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Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey

S. Thulasi Bharathi1 , S. Charles2

Section:Survey Paper, Product Type: Journal Paper
Volume-06 , Issue-11 , Page no. 210-215, Dec-2018

Online published on Dec 31, 2018

Copyright © S. Thulasi Bharathi, S. Charles . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Thulasi Bharathi, S. Charles, “Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.210-215, 2018.

MLA Style Citation: S. Thulasi Bharathi, S. Charles "Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey." International Journal of Computer Sciences and Engineering 06.11 (2018): 210-215.

APA Style Citation: S. Thulasi Bharathi, S. Charles, (2018). Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey. International Journal of Computer Sciences and Engineering, 06(11), 210-215.

BibTex Style Citation:
@article{Bharathi_2018,
author = {S. Thulasi Bharathi, S. Charles},
title = {Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {06},
Issue = {11},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {210-215},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=573},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=573
TI - Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey
T2 - International Journal of Computer Sciences and Engineering
AU - S. Thulasi Bharathi, S. Charles
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 210-215
IS - 11
VL - 06
SN - 2347-2693
ER -

           

Abstract

Sentiment analysis is an application of natural language processing. It is also known as emotion extraction or opinion mining. Sentiment analysis or opinion mining is the computational study of opinions, sentiments and emotions. Opinions are usually particular expressions that designate people’s sentiments, judgments’ or approach toward entities, events and their properties. In general, opinions can be expressed on anything, e.g., a product, a service, an individual, an organization, an event, or a topic. In this paper, the SA classification levels, approaches are discussed. It also reports about various categories of tools used to process the sentimental analysis data. And various application and challenges in sentimental analysis are explained.

Key-Words / Index Term

Senitmental Analysis, NLP, Opinion Mining

References

[1] Padmaja, S., & Fatima, S. S. (2013). Opinion Mining and Sentiment Analysis–An Assessment of Peoples’ Belief: A Survey. International Journal.
[2] AlessiaD`Andrea, Fernando Ferri, PatriziaGrifoni, TizianaGuzzo(2015) Approaches, Tools and Applications for Sentiment Analysis Implementation, International Journal of Computer Applications(09758887),vol.125)
[3] Medhat, W., Hassan, A., Korashy, H. 2014. “Sentiment analysis algorithms and applications: A survey”, Ain Shams Eng.
[4] Ashish Katrekar, AVP, Big Data Analytics “ an introduction to sentimental analysis”, GlobalLogic Inc, www.globallogic,com. – FIGURE 1.
[5] Maynard, D., & Funk, A. 2011. Automatic detection of political opinions in tweets. In: Proceedings of the 8th international conference on the semantic web, ESWC’11, p. 88-99.
[6] Pang, B., Lee, L., Vaithyanathan, S. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. Proc. of 7th EMNLP, pp.79-86.
[7] Michael W. Berry, Soft Computing in Data Science, First International Conference, Scds 2015, Putrajaya, Malaysia, September 2-3, 2015, Proceedings (Communications in Computer and Information Science)
[8] Vishal vyas , Uma, “An Extensive study of Sentiment Analysis tools and Binary Classification of tweets using Rapid Miner”, 6th International Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India, Procedia Computer Science 125 (2018) 329–335.
[9] K. Ravi, V. Ravi , “A survey on opinion mining and sentiment analysis: tasks, approaches and applications” Knowledge-Based Systems (2015), doi: http://dx.doi.org/10.1016/j.knosys.2015.06.015
[10] Lucas Montesano’s; S. Juan Pablo Rodrguez ; Marcos Orchard ; Susana Eyheramendy,” Sentiment analysis and prediction of events in TWITTER”, CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) , Page(s):903 - 910 , Oct 2015.
[11] Akshi Kumar, Prakhar Dogra and Vikrant Dabas,”Emotion Analysis of Twitter using Opinion Mining”,IEEE,978-1-4673-7948-9/15,2015
[12] B. Lue, Sentiment Analysis and Opinion Mining (Morgan & Claypool Publishers, 2012).
[13] Osamah A.M Ghaleb ,Anna Saro Vijendran, “THE CHALLENGES OF SENTIMENT ANALYSIS ON SOCIAL WEB COMMUNITIES” international journal on advanced researchin science and engineering vol 6, issue 12, dec 2017
[14] Harshali P. Patil1 and Mohammad Atique “Applications, Issues and Challenges in Sentiment analysis and Opinion Mining– A User’s perspective” international journal of control theory and aplications. International science press Vol 10. Number 19 2017.