Sentiment analysis on e-commerce platforms such as Kindle offers significant advantages by providing insights into customer opinions and satisfaction levels. By analyzing customer reviews and feedback, businesses can quickly identify trends in positive or negative sentiment, allowing them to respond to customer needs and concerns in real time. This analysis helps improve product offerings, enhance customer service, and tailor marketing strategies to align with customer preferences. Furthermore, sentiment analysis enables companies to monitor brand reputation, gain a competitive edge by understanding consumer attitudes, and make data-driven decisions to boost customer loyalty and sales.
The code compares three text classification models (Multinomial Naive Bayes, Logistic Regression, and Linear SVC) for sentiment analysis of Kindle reviews. By analyzing their performance with varying features, the code aims to identify the best-performing model and optimal feature set.
It determine the most effective approach for classifying Kindle review sentiment, considering accuracy and model complexity. It also provides insights into the overall sentiment expressed in the dataset.Since Linear SVC achieves the highest accuracy of 84% , we can state that it's the most suitable model, using a specific number of features for optimal performance. It would also highlight the general sentiment (positive as well as negative) prevalent in the reviews.