Automatic Classification of Mobile Apps Reviews for Requirement Engineering: Exploring The Customer’s Need from The Healthcare Mobile Applications - Couverture souple
Synopsis
We applied machine learning techniques in classifying health care application reviews into several types such as bug reports, new feature requests, application performance, and accuracy and user interface. There is no available free annotated data set for training and evaluating machine learning techniques, therefore, more than 7500 reviews for 10 different health-related mobile applications are annotated manually by experts in the field. Our experiments show that Multi-nominal NaiveBays can classify mobile apps reviews into bugs, new features and sentimental with an accuracy of 87%, and into a general bug, usability, security, and performance with an accuracy of 88%. The best result of the sentimental analysis system is 90%. in addition, the experiments show that the overall performance is improved when we use the data subset with high confidence labels and when two experts agree on the same label. The Re-sampling technique was successfully used to overcome the data imbalance problem in our data set, the accuracy improved to 89% for mobile apps reviews into a set of classes; bugs, security, new feature, performance, and usability and 96% for the sentimental reviews.
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Automatic Classification of Mobile Apps Reviews for Requirement Engineering
Edité par
LAP LAMBERT Academic Publishing Jan 2020, 2020
ISBN 10 : 6200500878
ISBN 13 : 9786200500878
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We applied machine learning techniques in classifying health care application reviews into several types such as bug reports, new feature requests, application performance, and accuracy and user interface. There is no available free annotated data set for training and evaluating machine learning techniques, therefore, more than 7500 reviews for 10 different health-related mobile applications are annotated manually by experts in the field. Our experiments show that Multi-nominal NaiveBays can classify mobile apps reviews into bugs, new features and sentimental with an accuracy of 87%, and into a general bug, usability, security, and performance with an accuracy of 88%. The best result of the sentimental analysis system is 90%. in addition, the experiments show that the overall performance is improved when we use the data subset with high confidence labels and when two experts agree on the same label. The Re-sampling technique was successfully used to overcome the data imbalance problem in our data set, the accuracy improved to 89% for mobile apps reviews into a set of classes; bugs, security, new feature, performance, and usability and 96% for the sentimental reviews. 100 pp. Englisch. N° de réf. du vendeur 9786200500878
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Automatic Classification of Mobile Apps Reviews for Requirement Engineering
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6200500878
ISBN 13 : 9786200500878
Neuf
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Alkilani NadeemNadeem Alkilani is an IT professional, he got a master s degree in software engineering at Birzeit University in Palestine, currently Nadeem works at Paltel, telecom company in Palestine.This work is under the supervis. N° de réf. du vendeur 349535464
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Automatic Classification of Mobile Apps Reviews for Requirement Engineering
Edité par
LAP LAMBERT Academic Publishing Jan 2020, 2020
ISBN 10 : 6200500878
ISBN 13 : 9786200500878
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -We applied machine learning techniques in classifying health care application reviews into several types such as bug reports, new feature requests, application performance, and accuracy and user interface. There is no available free annotated data set for training and evaluating machine learning techniques, therefore, more than 7500 reviews for 10 different health-related mobile applications are annotated manually by experts in the field. Our experiments show that Multi-nominal NaiveBays can classify mobile apps reviews into bugs, new features and sentimental with an accuracy of 87%, and into a general bug, usability, security, and performance with an accuracy of 88%. The best result of the sentimental analysis system is 90%. in addition, the experiments show that the overall performance is improved when we use the data subset with high confidence labels and when two experts agree on the same label. The Re-sampling technique was successfully used to overcome the data imbalance problem in our data set, the accuracy improved to 89% for mobile apps reviews into a set of classes; bugs, security, new feature, performance, and usability and 96% for the sentimental reviews.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. N° de réf. du vendeur 9786200500878
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Automatic Classification of Mobile Apps Reviews for Requirement Engineering : Exploring The Customer¿s Need from The Healthcare Mobile Applications
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6200500878
ISBN 13 : 9786200500878
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - We applied machine learning techniques in classifying health care application reviews into several types such as bug reports, new feature requests, application performance, and accuracy and user interface. There is no available free annotated data set for training and evaluating machine learning techniques, therefore, more than 7500 reviews for 10 different health-related mobile applications are annotated manually by experts in the field. Our experiments show that Multi-nominal NaiveBays can classify mobile apps reviews into bugs, new features and sentimental with an accuracy of 87%, and into a general bug, usability, security, and performance with an accuracy of 88%. The best result of the sentimental analysis system is 90%. in addition, the experiments show that the overall performance is improved when we use the data subset with high confidence labels and when two experts agree on the same label. The Re-sampling technique was successfully used to overcome the data imbalance problem in our data set, the accuracy improved to 89% for mobile apps reviews into a set of classes; bugs, security, new feature, performance, and usability and 96% for the sentimental reviews. N° de réf. du vendeur 9786200500878
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Automatic Classification of Mobile Apps Reviews for Requirement Engineering | Exploring The Customer's Need from The Healthcare Mobile Applications
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6200500878
ISBN 13 : 9786200500878
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. Automatic Classification of Mobile Apps Reviews for Requirement Engineering | Exploring The Customer's Need from The Healthcare Mobile Applications | Nadeem Alkilani (u. a.) | Taschenbuch | 100 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200500878 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 117980307
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Automatic Classification of Mobile Apps Reviews for Requirement Engineering : Exploring The Customer¿s Need from The Healthcare Mobile Applications
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6200500878
ISBN 13 : 9786200500878
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Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | We applied machine learning techniques in classifying health care application reviews into several types such as bug reports, new feature requests, application performance, and accuracy and user interface. There is no available free annotated data set for training and evaluating machine learning techniques, therefore, more than 7500 reviews for 10 different health-related mobile applications are annotated manually by experts in the field. Our experiments show that Multi-nominal NaiveBays can classify mobile apps reviews into bugs, new features and sentimental with an accuracy of 87%, and into a general bug, usability, security, and performance with an accuracy of 88%. The best result of the sentimental analysis system is 90%. in addition, the experiments show that the overall performance is improved when we use the data subset with high confidence labels and when two experts agree on the same label. The Re-sampling technique was successfully used to overcome the data imbalance problem in our data set, the accuracy improved to 89% for mobile apps reviews into a set of classes; bugs, security, new feature, performance, and usability and 96% for the sentimental reviews. N° de réf. du vendeur 36041476/1
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