Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi

Micro-blogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. It can pose difficulties for standard machine learning document representations because of the short length coupled with the...

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Main Author:	Che Alhadi, Nazirah
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Elementary mathematics Arithmetic Online data processing
Online Access:	https://ir.uitm.edu.my/id/eprint/35377/1/35377.pdf
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id	my-uitm-ir.35377
record_format	uketd_dc
spelling	my-uitm-ir.353772020-10-20T07:10:22Z Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi 2012 Che Alhadi, Nazirah Elementary mathematics. Arithmetic Online data processing Evolutionary programming (Computer science). Genetic algorithms Micro-blogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. It can pose difficulties for standard machine learning document representations because of the short length coupled with their noisy nature. The aim of this project is to classify Twitter’s messages into sentiment categories based on the important keywords. This project methodology consists of five phases which are preliminary study, data collection and preparation, model development, model evaluation and documentation. This project is designed using negative selection algorithm to automatically classify the Twitter’s messages into its sentiment’s category based on important keyword recognition. In order to develop this model classification and prototype, 480 Twitter’s messages were used as training data and 120 Twitter’s messages for testing data to determine the accuracy of the classification model. The accuracy of this model is about 60 percent. Second experiment was carried out by reducing the data to 240 for training data and 60 data for testing. The accuracy for second experiment is improved to 63.33 percent. 2012 Thesis https://ir.uitm.edu.my/id/eprint/35377/ https://ir.uitm.edu.my/id/eprint/35377/1/35377.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer & Mathematical Sciences Jantan, Dr Hamidah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jantan, Dr Hamidah
topic	Elementary mathematics Arithmetic Online data processing Elementary mathematics Arithmetic
spellingShingle	Elementary mathematics Arithmetic Online data processing Elementary mathematics Arithmetic Che Alhadi, Nazirah Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
description	Micro-blogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. It can pose difficulties for standard machine learning document representations because of the short length coupled with their noisy nature. The aim of this project is to classify Twitter’s messages into sentiment categories based on the important keywords. This project methodology consists of five phases which are preliminary study, data collection and preparation, model development, model evaluation and documentation. This project is designed using negative selection algorithm to automatically classify the Twitter’s messages into its sentiment’s category based on important keyword recognition. In order to develop this model classification and prototype, 480 Twitter’s messages were used as training data and 120 Twitter’s messages for testing data to determine the accuracy of the classification model. The accuracy of this model is about 60 percent. Second experiment was carried out by reducing the data to 240 for training data and 60 data for testing. The accuracy for second experiment is improved to 63.33 percent.
format	Thesis
qualification_level	Bachelor degree
author	Che Alhadi, Nazirah
author_facet	Che Alhadi, Nazirah
author_sort	Che Alhadi, Nazirah
title	Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
title_short	Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
title_full	Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
title_fullStr	Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
title_full_unstemmed	Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
title_sort	sentiment analysis using negative selection algorithm for twitter’s messages / nazirah che alhadi
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer & Mathematical Sciences
publishDate	2012
url	https://ir.uitm.edu.my/id/eprint/35377/1/35377.pdf
_version_	1783734301686235136

Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi

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