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