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Sentiment-Driven Topic Analysis Of Song Lyrics

Electronic Theses of Indian Institute of Science

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Field Value
 
Title Sentiment-Driven Topic Analysis Of Song Lyrics
 
Creator Sharma, Govind
 
Subject Song Lyrics
Non-negative Matrix Factorization (NMF)
Music Information Retrival
Music Recommendation Engine
Support Vector Machine (SVM)
Naive Bayes Classifier (NBC)
Sentiment Analysis
Emotion Analysis
Latent Dirichlet Allocation (LDA)
Sentiment Clustering
Sentiment Classification
k-Nearest Neighbour Classi er (k-NNC)
Computer Science
 
Description Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons.
For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA). It mines topics from songs, which are nothing but probability distributions over the vocabulary of words. Some of the topics seem sentiment-based, motivating us to continue with this approach. We evaluate our clusters using a gold dataset collected from an apt website and get positive results. This approach would be useful in the absence of a supervisor dataset.
In another part of our work, we argue the inescapable existence of supervision in terms of having to manually analyse the topics returned. Further, we have also used explicit supervision in terms of a training dataset for a classifier to learn sentiment specific classes. This analysis helps reduce dimensionality and improve classification accuracy. We get excellent dimensionality reduction using Support Vector Machines (SVM) for feature selection. For re-classification, we use the Naive Bayes Classifier (NBC) and SVM, both of which perform well. We also use Non-negative Matrix Factorization (NMF) for classification, but observe that the results coincide with those of NBC, with no exceptions. This drives us towards establishing a theoretical equivalence between the two.
 
Contributor Narasimha Murty, M
 
Date 2015-08-17T11:29:41Z
2015-08-17T11:29:41Z
2015-08-17
2012-08
 
Type Thesis
 
Identifier http://etd.iisc.ernet.in/handle/2005/2472
http://etd.ncsi.iisc.ernet.in/abstracts/3190/G25269-Abs.pdf
 
Language en_US
 
Relation G25269