The Role of Social Media In Promoting The Safety Of Women in Indian Cities
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Abstract
In every city, harassment and violence becomes one of the major problems for women. Further, women’s personal life is suffered by the bullying and abusive content presented in online social networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. However, the conventional methods failed to predict the maximum safety analysis. So, this work is focused on women safety prediction using decision tree (WSP-DT) classifier. Initially, twitter dataset is considered to implement the entire system, which is then pre-processed to remove the missing and unknown symbols. Then, natural language toolbox kit (NLTK) applied to perform the tokenization, conversion to lowercase, stop words identification, stemming and lemmatization of tweets. Then, text blob protocol is developed to identify sentiments of pre-processed tweets, which identifies the positive, negative and neutral polarities of tweets. Further, term frequency-inverse document frequency (TFIDF) is applied to extract the data features based on word and character frequency. Finally, decision tree classifier applied to identify the fake or genuine tweet based on multi-level training. The simulations conducted on twitter dataset show that the proposed WSP-DT classifier resulted in superior performance than the other methods.
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References
Gamon and 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.
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 A. F. Smeaton. “Classifying sentiment in microblogs: is brevity an advantage?”,
Proceedings of the 19th ACM international conference on Information and knowledge management. ACM,
V. Sahayak, V. Shete, and A. Pathan (2015). “Sentiment analysis on twitter data. International Journal of
Innovative Research in Advanced Engineering (IJIRAE)”, 2(1), 178-183.
N. Mamgain, E. Mehta, A. Mittal and G. Bhatt, “Sentiment analysis of top colleges in India using Twitter
data”, 2016 International Conference on Computational Techniques in Information and Communication
Technologies (ICCTICT), 2016, pp. 525-530, doi: 10.1109/ICCTICT.2016.7514636.
B. Gupta, M. Negi, K. Vishwakarma, G. Rawat, and P. Badhani (2017). “Study of Twitter sentiment
analysis using machine learning algorithms on Python”. International Journal of Computer Applications,
(9), 0975-8887.
Reyes-Menendez, J. R. Saura, and C. Alvarez Alons. “Understanding# World Environment Day user
opinions in Twitter: A topic-based sentiment analysis approach”. International journal of environmental
research and public health. 2018 Nov;15(11):2537.
D. Kumar and S. Aggarwal. “Analysis of Women Safety in Indian Cities Using Machine Learning on
Tweets”, 2019 Amity International Conference on Artificial Intelligence (AICAI), 2019, pp. 159-162, doi:
1109/AICAI.2019.8701247.
Vikram Chandra and Rampur Srinath. “Analysis of Women Safety using Machine Learning on Tweets”,
(IRJET) 2020.
F. Bravo-Marquez, B. Pfahringer, S. Mohammad and E. Frank, “Affective Tweets: a Weka Package for
Analysing effect in Tweets”, Journal of Machine Learning Research, vol. 20, no. 92, pp. 1-6, 2020.
K. Abdul Sattar, Q. Obeidat and M. Akure. “Towards harnessing based learning algorithms for tweets
sentiment analysis international conference of innovation and intelligence for informatics Computing and
technology 2020”.
K. R. Teja, K. A. Kumar, G. S. Praveen and D. N. Harini. “Analysis of Crimes Against Women in India
Using Machine Learning Techniques”, In Communication Software and Networks 2021 (pp. 499-510).
Springer, Singapore.
Srinivasan, S., P. Muthu Kannan, and R. Kumar. "A Machine Learning Approach to Design and Develop a
BEACON Device for Women’s Safety." Recent Advances in Internet of Things and Machine Learning.
Springer, Cham, 2022. 111-115.
Bonny, Afrin Jaman, et al. "Sentiment Analysis of User-Generated Reviews of Women Safety Mobile
Applications." 2022 First International Conference on Electrical, Electronics, Information and
Communication Technologies (ICEEICT). IEEE, 2022.
Ashok, K., et al. "A Survey on Design and Application Approaches in Women-Safety Systems." 2022 8th
International Conference on Advanced Computing and Communication Systems (ICACCS). Vol. 1. IEEE,
Tran, Martino, et al. "Monitoring the well-being of vulnerable transit riders using machine learning based
sentiment analysis and social media: Lessons from COVID-19." Environment and Planning B: Urban
Analytics and City Science (2022): 23998083221104489.
Zhong, Yongqi, et al. "Use of machine learning to estimate the per-protocol effect of low-dose aspirin on
pregnancy outcomes: a secondary analysis of a randomized clinical trial." JAMA network open 5.3 (2022):
e2143414-e2143414.
Patel, Bansi, and Manmitsinh C. Zala. "Crime Against Women Analysis & Prediction in India Using
Supervised Regression." 2022 First International Conference on Electrical, Electronics, Information and
Communication Technologies (ICEEICT). IEEE, 2022.
Islam, Md M., et al. "Risk factors identification and prediction of anemia among women in Bangladesh
using machine learning techniques." Current Women's Health Reviews 18.1 (2022): 118-133
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,
(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.