Sentiment Analysis of Online Customer Reviews to Optimize Marketing Strategies in the E-Commerce Industry
TL;DR
The authors wanted to look beyond surface-level star ratings and try to understand the mood, the frustrations, and the small wins that shape how consumers feel about brands.
Abstract
The present study observes the ideas, feelings, and shopping experiences that shapes consumers decision through online reviews within the fast-expanding e-commerce space. These days, evaluations have a significant impact on consumer decisions, brand perception, and customer trust. The authors wanted to look beyond surface-level star ratings and try to understand the mood, the frustrations, and the small wins that shape how consumers feel about brands. Two major e commerce players were studied for this analysis viz, Flipkart, Meesho representing affordable items. Both of this player operates in a slightly different niche, yet both share the same challenge: earning trust in a market where loyalty shifts quickly. For the practical side of this study, author pulled product reviews and star ratings from a publicly available Kaggle dataset. The reviews which were focused on covered electronics, fashion, lifestyle products, and cosmetics. These categories were analysed because written feedback tends to influence how buyers make decisions in these segments- a single negative review about a phone's battery life or a foundation shade mismatch can genuinely push someone toward a competitor. Authors used Python for the sentiment analysis of reviews collected from Kaggle for the analysis. For data cleaning and manipulation, Pandas library was used to filter rows, handle missing values and reshape the columns. A lot of the early preprocessing work dropping duplicates, normalizing the review text, and cutting columns that weren't going to matter for the analysis happened directly in Pandas. TextBlob was used to generate polarity scores and then split each review into positive, neutral, or negative based on where the score landed. Matplotlib and Seaborn were used for analysis of sentiment percentages across five platforms. This study reinforces that sentiment analysis is genuinely useful online shoppers and retailers- who want to stay close to what their customers actually feel. Going through customer evaluations helps a business see what is working, where the product or service is falling short, and how marketing messages can be shaped to speak to real concerns rather than assumptions