Authors : Megha Gupta; Chirasha Jain; Ishita Jain; Shivam Bisht; Deepanshu

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/mx54wsw9

Scribd : https://tinyurl.com/yey96745

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY214

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study, titled "Sentim(IOCL):Unlocking Sentiments: Enhancing IOCL Petrol Pump Experiences," delves deeply into the rich tapestry of public comments surrounding petrol pumps, with focus on discerning the sentiments and opinions relevant to IOCL. By employing cutting-edge natural language processing techniques, we extract explicit aspects from these comments and to gain a nuanced understanding of the sentiments associated with different facets. Our goal is to develop a usability index for selected petrol pumps, offering invaluable insights into their strengths and areas for refinement as perceived by the general populace. We're moving away from the usual method where each sentence is looked at separately. Instead, we're taking a more detailed approach that considers how different parts of the comments relate to each other. This way, we can understand not just what people are saying but also the reasons behind it. Our goal is to make a big contribution to understanding people's opinions by creating a method that looks at the whole picture, not just individual parts. By doing this, we hope to give IOCL and other companies in the industry practical advice on how to make their customers happier and keep getting better.

Keywords : Sentim IOCL, IOCL, Petrol Pumps, Public Comments, Sentiment Analysis, Natural Language Processing, Usability Index, Opinion Mining, Customer Satisfaction, Improvement, Personalized Research.

References :

1. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1-2), 1-135.
2. Liu, B. (2015). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 8(1), 1-167.
3. Mukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. Proceedings of the 21st International Conference on World Wide Web, 191-200.
4. Kim, S. M., & Hovy, E. (2004). Determining the sentiment of opinions. Proceedings of the 20th International Conference on Computational Linguistics, 1367-1373.
5. Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing, 347-354.
6. Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48-57.
7. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches, and applications. Knowledge-Based Systems, 89, 14-46.
8. Lu, Y., & Zhai, C. (2009). Multi-faceted opinion analysis in text: Model and method. Proceedings of the 18th International Conference on World Wide Web, 911-920.
9. Mukherjee, A., Venkataraman, R., Liu, B., & Glance, N. (2013). What Yelp fake review filter might be doing? Proceedings of the 22nd International Conference on World Wide Web, 535-536.