Detecting human emotions on Facebook comments

Abstract

Human emotion detection plays a vital role in interpersonal relationships. From the early eras, automatic recognition of emotions has been an active research topic. Today, sharing emotions on social media is one of the most popular activities among internet users. However, when it comes to a specific domain like emotion detection in social media, it is still on a research-level. There are less number of applications have been developed to detect emotions online, using online comments and user comments. The aim of this research is to develop a system that identifies human emotions on Facebook comments. Among the different social media platforms, this research specifically focuses on Facebook comments written in the English language to narrow down the problem. The research is based on Semantic analysis, which comes under Natural Language Processing (NLP) and the system development consists of four major steps, including the extraction of Facebook comments via Graph API, preprocessing, classification and emotion detection. To classify the emotions, a classification model was created by using Naïve Bayes Algorithm. When it comes to marketing, emotions are what lead your onlookers to purchase. By using the detected emotions, marketers can promote their campaigns by changing online advertisements dynamically. The results obtained through testing the system show that it is capable of accurately identifying human emotions hidden in Facebook comments with an accuracy level of 80%, making it highly useful for marketing purposes.