000 02454nam a2200325Ia 4500
001 90070
003 FT7751
005 20251106160543.0
008 240223n 000 0 eng d
040 _erda
041 _aengtag
050 _aQA76.9.A43 M34 2023
082 _a.
100 _aStephen Kent A. Malagday, Mark Raphael V. Sto. Domingo.
245 0 _aNamed entity recognition on E-Mono: An algorithm enhancement applied in sentiment analysis
264 _a.
_b.
_cc2023
300 _bUndergraduate Thesis: (Bachelor of Science in Computer Science) Pamantasan ng Lungsod ng Maynila. 2023.
336 _b.
_atext
_2rdacontent
337 _30
_b.
_aunmediated
_2rdamedia
338 _30
_b.
_avolume
_2rdacarrier
344 _a0
347 _a0
385 _a2
505 _aABSTRACT: 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 _a5
526 _aF
540 _a5
655 _aacademic writing
942 _alcc
_cMS
_01
_2lcc
999 _c24265
_d24265