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Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat

Twitter’s is a microblogging social networking website that has a large and rapidly growing user base. Thus, the website provides a rich bank of data in the form of “tweets”, which are short status update from Twitter’s user that must be written in 140 characters or less. As an increasingly popular...

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Bibliographic Details
Main Author: Mamat, Fatimah
Format: Thesis
Language:English
Published: 2012
Subjects:
Fuzzy arithmetic
Evolutionary programming (Computer science). Genetic algorithms
Creative ability in technology
Information technology. Information systems
Online Access:http://ir.uitm.edu.my/id/eprint/35379/1/35379.pdf
http://ir.uitm.edu.my/id/eprint/35379/
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record_format eprints
spelling my.uitm.ir.353792020-10-20T07:05:58Z http://ir.uitm.edu.my/id/eprint/35379/ Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat Mamat, Fatimah Fuzzy arithmetic Evolutionary programming (Computer science). Genetic algorithms Creative ability in technology Information technology. Information systems Twitter’s is a microblogging social networking website that has a large and rapidly growing user base. Thus, the website provides a rich bank of data in the form of “tweets”, which are short status update from Twitter’s user that must be written in 140 characters or less. As an increasingly popular platform for conveying opinions and thoughts, it seems natural to mine Twitter for potentially interesting trends regarding prominent topics in the news or popular culture. The sentiment analysis using clonal selection algorithm for twitter’s data system was developed to achieve the main objective which is to classify the twitter’s messages according three sentiments which are positive, negative and neutral. Clonal selection algorithm was used in this project because there are no researcher are focus on that technique for classify twitter’s data. This project can be used for marketing area because of the data was about review on I- phone. Nevertheless, it’s only accepts English standard word. In order to achieve the main objective, five phases of methodology was been implemented which are preliminary study, data preparation, model development, model evaluation & prototype development and last but not list is documentation. The evaluation conducted in this project has shown by accuracy is testing process. It used to check whether the data have been classifier correctly or incorrectly. Two experiments were carried out with different amount of data. At the first experiment, 200 data was used and the accuracy was 60 percent, while decrease data into 125 during experiment two, the accuracy was 56 percent only. 2012 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/35379/1/35379.pdf Mamat, Fatimah (2012) Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat. Degree thesis, Universiti Teknologi MARA, Terengganu.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Fuzzy arithmetic
Evolutionary programming (Computer science). Genetic algorithms
Creative ability in technology
Information technology. Information systems
spellingShingle Fuzzy arithmetic
Evolutionary programming (Computer science). Genetic algorithms
Creative ability in technology
Information technology. Information systems
Mamat, Fatimah
Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
description Twitter’s is a microblogging social networking website that has a large and rapidly growing user base. Thus, the website provides a rich bank of data in the form of “tweets”, which are short status update from Twitter’s user that must be written in 140 characters or less. As an increasingly popular platform for conveying opinions and thoughts, it seems natural to mine Twitter for potentially interesting trends regarding prominent topics in the news or popular culture. The sentiment analysis using clonal selection algorithm for twitter’s data system was developed to achieve the main objective which is to classify the twitter’s messages according three sentiments which are positive, negative and neutral. Clonal selection algorithm was used in this project because there are no researcher are focus on that technique for classify twitter’s data. This project can be used for marketing area because of the data was about review on I- phone. Nevertheless, it’s only accepts English standard word. In order to achieve the main objective, five phases of methodology was been implemented which are preliminary study, data preparation, model development, model evaluation & prototype development and last but not list is documentation. The evaluation conducted in this project has shown by accuracy is testing process. It used to check whether the data have been classifier correctly or incorrectly. Two experiments were carried out with different amount of data. At the first experiment, 200 data was used and the accuracy was 60 percent, while decrease data into 125 during experiment two, the accuracy was 56 percent only.
format Thesis
author Mamat, Fatimah
author_facet Mamat, Fatimah
author_sort Mamat, Fatimah
title Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
title_short Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
title_full Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
title_fullStr Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
title_full_unstemmed Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
title_sort sentiment analysis using clonal selection algorithm for twitter’s data / fatimah mamat
publishDate 2012
url http://ir.uitm.edu.my/id/eprint/35379/1/35379.pdf
http://ir.uitm.edu.my/id/eprint/35379/
_version_ 1685651297231962112
score 13.211869

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