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Analyzing Sentiment and Determining Negation Scope in Political News

S. Padmaja1 , Sasidhar Bandu2 , Deepa Ganu3 , S. Sameen Fatima4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-10 , Page no. 37-42, Oct-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i10.3742

Online published on Oct 31, 2019

Copyright © S. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima . 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. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima, “Analyzing Sentiment and Determining Negation Scope in Political News,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.37-42, 2019.

MLA Style Citation: S. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima "Analyzing Sentiment and Determining Negation Scope in Political News." International Journal of Computer Sciences and Engineering 7.10 (2019): 37-42.

APA Style Citation: S. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima, (2019). Analyzing Sentiment and Determining Negation Scope in Political News. International Journal of Computer Sciences and Engineering, 7(10), 37-42.

BibTex Style Citation:
@article{Padmaja_2019,
author = {S. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima},
title = {Analyzing Sentiment and Determining Negation Scope in Political News},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2019},
volume = {7},
Issue = {10},
month = {10},
year = {2019},
issn = {2347-2693},
pages = {37-42},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4890},
doi = {https://doi.org/10.26438/ijcse/v7i10.3742}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.3742}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4890
TI - Analyzing Sentiment and Determining Negation Scope in Political News
T2 - International Journal of Computer Sciences and Engineering
AU - S. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima
PY - 2019
DA - 2019/10/31
PB - IJCSE, Indore, INDIA
SP - 37-42
IS - 10
VL - 7
SN - 2347-2693
ER -

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Abstract

Automatic detection of linguistic negation in free text is a demanding need for many text processing applications including sentiment analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu to predict the scope of negation in the text. In this paper, our main focus was on identifying the scope of negation in news articles for two political parties namely YSR Congress Party (YSRCP) and Alliance (which includes Jana Sena Party, Communist Party of India , Bahujan Samaj Party , Telugu Desam Party (TDP)) by using two existing namely Fixed Window Length (FWL), Dependency Analysis (DA) and one proposed methodology is Negation Sentiment Analyzer (NSA). The average F measures for each one of them were 0.61, 0.66 and 0.72 respectively. It was observed that NSA outperforms the other two. We further evaluated the results of NSA against the standard BioScope negation corpus as a benchmark, achieving 0.75 as a F1 scores

Key-Words / Index Term

Negation Identification, Sentiment Analysis, Natural Language Processing, Artificial Intelligence

References

[1] Insaac G. Councill, Ryan McDonald, 2010, What‟s Great and What‟s Not: Learning to Classify the Scope of Negation for Improved Sentiment Analysis, Negation and Speculation in Natural Language Processing (NeSp-NLP 2010), Proceedings of the Workshop, UppNSAla, Sweden, 10 July 2010.
[2] Kevin Lerman, Ari Gilder, Mark Dredze, Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis, 2008.
[3] Veronika Vincze, György Szarvas, Richárd Farkas, György Móra, and János Csirik, The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts, BMC Bioinformatics, 2008.
[4] Talmy Giv´on, English Grammer: A Function-Based Introduction. Benjamins, Amsterdam, NL, 1993.
[5] Gunnel Tottie. Negation in English Speech and Writing: A Study in Variation Academic, San Diego, CA, 1991.
[6] Theresa Wilson, Janyce Wiebe, and Paul Hoffmann, Recognizing contextual polarity in phrase level sentiment analysis. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing Pages 347-354, 2005.
[7] Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables. Proceedingsof The 11th Annual conference of the North American Chapter of the Association for Computational Linguistics ACL, Los Angeles, CA, 2010.
[8] Cristian Danescu-Niculescu-Mizil, Lillian Lee, And Richard Ducott, Without a “doubt‟? Unsupervised discovery of downward-entailing operators. Proceedings of The 10th Annual Conference of the North American Chapter of the Association for Computational Linguistics. ACL, Boulder, CO, 2008.
[9] Theresa Wilson, Janyce Wiebe, and Paul Hoffmann, Recognizing contextual polarity in phraselevel sentiment analysis.Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing Vancouver, Canada, 2005.
[10] Edward S. Klima. Negation in English. Readings in the Philosophy of Language. Ed. J. A. Fodor and J. J. Katz. Prentice Hall, B. Englewood Cliffs, NJ: 246-323, 1964.
[11] Miller, G. A., WordNet: a lexical database for English. Communications of the ACM, Vol. 38, 11, pp. 39-41, 1995.
[12] Penn_Treebank, The Penn Treebank Project [WWW]. Available from: http://www.cis.upenn.edu/~treebank/ [Accessed April 21, 2012], 1992,
[13] Heerschop, B., Hogenboom, A. and Frasincar, F., 2011, Sentiment Lexicon Creation from Lexical Resources. In: 14th International Conference on Business Information Systems (BIS 2011). Springer, 185-196.