A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

Summary Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user‐generated content on the Web, this field gained great...

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Veröffentlicht in:Concurrency and computation Jg. 34; H. 3
Hauptverfasser: Asgarnezhad, Razieh, Monadjemi, S. Amirhassan, Aghaei, Mohammadreza Soltan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.02.2022
Wiley Subscription Services, Inc
Schlagworte:
Apriori algorithm
Classification
Data mining
Decomposition
Electronic documents
Error analysis
Evaluation
Evolutionary algorithms
feature selection
Genetic algorithms
multi‐objective evolutionary algorithm
Neural networks
Pattern analysis
Recall
Sentiment analysis
sequence pattern mining
Text categorization
text classification
User generated content
ISSN:1532-0626, 1532-0634
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Zusammenfassung:Summary Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user‐generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time‐consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation‐Power concept, a multi‐objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi‐objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error‐index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f‐measure.
Bibliographie:ObjectType-Article-1
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6594