University of Illinois Urbana-Champaign

Multimodal Sentiment Analysis of Songs Using Ensemble Classifiers

Gomez-Saavedra, Esteban

Content Files
ECE499-Sp2015-gomez-saavedra.pdf

Permalink

https://hdl.handle.net/2142/79038

Description

Title
Multimodal Sentiment Analysis of Songs Using Ensemble Classifiers
Author(s)
Gomez-Saavedra, Esteban
Contributor(s)
Do, Minh N.
Issue Date
2015-05
Keyword(s)
  • Music Information Retrieval
  • Sentiment Analysis
  • Multimodal Classification
  • Classification Algorithms
  • Multimodal Fusion
Date of Ingest
2015-08-13T16:08:52Z
Abstract
We consider the problem of performing sentiment analysis on songs by combining audio and lyrics in a large and varied dataset, using the Million Song Dataset for audio features and the MusicXMatch dataset for lyric information. The algorithms presented on this thesis utilize ensemble classifiers as a method of fusing data vectors from different feature spaces. We find that multimodal classification outperforms using only audio or only lyrics. This thesis argues that utilizing signals from different spaces can account for interclass inconsistencies and leverages class-specific performance. The experimental results show that multimodal classification not only improves overall classification, but is also more consistent across different classes.
Type of Resource
text
Genre of Resource
Other
Language
en
Permalink
http://hdl.handle.net/2142/79038

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