Analyzing Movie Reviews Sentiment

Abstract

In this chapter, we continue with our focus on case-study oriented chapters, where we will focus on specific real-world problems and scenarios and how we can use Machine Learning to solve them. We will cover aspects pertaining to natural language processing (NLP), text analytics, and Machine Learning in this chapter. The problem at hand is sentiment analysis or opinion mining, where we want to analyze some textual documents and predict their sentiment or opinion based on the content of these documents. Sentiment analysis is perhaps one of the most popular applications of natural language processing and text analytics with a vast number of websites, books and tutorials on this subject. Typically sentiment analysis seems to work best on subjective text, where people express opinions, feelings, and their mood. From a real-world industry standpoint, sentiment analysis is widely used to analyze corporate surveys, feedback surveys, social media data, and reviews for movies, places, commodities, and many more. The idea is to analyze and understand the reactions of people toward a specific entity and take insightful actions based on their sentiment. A text corpus consists of multiple text documents and each document can be as simple as a single sentence to a complete document with multiple paragraphs. Textual data, in spite of being highly unstructured, can be classified into two major types of documents. Factual documents that typically depict some form of statements or facts with no specific feelings or emotion attached to them. These are also known as objective documents. Subjective documents on the other hand have text that expresses feelings, moods, emotions, and opinions. Sentiment analysis is also popularly known as opinion analysis or opinion mining. The key idea is to use techniques from text analytics, NLP, Machine Learning, and linguistics to extract important information or data points from unstructured text. This in turn can help us derive qualitative outputs like the overall sentiment being on a positive, neutral, or negative scale and quantitative outputs like the sentiment polarity, subjectivity, and objectivity proportions. Sentiment polarity is typically a numeric score that's assigned to both the positive and negative aspects of a text document based on subjective parameters like specific words and phrases expressing feelings and emotion. Neutral sentiment typically has 0 polarity since it does not express and specific sentiment, positive sentiment will have polarity > 0, and negative < 0. Of course, you can always change these thresholds based on the type of text you are dealing with; there are no hard constraints on this. In this chapter, we focus on trying to analyze a large corpus of movie reviews and derive the sentiment. We cover a wide variety of techniques for analyzing sentiment, which include the following. • Unsupervised lexicon-based models • Traditional supervised Machine Learning models • Newer supervised Deep Learning models • Advanced supervised Deep Learning models