Determination of news biasedness using content sentiment analysis algorithm
Nowadays, identifying news biases in the social media is one of the most fundamental problems. News bias is a complex process that comprises several dimensions to be taken into account and it is interlinked with social, political and economic problems. In general, news bias has the ability to refle...
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| Main Authors: | , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-11-01.
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| Online Access: | Get fulltext |
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| LEADER | 02463 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs18242_13068 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a SV, Shri Bharathi |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Geetha, Angelina |e author |
| 245 | 0 | 0 | |a Determination of news biasedness using content sentiment analysis algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-11-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18242 | ||
| 520 | |a Nowadays, identifying news biases in the social media is one of the most fundamental problems. News bias is a complex process that comprises several dimensions to be taken into account and it is interlinked with social, political and economic problems. In general, news bias has the ability to reflect opinion of people about a topic or government policies and actions. The proposed algorithm develops a system which can detect the biasedness of news topics from different news Websites.This approach automatically collects the news contents from various online news media portals and then consolidates them for the determination of news biasedness. In the experimental study, the news topics are gathered from various Websites of U.S., U.K., and India. For training dataset 3265 news sentences were collected under various news topics from 20 different news Websites. The effectiveness of classification of algorithm is proved by the extensive experimental study. The proposed algorithm provides a method improves the determination of news biasedness, which in turn may help in providing impartial, unbiased and reliable information. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Sentiment Mining; Data Mining; Natural Language Processing | ||
| 690 | |a News bias, Machine Learning, Data and Text mining, Sentiment Analysis News values | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 16, No 2: November 2019; 882-889 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v16.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18242/13068 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18242/13068 |z Get fulltext |