Sentiment Analysis Using Text Mining: A Review

Swati Redhu; Sangeet Srivastava; Barkha Bansal; Gaurav Gupta

doi:doi:10.11648/j.ijdst.20180402.12

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Sentiment Analysis Using Text Mining: A Review

Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta

Published in International Journal on Data Science and Technology (Volume 4, Issue 2)

Received: 25 June 2018 Published: 26 June 2018

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Abstract

Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.

Published in	International Journal on Data Science and Technology (Volume 4, Issue 2)
DOI	10.11648/j.ijdst.20180402.12
Page(s)	49-53
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), 2018. Published by Science Publishing Group

Keywords

Sentiment Analysis, Supervised Learning, Unsupervised Learning, Text Mining, Feature Extraction, Feature Representation

References

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APA Style

Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta. (2018). Sentiment Analysis Using Text Mining: A Review. International Journal on Data Science and Technology, 4(2), 49-53. https://doi.org/10.11648/j.ijdst.20180402.12

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ACS Style

Swati Redhu; Sangeet Srivastava; Barkha Bansal; Gaurav Gupta. Sentiment Analysis Using Text Mining: A Review. Int. J. Data Sci. Technol. 2018, 4(2), 49-53. doi: 10.11648/j.ijdst.20180402.12

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AMA Style

Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta. Sentiment Analysis Using Text Mining: A Review. Int J Data Sci Technol. 2018;4(2):49-53. doi: 10.11648/j.ijdst.20180402.12

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@article{10.11648/j.ijdst.20180402.12,
  author = {Swati Redhu and Sangeet Srivastava and Barkha Bansal and Gaurav Gupta},
  title = {Sentiment Analysis Using Text Mining: A Review},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {2},
  pages = {49-53},
  doi = {10.11648/j.ijdst.20180402.12},
  url = {https://doi.org/10.11648/j.ijdst.20180402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.12},
  abstract = {Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.},
 year = {2018}
}

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TY  - JOUR
T1  - Sentiment Analysis Using Text Mining: A Review
AU  - Swati Redhu
AU  - Sangeet Srivastava
AU  - Barkha Bansal
AU  - Gaurav Gupta
Y1  - 2018/06/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180402.12
DO  - 10.11648/j.ijdst.20180402.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 49
EP  - 53
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180402.12
AB  - Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.
VL  - 4
IS  - 2
ER  -

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Author Information

Swati Redhu

Department of Applied Sciences, The NorthCap University, Gurgaon, India
Sangeet Srivastava

Department of Applied Sciences, The NorthCap University, Gurgaon, India
Barkha Bansal

Department of Applied Sciences, The NorthCap University, Gurgaon, India
Gaurav Gupta

School of Mathematical Sciences, College of Natural, Applied and Health Sciences, Wenzhou-Kean University, Wenzhou, China

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Plain Text BibTeX RIS

APA Style

Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta. (2018). Sentiment Analysis Using Text Mining: A Review. International Journal on Data Science and Technology, 4(2), 49-53. https://doi.org/10.11648/j.ijdst.20180402.12

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ACS Style

Swati Redhu; Sangeet Srivastava; Barkha Bansal; Gaurav Gupta. Sentiment Analysis Using Text Mining: A Review. Int. J. Data Sci. Technol. 2018, 4(2), 49-53. doi: 10.11648/j.ijdst.20180402.12

Copy | Download

AMA Style

Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta. Sentiment Analysis Using Text Mining: A Review. Int J Data Sci Technol. 2018;4(2):49-53. doi: 10.11648/j.ijdst.20180402.12

Copy | Download

@article{10.11648/j.ijdst.20180402.12,
  author = {Swati Redhu and Sangeet Srivastava and Barkha Bansal and Gaurav Gupta},
  title = {Sentiment Analysis Using Text Mining: A Review},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {2},
  pages = {49-53},
  doi = {10.11648/j.ijdst.20180402.12},
  url = {https://doi.org/10.11648/j.ijdst.20180402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.12},
  abstract = {Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Sentiment Analysis Using Text Mining: A Review
AU  - Swati Redhu
AU  - Sangeet Srivastava
AU  - Barkha Bansal
AU  - Gaurav Gupta
Y1  - 2018/06/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180402.12
DO  - 10.11648/j.ijdst.20180402.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 49
EP  - 53
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180402.12
AB  - Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.
VL  - 4
IS  - 2
ER  -

Copy | Download