More and more businesses are now leveraging on their knowledge of what the customers want through feedback and reviews to improve their business processes. Thus, the ability to capture and understand the customers fast is now the real game among enterprises. A lot of tools were already being used by large companies to acquire and monitor customer sentiments.<\/p>\n
In this post, we will try to understand how Natural Language Processing (NLP) can be used to analyze languages, specifically on movie reviews, and how businesses can use it to analyze the change in behavior of consumer preferences. We would be using an open-source tool called Empath.<\/p>\n
Empath is a tool to analyze text across lexical categories similar to Linguistic Inquiry and Word count (LIWC) – commonly used by researchers. It “analyzes text across 200 built-in, pre-validated categories generated from common topics in the web” [1]. It provides a numerical representation for each category as analyzed by content. It addresses the limitation of LIWC which only analyzes text across only 40 topics.<\/p>\n
We have managed to acquire compiled data of movie reviews from [2] which was first used by Pang and Lee in [3]. These are numerous film reviews from known film critic and personalities such as Dennis Schwartz, James Berardinelli, etc. that are pre-processed and stored in a .txt file format.<\/p>\n
Prepare Data<\/strong><\/p>\n We first load necessary libraries we need for our analysis and set up the list in which we will do the analysis on.<\/p>\n