Automated Product Sensing and Sentiments

Abstract

Our project, APSS, is a web application that analyses brands, competitors, hashtags and their market trends in real time using keywords. The user can input the keywords. The app uses Reddit and News APIs to get data about these keywords at the backend. We then pass this textual data from the VADER which is a pre trained model for text sentiment analysis. The model is available in NLTK package to help us in predicting the sentiments correctly. The VADER is better than TEXTBLOB in order to achieve the accuracy. Our project is a very effective monitoring tool which portrays the results in the form of mentions, graphs, percentages and comparison charts. It helps the new brands in market as well as established brands to analyze about the competitors in market and trends. This helps them devise their marketing strategies and improve their products accordingly.