Recognizing Language and Emotional Tone from Music Lyrics using IBM Watson Tone Analyzer

Music has a soothing impact on listener's mood and emotional states. Apart from the rhythm, sequence, instrumental effects on a song, lyrics could be considered as the most vital element. Lyricists' mood and affection towards a song while writing could be understand from the lyrics. Lyrics does have the elements of fictions such as language tone, language style, diction and voice are well maintained in music lyrics. Understanding the tone of a song both language and emotional tones are essential to develop different interactive applications. Music players, video repositories, video sharing sites could use the understandings to recommend next song to play according to the music interest or mood of the listeners. In this paper, we have investigated the possibilities to use IBM Watson Tone Analyzer, an API service to analyze language and emotional tones from song lyrics. We have extracted the features from a 300 English song dataset using the supported API service and formulated a machine learning methodology to classify the language tone (analytical, confident and tentative) and emotional tone (anger, fear, joy and sadness). For classification, we have applied different classifiers including Naïve Bayes, decision tree, random forest, sequential minimal optimization and simple logistic regression.

I. Introduction

Music industry is booming large because of the easy and free access in social networking sites such as Facebook, Twitter, LinkedIn, Google+ etc., social messaging applications such as WhatsApp, Line, WeChat, Snapchat etc., and video-sharing sites such as YouTube, Vimeo, Amazon Prime Videos, Metacafe, MySpace Videos etc. and music sharing applications such as iTunes, Google Play Music etc. Because of the availabilities of these mediums different culture and language songs could be found at one platform. Due to huge volume of music production listeners are getting biased or mood swings while listening different genre songs. Thus lyrics become the most influential part of a song apart from the tune, rhythm, fusion, singer or genre. As music has a soothing impact on listener’s mood and emotional states, choice of music according to the emotion and continuously playing songs of the same emotional state is an important automated task. Lyrics could play a vital role in this regard to understand the tone of the music for a particular song. Finding emotional features in a song lyrics to classify it as one of the emotional states automatically is a challenging task.