Simple Text Mining for Sentiment Analysis of Political Figure Using Naive Bayes Classifier Method
This work addresses sentiment analysis for political monitoring, but it is incremental as it applies an existing method to a specific domain.
The paper tackled sentiment analysis of political figures from digital news articles using a Naive Bayes classifier, achieving promising results in classifying positive or negative sentiment.
Text mining can be applied to many fields. One of the application is using text mining in digital newspaper to do politic sentiment analysis. In this paper sentiment analysis is applied to get information from digital news articles about its positive or negative sentiment regarding particular politician. This paper suggests a simple model to analyze digital newspaper sentiment polarity using naive Bayes classifier method. The model uses a set of initial data to begin with which will be updated when new information appears. The model showed promising result when tested and can be implemented to some other sentiment analysis problems.