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A Survey on Digital Content Sentiment Features and Techniques

Sukhlal Sangule1 , Sunil Phulre2

Section:Survey Paper, Product Type: Journal Paper
Volume-8 , Issue-3 , Page no. 114-118, Mar-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i3.114118

Online published on Mar 30, 2020

Copyright © Sukhlal Sangule, Sunil Phulre . 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: Sukhlal Sangule, Sunil Phulre, “A Survey on Digital Content Sentiment Features and Techniques,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.114-118, 2020.

MLA Style Citation: Sukhlal Sangule, Sunil Phulre "A Survey on Digital Content Sentiment Features and Techniques." International Journal of Computer Sciences and Engineering 8.3 (2020): 114-118.

APA Style Citation: Sukhlal Sangule, Sunil Phulre, (2020). A Survey on Digital Content Sentiment Features and Techniques. International Journal of Computer Sciences and Engineering, 8(3), 114-118.

BibTex Style Citation:
@article{Sangule_2020,
author = {Sukhlal Sangule, Sunil Phulre},
title = {A Survey on Digital Content Sentiment Features and Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2020},
volume = {8},
Issue = {3},
month = {3},
year = {2020},
issn = {2347-2693},
pages = {114-118},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5062},
doi = {https://doi.org/10.26438/ijcse/v8i3.114118}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i3.114118}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5062
TI - A Survey on Digital Content Sentiment Features and Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - Sukhlal Sangule, Sunil Phulre
PY - 2020
DA - 2020/03/30
PB - IJCSE, Indore, INDIA
SP - 114-118
IS - 3
VL - 8
SN - 2347-2693
ER -

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Abstract

Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering, etc. extend the utilization of sentiment analysis. This paper discusses in details the various techniques to Sentiment Analysis, so class of sentiment identify accurately. Text mining pre-processing steps were also discussed for generation of features. This paper provides previous researcher work in detail. Challenges of sentiment mining were also summarized. 

Key-Words / Index Term

Data Mining, Opinion mining, Sentiment analysis, Text Preprocessing

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