then based on the mutual information method, adding relative word frequency factor and combining the weight of feature items, the mining process is adaptively improved, which makes the frequency information of feature items effectively used. Bation et al [7] proposed an automatic document classifier of Tagalog news articles by stemming each document, representing it with TF-IDF values, and using it to train an SVM classifier.…”

Section: Dimensionality Reduction For Classification Of Filipino Text Documents Based On Improved Bayesianvectorization Techniquementioning

confidence: 99%

Dimensionality Reduction for Classification of Filipino Text Documents based on Improved BayesianVectorization Technique

2020

IJATCSE

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Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities to reduce the size of the feature vector used in the mining tasks to achieve a higher classification accuracy. While dimensionality reduction for text classification is becoming a great area of research in most languages, Filipino documents have received little or no attention from researchers. Thus, this paper addresses the issue of dimensionality reduction in representing relevant data from Filipino texts using an improved Bayesian vectorization technique. To validate the effectiveness of improved Bayesian vectorization, the model was compared to the Term Frequency and Inverse Document Frequency (TF-IDF) method. The outcomes are presented using standard measures such as precision, recall, f-score, and accuracy. The results revealed that the improved Bayesian vectorization has significantly better results having 98% classification accuracy compared to 76% classification accuracy of the TF-IDF vectorization technique.

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Section: Dimensionality Reduction For Classification Of Filipino Text Documents Based On Improved Bayesianvectorization Techniquementioning

confidence: 99%

Dimensionality Reduction for Classification of Filipino Text Documents based on Improved BayesianVectorization Technique

2020

IJATCSE

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“…Also, the frequency-based technique ignores implicit features hidden in the reviews. Hao Wang, et al [6] improved the traditional frequency-based product feature extraction (TF-IDF) by using the point mutual information (PMI) to reduce the dimensions of the appropriate features under the conditions specified. Nevertheless, PMI requires a considerable amount of time for calculations.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for extracting product feature from e-commerce comment

Kaewphet

Wisitpongpun

2021

IJEECS

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<span>Reviews of e-commerce play an important role in online purchasing decisions. Consumers are likely to read reviews and comments on products from other consumers. In addition to those opinions that reflect consumers' trust in products, it also provides each product's distinctive properties. Today, there are many online reviews, resulting in enormous comments and suggestions. However, as fully reading reviews is quite difficult, this article presents 3 algorithms for automatic extraction of product features hidden in e-commerce reviews: a traditional frequency-based product feature extraction (F-PFE), syntax analyzer system (SAS), and the hybrid approach called the frequency and syntax-based product feature extraction (FaS-PFE). The proposed algorithms were tested against 4 different types of products: shampoo, skincare, mobile phone, and tablet, using reviews from amazon.com. Based on the product review used in this study, it was found that the SAS can help improve the performance in terms of precision by 15% when compared with the traditional F-PEE approach. When considering both the word frequency and syntax, FaS-PFE clearly outperforms the other two approaches with 94.00% precision and 95.13% recall.</span>

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Research on Feature Mining Algorithm Based on Product Reviews

Cited by 2 publications

References 2 publications

Dimensionality Reduction for Classification of Filipino Text Documents based on Improved BayesianVectorization Technique

Dimensionality Reduction for Classification of Filipino Text Documents based on Improved BayesianVectorization Technique

Algorithm for extracting product feature from e-commerce comment

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