Multilingual Sentiment Analysis as Product Reputation Insight

Abstract

A vast amount of textual data is continually being generated every day and available across the internet. By utilizing this ‘big data’ we can deduct meaningful information from its contents. One of the major tasks of Natural Language Processing is sentiment analysis. In recent years sentiment analysis has gain much attention but this is an extremely challenging task due to the complexity of human language. Our day-to-day life has always been influenced by what people think. Behavior of customer can be represented as sequential data describing the interest and views of customer about products. Earlier approaches like Lexicon-Based methods weren’t able to extract deep insight from text data as those approaches were based on finding the density of negation, Irrealis-blocking.