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.
@cite_2 used LIWC and surface music components of all the phrases present in a small collection of songs as a dataset for identifying the emotions in that phrase. Each of the phrases was annotated for emotions. Using SVM classifier they obtained an accuracy of 0.87 using just the language features. They observed that the language components gave a higher accuracy than music features in predicting emotions. The accuracy is higher as they are looking at emotions in a phrase, where the chance of having multiple emotions inside such a small text is very low.
@cite_2
2403416428
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