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_1 implemented a multimodal music emotion classification (MEC) for classifying 14 kinds of emotions from music and song lyrics of western music genre. Their dataset consisted of 3500 songs with emotions mood such as sad, high, groovy, happy, lonely, sexy, energetic, romantic, angry, sleepy, nostalgic, funny, jazzy, and calm. They used AdaBoost with decision stumps for classification of the music and language features of the lyrics into their respective emotion categories. They have an accuracy of 0.78 using language as well as surface features of the audio. The authors claim that the language features played a more important role compared to the music features in classification.
@cite_1
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AutoTutor simulates a human tutor by holding a conversation with the learner in natural language. The dialogue is augmented by an animated conversational agent and three-dimensional (3-D) interactive simulations in order to enhance the learner's engagement and the depth of the learning. Grounded in constructivist learning theories and tutoring research, AutoTutor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition. The computational architecture of the system uses the .NET framework and has simplified deployment for classroom trials.