Description
This study applies computational methods to identify the emotional drivers shaping consumer purchasing decisions. This involves classifying the type of patronage a business receives based on sentiment analysis of customer feedback.
Technologies
Program Language: Python3,
Data Source: Yelp Academic Dataset
Libraries: Pandas, Numpy, Matplotlib, Seaborn, NLTK etc.
Problem
How do review sentiments and star ratings influence potential customer’s decision-making?
- This question aims to delve into the persuasive power of sentiments and ratings in shaping the preferences and choices of consumers. It explores whether a well-articulated sentiment or a high star rating can sway a potential client towards patronizing a business.
To what extent do online reviews impact business patronage and check-ins?
- This question delves into the practical outcomes of the reviews and ratings. It seeks to quantify the impact of online testimonials on the actual engagement of consumers with businesses, such as their physical visits or virtual interactions.
Solution
I use python and selected natural language processing libraries to comprehensively investigate the impact of review sentiments and star ratings on business patronage. Through this investigation, we aim to shed light on the intricate relationship between online reviews and business outcomes. Specifically, to unravel how sentiments expressed in reviews and the numerical representation of satisfaction through star ratings affect the decision-making processes of potential customers against business patronage.
