Using Sentiment Analysis to Spot Trending Products

Abstract

Trend detection refers to the process of recognizing significant patterns or trends within large datasets, including changes over time and seasonal variations. In the context of product-related data, trend product detection is a specialized tool or software that is specifically designed to identify trends in sales, customer preferences, product reviews, and other relevant data points. The study focuses on the combination of trend detection and sentiment analysis as a powerful approach for trend spotting. In this paper, we present a study that uses natural language processing (NLP) techniques and the VADER sentiment analysis tool to identify product sentiment from customer reviews. We then applied decision tree analysis to predict whether a product is likely to trend or not based on its sentiment score. Additionally, we used clustering to group the top-rated products based on their sentiment scores and applied a moving average to determine which products are currently trending. Our results show that the combination of trend detection and sentiment analysis can effectively identify trending products with an accuracy of 93%. This study demonstrates the power of NLP and sentiment analysis in detecting trends and provides valuable insights for businesses to make data-driven decisions about their product offerings.