Product Sentiment Trend Prediction

Abstract

The prospects of spectrum sentiment analysis are great and is a field that has been given very little research focus. We develop a system that can recognize human recognizable emotions and quantify them, the system can then predict the trend in the spectrum sentiments provided a chronological data. This paper discusses a lexicon-based approach for spectrum sentiment analysis. It further describes a quantification method to factor in the effects of time in trend prediction and a novel idea of using consecutive calculated values for current trend value calculation. The system is designed for e-commerce data but has flexibility to be used for other fields too. The system uses a simple neural network with image and text features as input and the trend values as output. This system can then be used to predict sentiment trend for newer or existing products. The system shows great prospects for multi-modal sentiment analysis of sentiments on spectrum range and can be advanced by using more complex approach.