- I. Cervesato, Kemal Oflazer, Mark Stehlik, Khaled A. Harras, Houda Bouamor, Alexander R. W. Cheek, Sabih Bin Wasi. None. 2015 Senior Thesis Project Reports State-of-the-art, Real-time, Interactive Twitter Sentiment Analysis Using Machine Learning . . . . . 1 State-of-the-art, Real-time, Interactive Twitter Sentiment Analysis Using Machine Learning Author Sabih Bin Wasi Table of Contents. Abstract: This technical report collects the final reports of the undergraduate Computer Science majors from the Qatar Campus of Carnegie Mellon University who elected to complete a senior research thesis in the academic year 2014–15 as part of their degree. These projects have spanned the students' entire senior year, during which they have worked closely with their faculty advisors to plan and carry out their projects. This work counts as 18 units of academic credit each semester. In addition to doing the research, the students presented a brief midterm progress report each semester, presented a public poster session in December, presented an oral summary in the year-end campus-wide Meeting of the Minds and submitted a written thesis in May. work and helped me achieve the high system performance on time. Also, I am grateful to my friends Rukhsar Neyaz, Afroz Aziz and Abdullah Zafar for motivating me throughout. This work wouldn't have been possible without the support of my parents who allowed me to fly away from home and always encouraged me to participate in research activities. Abstract With more than 500M tweets sent per day containing opinions and messages, Twitter has become a gold­mine for organizations to monitor their brand reputations and analyze their performance and public sentiments about their products. In this work, we present our efforts in building a machine learning based system for sentiment analysis of Twitter messages. Our system takes as input an arbitrary tweet and assigns it to one of the following classes that best reflects its sentiment: positive, negative or neutral. We adopt a supervised text classification approach and use a combination of published­proven features and novel features. Our system outperforms the state­of­the­art systems, especially those tested on the SemEval 2014 testset. Using our prediction model, we also build an interactive visualization tool that uses standard design principles to provide interpretation and trust to the big­data fed into the system. Our system can handle Twitter sentiment analysis of millions of Tweets in pseudo real­time. Furthermore we give an extra emphasis to the adaptive nature of the tool in order to enable brand managers dig deep into patterns found in visualizations ­­ resulting in effective public sentiment monitoring on social media.
