ANALYSIS OF WOMEN SAFETY IN INDIAN CITIES ON TWEETS USING MACHINE LEARNING
Main Article Content
Abstract
Women and girls have been experiencing a lot of violence and harassment in public places in various cities starting from stalking and leading to abuse harassment or abuse assault. This research paper basically focuses on the role of social media in promoting the safety of women in Indian cities with special reference to the role of social media websites and applications including Twitter platform Facebook and Instagram. This paper also focuses on how a sense of responsibility on part of Indian society can be developed the common Indian people so that we should focus on the safety of women surrounding them. Tweets on Twitter which usually contains images and text and also written messages and quotes which focus on the safety of women in Indian cities can be used to read a message amongst the Indian Youth Culture and educate people to take strict action and punish those who harass the women. Twitter and other Twitter handles which include hash tag messages that are widely spread across the whole globe sir as a platform for women to express their views about how they feel while we go out for work or travel in a public transport and what is the state of their mind when they are surrounded by unknown men and whether these women feel safe or not?
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Agarwal, Apoorv, Fadi Biadsy, and Kathleen R. Mckeown. "Contextual phrase-level polarity
analysis using lexical affect scoring and syntactic n-grams." Proceedings of the 12th Conference
of the European Chapter of the Association for Computational Linguistics. Association for
Computational Linguistics, 2009.
Barbosa, Luciano, and Junlan Feng. "Robust sentiment detection on twitter from biased and
noisy data." Proceedings of the 23rd international conference on computational linguistics:
posters. Association for Computational Linguistics, 2010.
Bermingham, Adam, and Alan F. Smeaton. "Classifying sentiment in microblogs: is brevity
an advantage?." Proceedings of the 19th ACM international conference on Information and
knowledge management. ACM, 2010.
Gamon, Michael. "Sentiment classification on customer feedback data: noisy data, large
feature vectors, and the role of linguistic analysis." Proceedings of the 20th international
conference on Computational Linguistics. Association for Computational Linguistics, 2004.
Kim, Soo-Min, and Eduard Hovy. "Determining the sentiment of opinions."
Proceedings of the 20th international conference on Computational Linguistics. Association for
Computational Linguistics, 2004.
Klein, Dan, and Christopher D. Manning. "Accurate unlexicalized parsing." Proceedings of
the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association
for Computational Linguistics, 2003.
Charniak, Eugene, and Mark Johnson. "Coarse-to-fine n-best parsing and MaxEnt
discriminative reranking." Proceedings of the 43rd annual meeting on association for
computational linguistics. Association for Computational Linguistics, 2005.
Gupta, B., Negi, M., Vishwakarma, K., Rawat, G., & Badhani, P. (2017). Study of Twitter
sentiment analysis using machine learning algorithms on Python. International Journal of
Computer Applications, 165(9), 0975- 8887.
Sahayak, V., Shete, V., & Pathan, A. (2015). Sentiment analysis on twitter data. International
Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(1), 178-183.
Mamgain, N., Mehta, E., Mittal, A., & Bhatt, G. (2016, March). Sentiment analysis of top
colleges in India using Twitter data. In Computational Techniques in Information and
Communication Technologies (ICCTICT), 2016 International Conference on (pp. 525-530).
IEEE.