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Project ID: 431
Author: Ming-Hsuan Wu
Project Title: Using Clustering and Sentiment Analysis on Twitter
Semester: 1 2014
Committe Chair: Dr. Longzhuang Li
Committee Member 1: Dr. David Thomas
Project Description: Recently, social media has become important for social networking and content sharing. Twitter, an online social network, allows users to upload short text messages, also known as tweets, with up to 140 characters. A lot of people use sentiment analysis on Twitter to do opinion mining. People choose Twitter because Twitter serves as a good platform for sentiment analysis because of its large user base from different sociocultural zones. The objective of Sentiment Analysis is to identify any clue of positive or negative emotions in a piece of text reflective of the authors’ opinions on a subject. Twitter API, twitter4j, is processed to search selected popular electronic products on Twitter. K-means cluster approach is used to find some clusters that have similar sentences. Similar sentence means the sentences have the same keywords. It means the tweets in the cluster are about how people think about similar features of selected popular electronic products. Each cluster is entered into feature-based sentiment analysis to get the score. After that, the total tweets also process in the sentiment analysis system to analyze how people think about selected popular electronic products. The system uses TF-IDF, k-means algorithm, SentiWordNet and Stanford tool to handle different level steps.
Project URL:   431.pdf