Data analysis on music classification system and creating a sentiment word dictionary for Kokborok language

Abstract

This work shows the development of a lexicon for a poorly resourced language, namely Kokborok. Kokborok is a regional language of North East India and offers an entirely new base for research in music information retrieval (MIR) field. We first create a sentimental word dictionary known as lexicons to develop a polarity classification system. It is a text analysis work involving two types of lyrical features that are: ‘text stylistic feature’, and the features were taken out from the newly developed dictionary. We have also shown the comparative analysis with a various subset of music database based on their accuracy rate. After the system development, the experimental/simulations were done, and the results have been computationally analyzed. We performed linear extrapolation of the data taken by both the feature set, thus developing a dictionary. Text stylistic (TS) features have been observed to converge, at 52 and 39 percent respectively for the number of songs tending to infinity. It has been found that at present, it might be better to increase the features from the dictionary since it gives better accuracy for low resource language Kokborok.