Cover Image

Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis

Complex event pattern detection has become an emerging research area in various monitoring applications. For learning and predicting the event patterns, dynamic Bayesian network (DBN) with Hidden Markov Model (HMM) and heuristic search learning algorithms have been a popular technique used in which...

Full description

Saved in:
Bibliographic Details
Main Authors: Kabir Ahmad, Farzana, Kamaruddin, Siti Sakira, Yusof, Yuhanis, Yusoff, Nooraini
Format: Monograph
Language:English
Published: UUM 2021
Subjects:
HM Sociology
Online Access:https://repo.uum.edu.my/id/eprint/29643/1/13064.pdf
https://repo.uum.edu.my/id/eprint/29643/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.29643
record_format eprints
spelling my.uum.repo.296432023-07-30T09:08:29Z https://repo.uum.edu.my/id/eprint/29643/ Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis Kabir Ahmad, Farzana Kamaruddin, Siti Sakira Yusof, Yuhanis Yusoff, Nooraini HM Sociology Complex event pattern detection has become an emerging research area in various monitoring applications. For learning and predicting the event patterns, dynamic Bayesian network (DBN) with Hidden Markov Model (HMM) and heuristic search learning algorithms have been a popular technique used in which structure learning is trained to classify complex events pattern. Regardless the success of DBN with HMM in detecting complex event patterns, learning the spatial and temporal features from large-scale events is a challenging task. This is due to the difficulties in learning DBN structure which is NP-hard as number of possible graphs increases super-exponentially with the number of events. Thus, this study proposed a Bat Algorithm (BA) to address the complex learning structure of DBN in detecting sentiment patterns. This algorithm mainly based on the echolocation behavior of bats. Two main characters are highlighted in this learning, which is the velocity and frequency. Bats fly randomly with certain velocity at a fixed frequency to search for prey. They can automatically adjust the frequency of their emitted pulses and modify the rate of pulse emission, depending on the proximity of their target. Such behavior may be suitable to analyze sentiment expression. However, BA needs to be coupled with appropriate learning scheme in determining the tradeoff between global explorative search with local exploitative search. In addressing this matter, the study includes several phases such as designing a DBN learning scheme, determining spatio-temporal sentiment features and creating BA in analyzing large-scale sentiment expressions. The outcome of this research is to detect sentiment polarity patterns (whether the polarity is positive, neutral or negative based on subjectivity lexicons) from spatio-temporal activities on news. The pattern can be used to monitor and analyze emerging issues in the setting of public sector as direct response to the surge of interest. UUM 2021 Monograph NonPeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/29643/1/13064.pdf Kabir Ahmad, Farzana and Kamaruddin, Siti Sakira and Yusof, Yuhanis and Yusoff, Nooraini (2021) Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis. Technical Report. UUM. (Submitted)
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic HM Sociology
spellingShingle HM Sociology
Kabir Ahmad, Farzana
Kamaruddin, Siti Sakira
Yusof, Yuhanis
Yusoff, Nooraini
Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
description Complex event pattern detection has become an emerging research area in various monitoring applications. For learning and predicting the event patterns, dynamic Bayesian network (DBN) with Hidden Markov Model (HMM) and heuristic search learning algorithms have been a popular technique used in which structure learning is trained to classify complex events pattern. Regardless the success of DBN with HMM in detecting complex event patterns, learning the spatial and temporal features from large-scale events is a challenging task. This is due to the difficulties in learning DBN structure which is NP-hard as number of possible graphs increases super-exponentially with the number of events. Thus, this study proposed a Bat Algorithm (BA) to address the complex learning structure of DBN in detecting sentiment patterns. This algorithm mainly based on the echolocation behavior of bats. Two main characters are highlighted in this learning, which is the velocity and frequency. Bats fly randomly with certain velocity at a fixed frequency to search for prey. They can automatically adjust the frequency of their emitted pulses and modify the rate of pulse emission, depending on the proximity of their target. Such behavior may be suitable to analyze sentiment expression. However, BA needs to be coupled with appropriate learning scheme in determining the tradeoff between global explorative search with local exploitative search. In addressing this matter, the study includes several phases such as designing a DBN learning scheme, determining spatio-temporal sentiment features and creating BA in analyzing large-scale sentiment expressions. The outcome of this research is to detect sentiment polarity patterns (whether the polarity is positive, neutral or negative based on subjectivity lexicons) from spatio-temporal activities on news. The pattern can be used to monitor and analyze emerging issues in the setting of public sector as direct response to the surge of interest.
format Monograph
author Kabir Ahmad, Farzana
Kamaruddin, Siti Sakira
Yusof, Yuhanis
Yusoff, Nooraini
author_facet Kabir Ahmad, Farzana
Kamaruddin, Siti Sakira
Yusof, Yuhanis
Yusoff, Nooraini
author_sort Kabir Ahmad, Farzana
title Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
title_short Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
title_full Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
title_fullStr Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
title_full_unstemmed Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
title_sort bat algorithm for complex event pattern detection in sentiment analysis
publisher UUM
publishDate 2021
url https://repo.uum.edu.my/id/eprint/29643/1/13064.pdf
https://repo.uum.edu.my/id/eprint/29643/
_version_ 1773546683628519424
score 13.211869

Similar Items