Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification

TL;DR

This paper proposes a text mining architecture utilizing Naive Bayes classification to analyze public sentiments from Twitter data regarding the West Java governor election, emphasizing preprocessing steps for effective sentiment classification.

Contribution

It develops a generalized text mining architecture for sentiment analysis from Twitter data, tailored for election-related public opinion, incorporating specific preprocessing techniques.

Findings

Preprocessing steps like cleansing, case folding, POS tagging, and stemming are essential.

The architecture effectively classifies opinions into positive or negative.

Twitter opinion mining is a viable application of text mining techniques.

Abstract

The selection of West Java governor is one event that seizes the attention of the public is no exception to social media users. Public opinion on a prospective regional leader can help predict electability and tendency of voters. Data that can be used by the opinion mining process can be obtained from Twitter. Because the data is very varied form and very unstructured, it must be managed and uninformed using data pre-processing techniques into semi-structured data. This semi-structured information is followed by a classification stage to categorize the opinion into negative or positive opinions. The research methodology uses a literature study where the research will examine previous research on a similar topic. The purpose of this study is to find the right architecture to develop it into the application of twitter opinion mining to know public sentiments toward the election of the governor of west java. The result of this research is that Twitter opinion mining is part of text mining where opinions in Twitter if they want to be classified, must go through the preprocessing text stage first. The preprocessing step required from twitter data is cleansing, case folding, POS Tagging and stemming. The resulting text mining architecture is an architecture that can be used for text mining research with different topics.