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The Hybrid Approach for Sentimental Analysis of Twitter Data

Kajal 1 , Prince Verma2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 612-617, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.612617

Online published on Jun 30, 2019

Copyright © Kajal, Prince Verma . 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: Kajal, Prince Verma, “The Hybrid Approach for Sentimental Analysis of Twitter Data,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.612-617, 2019.

MLA Style Citation: Kajal, Prince Verma "The Hybrid Approach for Sentimental Analysis of Twitter Data." International Journal of Computer Sciences and Engineering 7.6 (2019): 612-617.

APA Style Citation: Kajal, Prince Verma, (2019). The Hybrid Approach for Sentimental Analysis of Twitter Data. International Journal of Computer Sciences and Engineering, 7(6), 612-617.

BibTex Style Citation:
@article{Verma_2019,
author = { Kajal, Prince Verma},
title = {The Hybrid Approach for Sentimental Analysis of Twitter Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {612-617},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4601},
doi = {https://doi.org/10.26438/ijcse/v7i6.612617}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.612617}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4601
TI - The Hybrid Approach for Sentimental Analysis of Twitter Data
T2 - International Journal of Computer Sciences and Engineering
AU - Kajal, Prince Verma
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 612-617
IS - 6
VL - 7
SN - 2347-2693
ER -

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Abstract

Any kind of attitude, through or judgment that occurs due to any feeling is known as a sentiment which is also known as opinion mining. The sentiments of individuals towards particular elements are analyzed in this approach. To gather sentiment information, web or internet is the best known source. A platform that is accessed socially by various users to post their views is known as Twitter. The messages that are posted by these users are known as tweets. The properties of Tweets are highly unique due to which new challenges have raised. In comparison to several other domains, the sentiment analysis requires higher analysis studies. This research work is based on the sentiment analysis of product reviews of Amazon data. To apply sentiment analysis the technique of feature extraction and classification is applied. For the sentiment analysis in the previous work, the SVM technique is applied and which is replaced with the KNN technique.

Key-Words / Index Term

SA (Sentiment Analysis), SVM (Support Vector Machine), KNN (K-Nearest Neighbor).

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