Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models

Kiplagat Wilfred Kiprono; Elisha Odira Abade

doi:doi:10.11648/j.ijdst.20160204.11

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Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models

Kiplagat Wilfred Kiprono, Elisha Odira Abade

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

Received: 12 June 2016 Accepted: 23 June 2016 Published: 1 August 2016

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Abstract

The transition from web 1.0 to web 2.0 has enabled direct interaction between users and its various resources and services such as social media networks. In this research paper we have analyzed algorithms for sentiment analysis which can be used to utilize this huge information. The goals of this paper is to device a way of obtaining social network opinions and extracting features from unstructured text and assign for each feature its associated sentiment in a clear and efficient way. In this project we have applied naïve bayes, support vector machines and maximum entropy for analysis and produced an analytical report of the three qualitatively and quantitatively. We performed the project empirically and analyzed the resulting data using an excel tool so as to obtain comparative analysis of the three algorithms for classification.

Published in	International Journal on Data Science and Technology (Volume 2, Issue 4)
DOI	10.11648/j.ijdst.20160204.11
Page(s)	41-45
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), 2016. Published by Science Publishing Group

Keywords

Pos, Svm, Maxent, Naive Bayes, Feature Selection, Sentiment Classification, N-grams, Bigrams, Unigrams, Trigrams

References

[1]	Li Yung-Ming, Li Tsung-Ying. Deriving market intelligence from microblogs. Decis Support Syst 2013.
[2]	Caro Luigi Di, Grella Matteo. Sentiment analysis via dependency parsing. Comput Stand Interfaces 2012.
[3]	Liu B. Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 2012.
[4]	Pang B, Lee L. Opinion mining and sentiment analysis. Found Trends Inform Retriev 2008; 2: 1–135.
[5]	Mohammad SM. From once upon a time to happily ever after: tracking emotions in mail and books. Decis Support Syst 2012 s; 53: 730–41.
[6]	Fully Automatic Lexicon Expansion for Domain- oriented Sentiment Analysis by Hiroshi Kanayama Tetsuya Nasukawa, Tokyo Research Laboratory, IBM Japan, Ltd. 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken, 242- 8502 Japan {hkananasukawa}@jp.ibm.com
[7]	Text normalization in social media: progress, problems and applications for a pre-processing system of casual English - Eleanor Clarka* and Kenji Arakia Pre-processing very noisy text - Alexander Clark, ISSCO / TIM, University of Geneva, UNI-MAIL, Boulevard du Pont-d’Arve, CH-1211 Geneva 4, Switzerland.
[8]	M. Almashraee, D. M. Diaz, and R. Unland, “Sentiment classification of online products based on machine learning techniques and multi-agent systems technologies,” in Industrial Conference on Data Mining - Workshops, 2012.
[9]	Mugenda, M., Mugenda, G. (1999). Research Methods. Quantitative and Qualitative Approaches. Nairobi, Kenya.
[10]	Pauls, Adam, and Dan Klein. "Faster and smaller n-gram language models." Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies. Vol. 1. 2011.
[11]	Socher, Richard, et al. "Semi-supervised recursive auto encoders for predicting sentiment distributions." Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011.
[12]	Kennedy, Alistair, and Diana Inkpen. "Sentiment classification of movie reviews using contextual valence shifters." Computational Intelligence 22.2 (2006): 110-125.

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APA Style

Kiplagat Wilfred Kiprono, Elisha Odira Abade. (2016). Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. International Journal on Data Science and Technology, 2(4), 41-45. https://doi.org/10.11648/j.ijdst.20160204.11

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ACS Style

Kiplagat Wilfred Kiprono; Elisha Odira Abade. Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. Int. J. Data Sci. Technol. 2016, 2(4), 41-45. doi: 10.11648/j.ijdst.20160204.11

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AMA Style

Kiplagat Wilfred Kiprono, Elisha Odira Abade. Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. Int J Data Sci Technol. 2016;2(4):41-45. doi: 10.11648/j.ijdst.20160204.11

