Abstract:
In recent years, micro-blogs on the Internet have become a popular way of
expressing feelings, thoughts, and even communicating opinions about products and
services that are common among its users. Twitter is one of the most widely used
social media micro-blogging sites with more than 326 million active users worldwide.
Twitter is a social networking service specifically for phenomena. Because of the
breadth and popularity, there are a large number of user reviews or opinions that are
generated and shared every day. Mining the opinions of users from social media data
is not an easy task; it can be achieved in various ways.
In this research, Lexicon-Based approach especially AFINN lexicon to be used
to classify user twitter sentiment, throughout which, twitter Micro-blogs data has
been collected, pre-processed analyzed, and classified. Results classify users'
perspective via tweets into positive and negative, which is represented in a pie chart
for Monthly report. Collecting user opinions can be an expensive and
time-consuming task using conventional methods such as surveys. The sentiment
analysis of the customer opinions makes it easier for businesses to understand their
competitive value in a changing market and to understand their customer views about
their products and services.
