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Review Article Open Access December 21, 2016

Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media

Srinivas Chippagiri 1, Savan Kumar 1 and Olivia R Liu Sheng 1
1
Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, 84112, USA
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2021)
Page(s): 11-20
DOI: 10.31586/jaibd.2016.1293
Received
July 26, 2016
Revised
October 19, 2016
Accepted
November 12, 2016
Published
December 21, 2016
Keywords
Social Media; Sentiment Analysis; Natural Language Processing (NLP); Twitter Data; Machine Learning; Text Classification
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2021. Published by Scientific Publications
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Cite This Article

APA Style
Chippagiri, S. , Kumar, S. , & Sheng, O. R. L. (2021). Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media. Current Research in Public Health, 1(1), 11-20. https://doi.org/10.31586/jaibd.2016.1293
ACS Style
Chippagiri, S. ; Kumar, S. ; Sheng, O. R. L. Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media. Current Research in Public Health 2021 1(1), 11-20. https://doi.org/10.31586/jaibd.2016.1293
Chicago/Turabian Style
Chippagiri, Srinivas, Savan Kumar, and Olivia R Liu Sheng. 2021. "Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media". Current Research in Public Health 1, no. 1: 11-20. https://doi.org/10.31586/jaibd.2016.1293
AMA Style
Chippagiri S, Kumar S, Sheng ORL. Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media. Current Research in Public Health. 2021; 1(1):11-20. https://doi.org/10.31586/jaibd.2016.1293 
@Article{crph1293,
AUTHOR = {Chippagiri, Srinivas and Kumar, Savan and Sheng, Olivia R Liu},
TITLE = {Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {11-20},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1293},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2016.1293},
ABSTRACT = {The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.},
}
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%A Chippagiri, Srinivas
%A Kumar, Savan
%A Sheng, Olivia R Liu
%D 2021
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%T Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media
%M doi:10.31586/jaibd.2016.1293
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1293

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TI  - Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media
T2  - Current Research in Public Health
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AB  - The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.
DO  - Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media
TI  - 10.31586/jaibd.2016.1293
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