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@article{10.11648/j.ijdst.20160204.11,
  author = {Kiplagat Wilfred Kiprono and Elisha Odira Abade},
  title = {Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models},
  journal = {International Journal on Data Science and Technology},
  volume = {2},
  number = {4},
  pages = {41-45},
  doi = {10.11648/j.ijdst.20160204.11},
  url = {https://doi.org/10.11648/j.ijdst.20160204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20160204.11},
  abstract = {The transition from web 1.0 to web 2.0 has enabled direct interaction between users and its various resources and services such as social media networks. In this research paper we have analyzed algorithms for sentiment analysis which can be used to utilize this huge information. The goals of this paper is to device a way of obtaining social network opinions and extracting features from unstructured text and assign for each feature its associated sentiment in a clear and efficient way. In this project we have applied naïve bayes, support vector machines and maximum entropy for analysis and produced an analytical report of the three qualitatively and quantitatively. We performed the project empirically and analyzed the resulting data using an excel tool so as to obtain comparative analysis of the three algorithms for classification.},
 year = {2016}
}

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TY  - JOUR
T1  - Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models
AU  - Kiplagat Wilfred Kiprono
AU  - Elisha Odira Abade
Y1  - 2016/08/01
PY  - 2016
N1  - https://doi.org/10.11648/j.ijdst.20160204.11
DO  - 10.11648/j.ijdst.20160204.11
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  - 41
EP  - 45
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20160204.11
AB  - The transition from web 1.0 to web 2.0 has enabled direct interaction between users and its various resources and services such as social media networks. In this research paper we have analyzed algorithms for sentiment analysis which can be used to utilize this huge information. The goals of this paper is to device a way of obtaining social network opinions and extracting features from unstructured text and assign for each feature its associated sentiment in a clear and efficient way. In this project we have applied naïve bayes, support vector machines and maximum entropy for analysis and produced an analytical report of the three qualitatively and quantitatively. We performed the project empirically and analyzed the resulting data using an excel tool so as to obtain comparative analysis of the three algorithms for classification.
VL  - 2
IS  - 4
ER  -

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Author Information

Kiplagat Wilfred Kiprono

School of computing and informatics, University of Nairobi, Nairobi, Kenya
Elisha Odira Abade

School of computing and informatics, University of Nairobi, Nairobi, Kenya

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APA Style

Kiplagat Wilfred Kiprono, Elisha Odira Abade. (2016). Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. International Journal on Data Science and Technology, 2(4), 41-45. https://doi.org/10.11648/j.ijdst.20160204.11

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ACS Style

Kiplagat Wilfred Kiprono; Elisha Odira Abade. Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. Int. J. Data Sci. Technol. 2016, 2(4), 41-45. doi: 10.11648/j.ijdst.20160204.11

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AMA Style

Kiplagat Wilfred Kiprono, Elisha Odira Abade. Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. Int J Data Sci Technol. 2016;2(4):41-45. doi: 10.11648/j.ijdst.20160204.11

Copy | Download

@article{10.11648/j.ijdst.20160204.11,
  author = {Kiplagat Wilfred Kiprono and Elisha Odira Abade},
  title = {Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models},
  journal = {International Journal on Data Science and Technology},
  volume = {2},
  number = {4},
  pages = {41-45},
  doi = {10.11648/j.ijdst.20160204.11},
  url = {https://doi.org/10.11648/j.ijdst.20160204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20160204.11},
  abstract = {The transition from web 1.0 to web 2.0 has enabled direct interaction between users and its various resources and services such as social media networks. In this research paper we have analyzed algorithms for sentiment analysis which can be used to utilize this huge information. The goals of this paper is to device a way of obtaining social network opinions and extracting features from unstructured text and assign for each feature its associated sentiment in a clear and efficient way. In this project we have applied naïve bayes, support vector machines and maximum entropy for analysis and produced an analytical report of the three qualitatively and quantitatively. We performed the project empirically and analyzed the resulting data using an excel tool so as to obtain comparative analysis of the three algorithms for classification.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models
AU  - Kiplagat Wilfred Kiprono
AU  - Elisha Odira Abade
Y1  - 2016/08/01
PY  - 2016
N1  - https://doi.org/10.11648/j.ijdst.20160204.11
DO  - 10.11648/j.ijdst.20160204.11
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  - 41
EP  - 45
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20160204.11
AB  - The transition from web 1.0 to web 2.0 has enabled direct interaction between users and its various resources and services such as social media networks. In this research paper we have analyzed algorithms for sentiment analysis which can be used to utilize this huge information. The goals of this paper is to device a way of obtaining social network opinions and extracting features from unstructured text and assign for each feature its associated sentiment in a clear and efficient way. In this project we have applied naïve bayes, support vector machines and maximum entropy for analysis and produced an analytical report of the three qualitatively and quantitatively. We performed the project empirically and analyzed the resulting data using an excel tool so as to obtain comparative analysis of the three algorithms for classification.
VL  - 2
IS  - 4
ER  -

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