MARC details
| 000 -LEADER |
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| fixed length control field |
03361ntm a2200373 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110724.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230404t20222022my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009557 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .S93 2022 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Nur Syafiqah Mohd Nafis, |
| Relator term |
author. |
| 245 14 - TITLE STATEMENT |
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| Title |
The hybrid feature selection technique using term frequency-inverse document frequency and support vector machine-recursive feature elimination for sentiment classification / |
| Statement of responsibility, etc. |
Nur Syafiqah Binti Mohd Nafis |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 170 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2022 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Sentiment classification is increasingly used to automatically identify a positive or negative sentiment in the opinionated text document, for instance, customer feedback or review. Feature selection has always been a critical and challenging problem in machine learning-based sentiment classification. Hybrid feature selection is an efficient technique in sentiment classification. However, there are several disadvantages that can be solved. Firstly, the ability to identify feature importance and reduce some features from opinionated text documents. The failure to address this issue will result in poor classification performance. Therefore, this research aims to improve the classification performances by proposing term frequency-inverse document frequency (TF-IDF) and support vector machine-recursive feature elimination (SVM-RFE) as a hybrid feature selection technique. The TF-IDF evaluates the feature importance, and the standard deviation-based threshold is used for feature reduction. The objective is to improve the conventional approach of reducing features from feature matrix. Later, the SVM-RFE re-evaluates and ranks the remaining features from TF-IDF-based feature matrix. Only the k-top features group from the SVM-RFE ranked features were used for sentiment classification. Finally, the support vector machine (SVM) classifier is employed to classify the English customer review datasets, i.e., opinion-labelled, and large IMDb. The performance was measured using accuracy, precision, recall, F-measure, and feature size reduction. The experimental results present promising performances up to 95.06% in the performance measurements, especially from the large IMDb datasets and additional dataset, hotel review. Consequently, the proposed technique could minimise 31.80% to 64.00% of the features during classification. This reduction rate is significant in optimally utilising the computational resources while preserving the efficiency of the classification performance |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |