Sentiment Analysis of Text and Emoji using Machine Learning Algorithms

A major form of communication after the speech is text. Unlike direct communication through a speech where the tone can be analyzed depending on the pitch and frequency, text analysis is based on the frequency of words in a particular text. A recent survey conducted shows that around 80% of business data is in textual format. All industry and business users including call centers, online reviews, customer surveys, etc., work with raw textual data. This untapped data when mined and analyzed can provide great insights to the organization. Consumers are interested in other’s opinions and experiences. Statistics reveal that at least 90% of people are influenced by what they read and hence the negative comments take a huge toll. Over the recent years, sites have started collecting customer reviews for their products. Companies use ‘Text Analysis’ to manage their content. Textual data when converted into data slices that are easy to automate, processes like product development, decision making, marketing optimization, and business intelligence take place easily. When the text analysis process is automated, it helps analyze large chunks of data within a short period of time and with lesser resources when compared to manual analysis. Automated text analysis is also proven to be faster and scalable along with consistent results. The purpose of this work is to develop a text analysis algorithm that will classify text into either one of the three categories: Positive, Negative, or Neutral. Once the received text is cleaned and pre-processed, it can be classified into one of the categories based on classification techniques like Naive Bayes, Support Vector Machine, etc., although it is observed that the Naive Bayes approach produces better accuracy. Similar to text, emoji can also be converted to its respective textual sequence and then classified.