"Emograph: transforming customer sentiment into actionable insights for smarter purchasing"
Gayatri Patil
Ajeenkya DYPatil School of Engineering,Savitribai Phule Pune University
Gauri Pansambal
Ajeenkya DYPatil School of Engineering,Savitribai Phule Pune University
Sanket Pandit
Ajeenkya DYPatil School of Engineering,Savitribai Phule Pune University
Abhay Pawar
Ajeenkya DYPatil School of Engineering,Savitribai Phule Pune University
Parth Nalawade
Ajeenkya DYPatil School of Engineering,Savitribai Phule Pune University
DOI: https://doi.org/10.59429/ima.v2i2.6976
Keywords: Customer, Purchasing, Emograph,decision-making
Abstract
"Emograph: Transforming Customer Sentiment into Actionable Insights for Smarter Purchasing" introduces a cutting-edge approach to understanding customer sentiments and translating them into practical strategies for informed decision-making. In simpler terms, it's about using advanced technology to figure out how customers feel about products or services, and then using that information to make smarter choices.Imagine you're a business owner. You want to know what your customers think about your products. Emograph helps you do that. It uses fancy tools and techniques to analyze data from customers - like what they say on social media, in reviews, or even in surveys. Then, it breaks down all that information to figure out if people like your stuff or not. Basically, Emograph is like having a super-smart assistant who listens to what your customers are saying, translates it into plain English, and then gives you advice on what to do next. It's like having a secret weapon for making your business even better. And in today's world, where understanding customers is key to success.
References
1. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information
Retrieval, 2(1-2), 1-135
2. Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social
media text. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media.
3. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies,
5(1), 1-167.
4. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University
Press.
5. McKinney, W., & others. (2010). Data structures for statistical computing in Python. In Proceedings of the 9th
Python in Science Conference (Vol. 445, pp. 51-56).
6. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Vanderplas, J. (2011). Scikitlearn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825-2830.
7. Rosenthal, S., Nakov, P., Kiritchenko, S... Mohammad, S., Ritter, A., & Stoyanov, V. (2017). Semeval-2017 task
4: Sentiment analysis in twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation
(SemEval-2017) (pp. 502-518).
8. van Rossum, G., & Drake Jr, F. L. (2009). Python 3 reference manual. CreateSpace.