How To Train a Neural Network for Sentiment Analysis

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Introduction

Sentiment analysis, strongly related to text mining and natural language processing, extracts qualitative assessment from written reviews. Many people read movie reviews to assess how good a movie seems to be among the general population. While assigning a number or star rating to a film may not indicate its quantitative success or failure, a collection of film reviews offers a qualitative perspective on these films. A textual movie review can identify what viewers believe to be the film’s good and poor elements. A more in-depth examination of the review will often reveal if the film lives up to the reviewer’s expectations. Sentiment analysis can be used to assess the reviewer’s perspective on subjects or the overall polarity of the review.

To conduct sentiment analysis, you would run a computational program to recognize and categorize opinions in a piece of text, such as to discern whether the writer (or reviewer) has a positive or negative attitude towards a given topic (in this case, a film). As a sub-domain of opinion mining, sentiment analysis focuses on extracting emotions and opinions towards a particular topic from structured, semi-structured, or unstructured textual data. As with other opinion mining models, you might use sentiment analysis to monitor brand and product opinions and to understand customer needs. Sentiment analysis focuses on the polarity of a text (positive, negative, or neutral), as well as detecting specific feelings and emotions of the reviewer (angry, happy, sad, and so on as defined by the model), urgency, and even intentions (interested or not interested).

In this tutorial, you will build a neural network that predicts the sentiment of film reviews with keras. Your model will categorize the reviews into two categories (positive or negative) using the International Movie Database (IMDb) review dataset, which contains 50,000 movie reviews. By the end of this tutorial, you will have created a deep learning model and trained a neural network to perform sentiment analysis.