The music industry is a $130 billion industry. Predicting whether a song catches the pulse of the audience impacts the industry. In this paper we analyze language inside the lyrics of the songs using several computational linguistic algorithms and predict whether a song would make to the top or bottom of the billboard rankings based on the language features. We trained and tested an SVM classifier with a radial kernel function on the linguistic features. Results indicate that we can classify whether a song belongs to top and bottom of the billboard charts with a precision of 0.76.
We present in this paper the design of DeepTutor, the first dialoguebased intelligent tutoring system based on Learning Progressions, and its implications for developing the Generalized Framework for Intelligent Tutoring. We also present the design of SEMILAR, a semantic similarity toolkit, that helps researchers investigate and author semantic similarity models for evaluating natural language student inputs in conversatioanl ITSs. DeepTutor has been developed as a web service while SEMILAR is a Java library. Based on our experience with developing DeepTutor and SEMILAR, we contrast three different models for developing a standardized architecture for intelligent tutoring systems: (1) a single-entry web service coupled with XML protocols for queries and data, (2) a bundle of web services, and (3) library-API. Based on the analysis of the three models, recommendations are