Feature Selection Technique-Based Approach for Suggestion Mining

Ramesh, A.; Reddy, Kumbala Pradeep; Sreenivas, M.; Para, Upendar

doi:10.1007/978-981-16-6616-2_53

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…They used both traditional machine learning approaches (feature engineering-based models) and deep learning models in the Chinese reviews. Ramesh et al [27] proposed the feature selection-based approach for suggestion mining. The method involves feature selection algorithms such as chi-square and multivariate relative discrimination criterion (MRDC), as well as learner models like support vector machine (SVM) and random forest (RF) as classifiers.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Hadizadeh,

Nazari,

Mansoorizadeh

2024

JWE

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In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F1 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.

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Section: Related Workmentioning

confidence: 99%

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Hadizadeh,

Nazari,

Mansoorizadeh

2024

JWE

View full text Add to dashboard Cite

show abstract

“…They used both traditional machine learning approaches (feature engineering-based models) and deep learning models in the Chinese review. Ramesh et al [27] proposed the feature selection-based approach for suggestion mining. Their method is performed with three feature selection algorithms including CHI2, DFD, and MRDC, and SVM and RF as the learning classifiers.…”

Section: Related Workmentioning

confidence: 99%

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

Hadizadeh,

Nazari,

Mansoorizadeh

2023

Preprint

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After the outbreak of COVID-19, the use of the web platform as a means of communication between users and decision-making in online shopping has been accepted in e-commerce. Comments of users express substantially positive and negative emotions about a particular item, which offers suggestions for the improvement of products and organizations. Therefore, mining opinions to extract suggestions can increase the utility of companies and organizations. A good deal of research on suggestion mining employs rule-based approaches and statistical classifiers using manually identified features, while recently, several researchers have considered solutions based on deep learning tools and techniques. The purpose of this study is to use information retrieval techniques for the automatic detection of suggestions. Therefore, distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks with TF-IDF, BOW, and Word2Vec vectors, and keyword extraction were adopted. The proposed approach is presented on the SemEval2019 dataset for extracting suggestions from the text of online user reviews. The results show that the F1Score has improved, as compared to the previous work, and has reached 0.87. The experiments indicate that information retrieval-based approaches tend to be promising for this task.

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Ensemble Approach for Suggestion Mining Using Deep Recurrent Convolutional Networks

Khan,

Akhtar,

Sana

2023

Proceedings of Data Analytics and Management

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Feature Selection Technique-Based Approach for Suggestion Mining

Cited by 3 publications

References 10 publications

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

Ensemble Approach for Suggestion Mining Using Deep Recurrent Convolutional Networks

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