Sentiment Analysis on Customer Reviews using Convolutional Neural Network

21st century is touted as a data revolution where tons of data silos being created every second. These data silos are stored across various servers and creating authentic data mines across multiple locations. Business leaders across the world realized the significance of the information residing in these data silos and constantly trying to extract relevant information which can be used to strategize and make savvy decisions. Prodigious salient information out there concealed in customer reviews, tweets, social media comments, and the challenge is to extract hidden insights from it. It is an arduous process to extract meaningful information from these data silos manually. One of the imperative aspects in Natural Language Processing is sentiment Analysis - Extracting emotions, opinions, sentiments, fervor from the corpus of data and showcasing general opinion of the reviewers about the product. All previous works in NLP rely on traditional classification techniques like Support Vector Machines, Naive Bayes, Maximum Entropy, Random forest, etc. With the advent of deep learning models, there is a reliable improvement in accuracy in Natural Language Processing. This paper introduces a deep learning technique namely Deep Convolutional Neural Network which captures a sentiment in the text corpus. The performance of this model will be evaluated on the Amazon product reviews and IMDB movie reviews.