Lyrics mood prediction using Decision Tree algorithm in python

 

Lyrics mood prediction using Decision Tree algorithm

 

 PROJECT ID: PYTHON16

 

PROJECT NAME: Lyrics mood prediction using Decision Tree algorithm

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

This paper presents a case-study of the effectiveness of a trained system into classifying Greek songs according to their audio characteristics or/and their lyrics into moods. We examine how the usage of different algorithms, featureset combinations and pre-processing parameters affect the precision and recall percentages of the classification process for each mood model characteristic. Experimental results indicate that the current selection of features offers accuracy results, the superiority of lyrics content over generic audio features as well as potential caveats with current research in Greek language stemming pre-processing methods.

Related Work

Research in mood detection and classification in musical pieces has received extensive attention during most of the last decade, while since 2007, the Music Information Retrieval Evaluation eXchange (MIREX) evaluation campaign [7] additionally hosts the “Audio Music Mood Classification” task. In this section, we present some of the key assumptions in mood modeling as well as related works in songs’ mood classification.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Experimental Setup

The dataset utilised in this work consist of 943 Greek songs from various genres that include lyrics collected from several sources. The annotation of the dataset with the labels of the mood model selected was made by manual appointment.

Experiments were run using the Weka Machine Learning Workbench [18]. Several learning algorithms were experimented with for investigative purposes. The classifiers for the performed experiments are the ones below. The Naïve Bayes classifier, a probabilistic algorithm which applies the Bayes’ theorem with strong (naive) independence assumptions. The J48, an algorithm which generates an unpruned or pruned C4.5 decision tree. The IBk, a K-nearest neighbors classifier, using 5-13 neighbors. The Random Forest, a method based on bagging models built using the Random Tree method, in which classification trees are grown on a random subset of descriptors, using 50-80 trees. Support Vector Machines (SVMs) [19] were also experimented with, due to their ability to deal 6 Mood Classification using Lyrics and Audio: A Case-study in Greek Music efficiently with few data and high dimensional spaces, both valid properties of the data used in the current approach. Experiments were run using first degree polynomial kernel functions and Platt’s Sequential Minimal Optimization (SMO) algorithm for training

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

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