A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators
The increase in the amount of content shared on social media makes it difficult to extract meaningful information from scientific studies. Accordingly in recent years researchers have been working extensively on sentiment analysis studies for the automatic evaluation of social media data. One of the...
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ftkadirhasuniv:oai:academicrepository.khas.edu.tr:20.500.12469/1784 2023-05-15T17:33:20+02:00 A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators Yelmen, Ilkay Zontul, Metin Kaynar, Oǧuz Sönmez, Ferdi Yelmen, Ilkay 2018 https://hdl.handle.net/20.500.12469/1784 English eng North Atlantic University Union International Journal of Circuits, Systems and Signal Processing 19984464 https://hdl.handle.net/20.500.12469/1784 637 645 12 Classification algorithms Feature extraction Genetic algorithms Sentiment analysis Text mining Article 2018 ftkadirhasuniv https://doi.org/20.500.12469/1784 2021-09-21T08:47:24Z The increase in the amount of content shared on social media makes it difficult to extract meaningful information from scientific studies. Accordingly in recent years researchers have been working extensively on sentiment analysis studies for the automatic evaluation of social media data. One of the focuses of these studies is sentiment analysis on tweets. The more tweets are available the more features in terms of words exist. This leads to the curse of dimensionality and sparsity resulting in a decrease in the success of the classification. In this study Gini Index Information Gain and Genetic Algorithm (GA) are used for feature selection and Support Vector Machines (SVMs) Artificial Neural Networks (ANN) and Centroid Based classification algorithms are used for the classification of Turkish tweets obtained from 3 different GSM operators. The feature selection methods are combined with the classification methods to investigate the effect on the success rate of analysis. Especially when the SVMs are used with the GA as a hybrid 96.8% success has been achieved for the classification of the tweets as positive or negative. © 2018 North Atlantic University Union. All rights reserved. Article in Journal/Newspaper North Atlantic Kadir Has University Academic Repository |
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Open Polar |
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Kadir Has University Academic Repository |
op_collection_id |
ftkadirhasuniv |
language |
English |
topic |
Classification algorithms Feature extraction Genetic algorithms Sentiment analysis Text mining |
spellingShingle |
Classification algorithms Feature extraction Genetic algorithms Sentiment analysis Text mining Yelmen, Ilkay Zontul, Metin Kaynar, Oǧuz Sönmez, Ferdi A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators |
topic_facet |
Classification algorithms Feature extraction Genetic algorithms Sentiment analysis Text mining |
description |
The increase in the amount of content shared on social media makes it difficult to extract meaningful information from scientific studies. Accordingly in recent years researchers have been working extensively on sentiment analysis studies for the automatic evaluation of social media data. One of the focuses of these studies is sentiment analysis on tweets. The more tweets are available the more features in terms of words exist. This leads to the curse of dimensionality and sparsity resulting in a decrease in the success of the classification. In this study Gini Index Information Gain and Genetic Algorithm (GA) are used for feature selection and Support Vector Machines (SVMs) Artificial Neural Networks (ANN) and Centroid Based classification algorithms are used for the classification of Turkish tweets obtained from 3 different GSM operators. The feature selection methods are combined with the classification methods to investigate the effect on the success rate of analysis. Especially when the SVMs are used with the GA as a hybrid 96.8% success has been achieved for the classification of the tweets as positive or negative. © 2018 North Atlantic University Union. All rights reserved. |
author2 |
Yelmen, Ilkay |
format |
Article in Journal/Newspaper |
author |
Yelmen, Ilkay Zontul, Metin Kaynar, Oǧuz Sönmez, Ferdi |
author_facet |
Yelmen, Ilkay Zontul, Metin Kaynar, Oǧuz Sönmez, Ferdi |
author_sort |
Yelmen, Ilkay |
title |
A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators |
title_short |
A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators |
title_full |
A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators |
title_fullStr |
A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators |
title_full_unstemmed |
A novel hybrid approach for sentiment classification of TURKISH tweets for GSM operators |
title_sort |
novel hybrid approach for sentiment classification of turkish tweets for gsm operators |
publisher |
North Atlantic University Union |
publishDate |
2018 |
url |
https://hdl.handle.net/20.500.12469/1784 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
International Journal of Circuits, Systems and Signal Processing 19984464 https://hdl.handle.net/20.500.12469/1784 637 645 12 |
op_doi |
https://doi.org/20.500.12469/1784 |
_version_ |
1766131811600039936 |