ARCHIVES
Research Article
We care: A User-Focused Sentiment Analysis Framework for Social Media Interactions
Mohammad Airad Khan1
Mohd. Irshad2
Pankaj Maurya3
Piyush Kumar4
Garima Gakhar5
1234 Department of Information Technology, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow, India. 5Professor of Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India.
Published Online: May-August 2024
Pages: 164-168
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20240302025
References
1. Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., Kumar,
V., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A., & Wang, Y. (2021). Setting the future of digital and
social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
https://doi.org/10.1016/j.ijinfomgt.2020.102168.
2. Gordon, W. (2024, April 2). Understanding Customer Behavior and Preferences. Nutshell. Retrieved from
https://www.nutshell.com/blog/understanding-customer-behavior-and-preferences
3. Muhammed T, S., & Mathew, S.K. (2022). The disaster of misinformation: a review of research in social media. International Journal of
Data Science and Analysis, 13, 271–285. https://doi.org/10.1007/s41060-022-00311-6
4. Wankhade, M., Rao, A.C.S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial
Intelligence Review, 55, 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
5. Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 5. https://doi.org/10.1186/s40537-
015-0015-2
6. Wankhade, M., Rao, A.C.S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial
Intelligence Review, 55(10), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
7. Walaa Medhat, Ahmed Hassan, Hoda Korashy. (2014). Sentiment analysis algorithms and applications: A survey. *Ain Shams Engineering
Journal, 5*(4), 1093-1113. doi: [https://doi.org/10.1016/j.asej.2014.04.011](https://doi.org/10.1016/j.asej.2014.04.011).
8. Chawla, P., Hazarika, S., Shen, H-W. (2020). Token-wise sentiment decomposition for ConvNet: Visualizing a sentiment classifier. *Visual
Informatics, 4*(2), 132-141. doi: [https://doi.org/10.1016/j.visinf.2020.04.006](https://doi.org/10.1016/j.visinf.2020.04.006).
9. Nahar, V., Unankard, S., Li, X., Pang, C. (2012). Sentiment Analysis for Effective Detection of Cyber Bullying. In: Sheng, Q.Z., Wang, G.,
Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. *Lecture Notes in Computer Science*, vol 7235. Springer,
Berlin, Heidelberg. doi: [https://doi.org/10.1007/978-3-642-29253-8_75](https://doi.org/10.1007/978-3-642-29253-8_75).
10. Hook, K., & Verdeja, E. (2022). Social Media Misinformation and the Prevention of Political Instability and Mass Atrocities. *Human
Security & Governance*, *Protecting Civilians & Human Security Program*, *Atrocities Prevention Study Group Project*. Retrieved
from [https://docs.wixstatic.com/ugd/e64469_ea7b4527e03e42b88c46fc35eeaf35e4.pdf].
V., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A., & Wang, Y. (2021). Setting the future of digital and
social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
https://doi.org/10.1016/j.ijinfomgt.2020.102168.
2. Gordon, W. (2024, April 2). Understanding Customer Behavior and Preferences. Nutshell. Retrieved from
https://www.nutshell.com/blog/understanding-customer-behavior-and-preferences
3. Muhammed T, S., & Mathew, S.K. (2022). The disaster of misinformation: a review of research in social media. International Journal of
Data Science and Analysis, 13, 271–285. https://doi.org/10.1007/s41060-022-00311-6
4. Wankhade, M., Rao, A.C.S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial
Intelligence Review, 55, 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
5. Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 5. https://doi.org/10.1186/s40537-
015-0015-2
6. Wankhade, M., Rao, A.C.S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial
Intelligence Review, 55(10), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
7. Walaa Medhat, Ahmed Hassan, Hoda Korashy. (2014). Sentiment analysis algorithms and applications: A survey. *Ain Shams Engineering
Journal, 5*(4), 1093-1113. doi: [https://doi.org/10.1016/j.asej.2014.04.011](https://doi.org/10.1016/j.asej.2014.04.011).
8. Chawla, P., Hazarika, S., Shen, H-W. (2020). Token-wise sentiment decomposition for ConvNet: Visualizing a sentiment classifier. *Visual
Informatics, 4*(2), 132-141. doi: [https://doi.org/10.1016/j.visinf.2020.04.006](https://doi.org/10.1016/j.visinf.2020.04.006).
9. Nahar, V., Unankard, S., Li, X., Pang, C. (2012). Sentiment Analysis for Effective Detection of Cyber Bullying. In: Sheng, Q.Z., Wang, G.,
Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. *Lecture Notes in Computer Science*, vol 7235. Springer,
Berlin, Heidelberg. doi: [https://doi.org/10.1007/978-3-642-29253-8_75](https://doi.org/10.1007/978-3-642-29253-8_75).
10. Hook, K., & Verdeja, E. (2022). Social Media Misinformation and the Prevention of Political Instability and Mass Atrocities. *Human
Security & Governance*, *Protecting Civilians & Human Security Program*, *Atrocities Prevention Study Group Project*. Retrieved
from [https://docs.wixstatic.com/ugd/e64469_ea7b4527e03e42b88c46fc35eeaf35e4.pdf].
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