| 000 -LEADER |
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| fixed length control field |
02454nam a2200325Ia 4500 |
| 001 - CONTROL NUMBER |
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| control field |
90070 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
FT7751 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251106160543.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240223n 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Description conventions |
rda |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9.A43 M34 2023 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Stephen Kent A. Malagday, Mark Raphael V. Sto. Domingo. |
| 245 #0 - TITLE STATEMENT |
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| Title |
Named entity recognition on E-Mono: An algorithm enhancement applied in sentiment analysis |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Computer Science) Pamantasan ng Lungsod ng Maynila. 2023. |
| 336 ## - CONTENT TYPE |
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| Content type code |
. |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Materials specified |
0 |
| Media type code |
. |
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Materials specified |
0 |
| Carrier type code |
. |
| Carrier type term |
volume |
| Source |
rdacarrier |
| 344 ## - SOUND CHARACTERISTICS |
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| Type of recording |
0 |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| File type |
0 |
| 385 ## - AUDIENCE CHARACTERISTICS |
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| Audience term |
2 |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: Sentiment analysis is a critical component of natural language processing that seeks to classify emotions conveyed in the text. To achieve this goal, various approaches have been developed, and one commonly used method is the Extended Max- Occurrence with Normalized Non-Occurrence (EMONO) term weighting scheme. The EMONO scheme builds upon the Max-Occurrence with a Normalized Non-Occurrence (MONO) approach, which considers the frequencies of term occurrences in sentiment classes. However, the original EMONO approach has a limitation in that it does not consider the importance of named entities and their associated sentiment. Recognizing this gap, the researchers proposed an enhanced approach that integrates the E-MONO term weighting scheme with Name Entity Recognition (NER). By incorporating NER, the researchers aim to enhance the accuracy of sentiment analysis by identifying named entities and analysing the sentiment expressed towards specific entities. The combination of NER and the enhanced EMONO term weighting scheme aims to capture the nuanced sentiment expressed towards named entities, resulting in improved sentiment analysis outcomes. In experimental results, the proposed approach achieved an accuracy rate of 84% in classifying sentiment using E-MONO with the integrated Named Entity Recognition. These findings demonstrate the effectiveness of the combined approach in accurately identifying and analysing sentiment towards named entities, contributing to more precise sentiment analysis results overall. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
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| Terms governing access |
5 |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
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| Terms governing use and reproduction |
5 |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Institution code [OBSOLETE] |
lcc |
| Item type |
Thesis/Dissertation |
| Koha issues (borrowed), all copies |
1 |
| Source of classification or shelving scheme |
|