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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| Published in: | Concurrency and computation Vol. 34; no. 3 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Hoboken, USA
John Wiley & Sons, Inc
01.02.2022
Wiley Subscription Services, Inc |
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| ISSN: | 1532-0626, 1532-0634 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Monadjemi, S. Amirhassan Asgarnezhad, Razieh Aghaei, Mohammadreza Soltan |
| Author_xml | – sequence: 1 givenname: Razieh orcidid: 0000-0002-2563-1007 surname: Asgarnezhad fullname: Asgarnezhad, Razieh organization: Islamic Azad University – sequence: 2 givenname: S. Amirhassan orcidid: 0000-0002-8094-2449 surname: Monadjemi fullname: Monadjemi, S. Amirhassan email: sleam@nus.edu.sg, monadjemi@eng.ui.ac.ir organization: University of Isfahan – sequence: 3 givenname: Mohammadreza Soltan orcidid: 0000-0002-2930-9066 surname: Aghaei fullname: Aghaei, Mohammadreza Soltan organization: Islamic Azad University |
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Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special... Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic... |
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| SubjectTerms | 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 |
| Title | A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification |
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