Crime Detection Using Sentiment Analysis
Abstract Women and girls have been subjected to a great deal of violence and harassment in public locations around the country, ranging from stalking to abuse harassment and assault. This research paper examines the role of social media in improving women's safety in Indian cities, with a focus on the use of social media websites and apps such as Twitter, Facebook, and Instagram. This research also looks at how ordinary Indians can develop a sense of responsibility in Indian society so that we can focus on the protection of women in their surroundings. Tweets on the safety of women in Indian cities, which often include images and text as well as written phrases and quotations, can be used to send a message to the Indian youth culture and encourage them to take harsh action and punish those who harass women. Twitter and other Twitter handles that feature hash tag messages are extensively used throughout the world as a channel for women to share their feelings about how they feel when going to work or travelling by public transportation and what is their mental condition when they are surrounded by unknown males, and do they feel safe or not?
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Agarwal, S., Godbole, S., Punjani, D., & Roy, S. (2007, October). How much noise is too much: A study in automatic text classification. In Seventh IEEE International Conference on Data Mining (ICDM 2007) (pp. 3-12). IEEE.
Ahmad, K., Han, J., Hutson, E., Kearney, C., & Liu, S. (2016). Media-expressed negative tone and firm-level stock returns. Journal of Corporate Finance, 37, 152-172.
Baid, P., Gupta, A., & Chaplot, N. (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), 45-49.
Bollen, J., Mao, H., & Pepe, A. (2011, July). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the international AAAI conference on web and social media (Vol. 5, No. 1).
Choudhury, M., Saraf, R., Jain, V., Mukherjee, A., Sarkar, S., & Basu, A. (2007). Investigation and modeling of the structure of texting language. International Journal of Document Analysis and Recognition (IJDAR), 10(3), 157-174.
Diakopoulos, N. A., & Shamma, D. A. (2010, April). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1195-1198).
Fersini, E., Messina, E., & Pozzi, F. A. (2014). Sentiment analysis: Bayesian ensemble learning. Decision support systems, 68, 26-38.
Gamon, M. (2004). Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics (pp. 841-847).
Kabir, M., Kabir, M. M. J., Xu, S., & Badhon, B. (2019). An empirical research on sentiment analysis using machine learning approaches. International Journal of Computers and Applications, 1-9.
Kolchyna, O., Souza, T. T., Treleaven, P., & Aste, T. (2015). Twitter sentiment analysis: Lexicon method, machine learning method and their combination. arXiv preprint arXiv:1507.00955.
Matsumoto, S., Takamura, H., & Okumura, M. (2005, May). Sentiment classification using word sub-sequences and dependency sub-trees. In Pacific-Asia conference on knowledge discovery and data mining (pp. 301-311). Springer, Berlin, Heidelberg.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., & Smeaton, A.F. (2009, November). Topic-dependent sentiment analysis of financial blogs. In Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion (pp. 9-16).
Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. arXiv preprint cs/0409058.
Pang, B., & Lee, L. (2009). Opinion mining and sentiment analysis. Comput. Linguist, 35(2), 311-312.
Renault, T. (2020). Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages. Digital Finance, 2(1), 1-13.
Sarlan, A., Nadam, C., & Basri, S. (2014, November). Twitter sentiment analysis. In Proceedings of the 6th International conference on Information Technology and Multimedia (pp. 212-216). IEEE.
Siddiqui, S., Darbari, M., & Yagyasen, D. (2020). Modelling and Simulation of Queuing Models through the concept of Petri Nets. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(3), 17-28.
Wang, S. I., & Manning, C. D. (2012, July). Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 90-94).
Whitehead, M., & Yaeger, L. (2010). Sentiment mining using ensemble classification models. In Innovations and advances in computer sciences and engineering (pp. 509-514). Springer, Dordrecht.
Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing (pp. 347-354).
Ahmad, K., Han, J., Hutson, E., Kearney, C., & Liu, S. (2016). Media-expressed negative tone and firm-level stock returns. Journal of Corporate Finance, 37, 152-172.
Baid, P., Gupta, A., & Chaplot, N. (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), 45-49.
Bollen, J., Mao, H., & Pepe, A. (2011, July). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the international AAAI conference on web and social media (Vol. 5, No. 1).
Choudhury, M., Saraf, R., Jain, V., Mukherjee, A., Sarkar, S., & Basu, A. (2007). Investigation and modeling of the structure of texting language. International Journal of Document Analysis and Recognition (IJDAR), 10(3), 157-174.
Diakopoulos, N. A., & Shamma, D. A. (2010, April). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1195-1198).
Fersini, E., Messina, E., & Pozzi, F. A. (2014). Sentiment analysis: Bayesian ensemble learning. Decision support systems, 68, 26-38.
Gamon, M. (2004). Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics (pp. 841-847).
Kabir, M., Kabir, M. M. J., Xu, S., & Badhon, B. (2019). An empirical research on sentiment analysis using machine learning approaches. International Journal of Computers and Applications, 1-9.
Kolchyna, O., Souza, T. T., Treleaven, P., & Aste, T. (2015). Twitter sentiment analysis: Lexicon method, machine learning method and their combination. arXiv preprint arXiv:1507.00955.
Matsumoto, S., Takamura, H., & Okumura, M. (2005, May). Sentiment classification using word sub-sequences and dependency sub-trees. In Pacific-Asia conference on knowledge discovery and data mining (pp. 301-311). Springer, Berlin, Heidelberg.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., & Smeaton, A.F. (2009, November). Topic-dependent sentiment analysis of financial blogs. In Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion (pp. 9-16).
Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. arXiv preprint cs/0409058.
Pang, B., & Lee, L. (2009). Opinion mining and sentiment analysis. Comput. Linguist, 35(2), 311-312.
Renault, T. (2020). Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages. Digital Finance, 2(1), 1-13.
Sarlan, A., Nadam, C., & Basri, S. (2014, November). Twitter sentiment analysis. In Proceedings of the 6th International conference on Information Technology and Multimedia (pp. 212-216). IEEE.
Siddiqui, S., Darbari, M., & Yagyasen, D. (2020). Modelling and Simulation of Queuing Models through the concept of Petri Nets. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(3), 17-28.
Wang, S. I., & Manning, C. D. (2012, July). Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 90-94).
Whitehead, M., & Yaeger, L. (2010). Sentiment mining using ensemble classification models. In Innovations and advances in computer sciences and engineering (pp. 509-514). Springer, Dordrecht.
Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing (pp. 347-354).
Khan, R., Siddiqui, S., Rastogi, A., & Ali Ansari, Z. (2021). Crime Detection Using Sentiment Analysis. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(3), 281–291. https://doi.org/10.14201/ADCAIJ2021103281291
Most read articles by the same author(s)
- Shadab Siddiqui, Manuj Darbari, Diwakar Yagyasen, Modelling and Simulation of Queuing Models through the concept of Petri Nets , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 9 No. 3 (2020)
Downloads
Download data is not yet available.
+
−