Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor

Aadil Gani Ganie; Samad Dadvandipour

doi:10.1007/978-3-030-82322-1_1

Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor

Aadil Gani Ganie^*
, Samad Dadvandipour

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Sentiment analysis is a developing machine learning approach to understand the text’s emotions. Sentimental analysis is used to get the context of text. Virtualization of source less information is bait to further the political, religious, or personal agenda. Sentimental analysis of tweets on a late Indian actor (Sushant Singh Rajput) has been studied in this paper. Using different sentimental analysis classifiers, most of the tweets were harmful in context. Naïve Bayes proves to be the most effective classifier, with more than 82% accuracy and fewer false positive and negative ratios. Neural networks have been used with two layers on the dataset; increasing the value of epochs increases the accuracy to some level and shows an accuracy of 70.58%. Both Random Forest and SVM classifiers showed the same accuracy of 73.52% and the same false-positive and false-negative ratios.

Original language	English
Title of host publication	Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers
Editors	Arun Solanki, Sanjay Kumar Sharma, Sandhya Tarar, Pradeep Tomar, Sandeep Sharma, Anand Nayyar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-16
Number of pages	14
ISBN (Print)	9783030823214
DOIs	https://doi.org/10.1007/978-3-030-82322-1_1
State	Published - 2021
Externally published	Yes
Event	1st International Conference on Artificial Intelligence and Sustainable Computing for Smart City, AIS2C2 2021 - Virtual, Online Duration: 22 Mar 2021 → 23 Mar 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1434
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Artificial Intelligence and Sustainable Computing for Smart City, AIS2C2 2021
City	Virtual, Online
Period	22/03/21 → 23/03/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Classifiers
Fake news
Hate comments
Sentiment analysis

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-82322-1_1

Cite this

Ganie, A. G., & Dadvandipour, S. (2021). Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor. In A. Solanki, S. K. Sharma, S. Tarar, P. Tomar, S. Sharma, & A. Nayyar (Eds.), Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers (pp. 3-16). (Communications in Computer and Information Science; Vol. 1434). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-82322-1_1

Ganie, Aadil Gani ; Dadvandipour, Samad. / Sentiment Analysis on the Effect of Trending Source Less News : Special Reference to the Recent Death of an Indian Actor. Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers. editor / Arun Solanki ; Sanjay Kumar Sharma ; Sandhya Tarar ; Pradeep Tomar ; Sandeep Sharma ; Anand Nayyar. Springer Science and Business Media Deutschland GmbH, 2021. pp. 3-16 (Communications in Computer and Information Science).

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title = "Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor",

abstract = "Sentiment analysis is a developing machine learning approach to understand the text{\textquoteright}s emotions. Sentimental analysis is used to get the context of text. Virtualization of source less information is bait to further the political, religious, or personal agenda. Sentimental analysis of tweets on a late Indian actor (Sushant Singh Rajput) has been studied in this paper. Using different sentimental analysis classifiers, most of the tweets were harmful in context. Na{\"i}ve Bayes proves to be the most effective classifier, with more than 82\% accuracy and fewer false positive and negative ratios. Neural networks have been used with two layers on the dataset; increasing the value of epochs increases the accuracy to some level and shows an accuracy of 70.58\%. Both Random Forest and SVM classifiers showed the same accuracy of 73.52\% and the same false-positive and false-negative ratios.",

keywords = "Classifiers, Fake news, Hate comments, Sentiment analysis",

author = "Ganie, \{Aadil Gani\} and Samad Dadvandipour",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 1st International Conference on Artificial Intelligence and Sustainable Computing for Smart City, AIS2C2 2021 ; Conference date: 22-03-2021 Through 23-03-2021",

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doi = "10.1007/978-3-030-82322-1\_1",

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Ganie, AG & Dadvandipour, S 2021, Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor. in A Solanki, SK Sharma, S Tarar, P Tomar, S Sharma & A Nayyar (eds), Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1434, Springer Science and Business Media Deutschland GmbH, pp. 3-16, 1st International Conference on Artificial Intelligence and Sustainable Computing for Smart City, AIS2C2 2021, Virtual, Online, 22/03/21. https://doi.org/10.1007/978-3-030-82322-1_1

Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor. / Ganie, Aadil Gani; Dadvandipour, Samad.
Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers. ed. / Arun Solanki; Sanjay Kumar Sharma; Sandhya Tarar; Pradeep Tomar; Sandeep Sharma; Anand Nayyar. Springer Science and Business Media Deutschland GmbH, 2021. p. 3-16 (Communications in Computer and Information Science; Vol. 1434).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sentiment Analysis on the Effect of Trending Source Less News

T2 - 1st International Conference on Artificial Intelligence and Sustainable Computing for Smart City, AIS2C2 2021

AU - Ganie, Aadil Gani

AU - Dadvandipour, Samad

PY - 2021

Y1 - 2021

N2 - Sentiment analysis is a developing machine learning approach to understand the text’s emotions. Sentimental analysis is used to get the context of text. Virtualization of source less information is bait to further the political, religious, or personal agenda. Sentimental analysis of tweets on a late Indian actor (Sushant Singh Rajput) has been studied in this paper. Using different sentimental analysis classifiers, most of the tweets were harmful in context. Naïve Bayes proves to be the most effective classifier, with more than 82% accuracy and fewer false positive and negative ratios. Neural networks have been used with two layers on the dataset; increasing the value of epochs increases the accuracy to some level and shows an accuracy of 70.58%. Both Random Forest and SVM classifiers showed the same accuracy of 73.52% and the same false-positive and false-negative ratios.

AB - Sentiment analysis is a developing machine learning approach to understand the text’s emotions. Sentimental analysis is used to get the context of text. Virtualization of source less information is bait to further the political, religious, or personal agenda. Sentimental analysis of tweets on a late Indian actor (Sushant Singh Rajput) has been studied in this paper. Using different sentimental analysis classifiers, most of the tweets were harmful in context. Naïve Bayes proves to be the most effective classifier, with more than 82% accuracy and fewer false positive and negative ratios. Neural networks have been used with two layers on the dataset; increasing the value of epochs increases the accuracy to some level and shows an accuracy of 70.58%. Both Random Forest and SVM classifiers showed the same accuracy of 73.52% and the same false-positive and false-negative ratios.

KW - Classifiers

KW - Fake news

KW - Hate comments

KW - Sentiment analysis

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DO - 10.1007/978-3-030-82322-1_1

M3 - Conference contribution

AN - SCOPUS:85113285941

SN - 9783030823214

T3 - Communications in Computer and Information Science

SP - 3

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BT - Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers

A2 - Solanki, Arun

A2 - Sharma, Sanjay Kumar

A2 - Tarar, Sandhya

A2 - Tomar, Pradeep

A2 - Sharma, Sandeep

A2 - Nayyar, Anand

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 March 2021 through 23 March 2021

ER -

Ganie AG, Dadvandipour S. Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor. In Solanki A, Sharma SK, Tarar S, Tomar P, Sharma S, Nayyar A, editors, Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 3-16. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-82322-1_1

Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this