MARC details
| 000 -LEADER |
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03626ntm 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 |
20251125110722.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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230331t20222022my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009544 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Language of cataloging |
UMP |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .S56 2022 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Md Shofiqul Islam, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
A hybrid deep learning method for multilabel sentiment analysis using attentive capsule and dual structured recurrent network / |
| Statement of responsibility, etc. |
Md Shofiqul Islam |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| 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 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xii, 107 pages |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| 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 (Master of Science) -- 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. |
People express opinions freely in social media platforms such as Facebook, Twitter and Instagram. Sentiment analysis is a machine learning based technique that takes the conversation of social media users about certain products or issues and puts them into context to understand their opinion on that particular topic of interest. Recent developments in machine learning show that they are helpful in sentiment classification particularly to classify human-like concentration words. However, the existing models are typically inadequate when dealing with certain characteristics of sentences. They still have limitations in handling sentences that contain multilabel texts and the sentences that are syntactically complex structured. Due to these two limitations, the accuracy of existing models can still be improved and will be done so when the limitations are properly handled. In order to solve these limitations, a hybrid deep learning method (HACDD) for sentiment analysis to recognize multilabel text emotion is proposed. At first, the data will have to undergo a pre-processing. After preprocessing, the data is embedded using GloVe vector that handles semantic relation among similar words. This proposed model is using a hierarchical attention-based capsule network based on dual and bidirectional recurrent neural networks (RNN). The BiGRU-BiLSTM (dual structured) based framework has been implemented to automatically extract and handle long ranged features. Then, the Dilated Convolutional Neural Network (CNN) layer is implemented to handle specific features with less convolutional operation and less usage of weights. This hybrid model is constructed to handle most relevant and informative feature by dynamically routing its hierarchical system to an attentive capsule. The model is also fitted with a hierarchical attention mechanism to allow full use of the details to effectively acquire semantic knowledge to handle complex sentences. The proposed models were trained and tested on ER (Emotion Recognition) dataset. This dataset was then trained and tested with nine other benchmark models. Our method obtained higher accuracy of 95.46% on ER. The experimental findings reveal that HACDD outperformed all benchmark models and also the best one in terms of training time, accuracy, and handling of complex sentence. |
| 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 |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Restricted Collection |