Sentiment analysis using relative prosody features

Recent improvement in usage of digital media has led people to share their opinions about specific entity through audio. In this paper, an approach to detect the sentiment of an online spoken reviews based on relative prosody features is presented. Most of the existing systems for audio based sentim...

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Bibliographic Details
Main Authors: Abburi, Harika, Alluri, K. N. R. K. Raju, Vuppala, Anil Kumar, Shrivastava, Manish, Gangashetty, Suryakanth V.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Feature extraction
Mel frequency cepstral coefficient
Sentiment analysis
Speech
Support vector machines
Online Access:Request full text
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Summary:Recent improvement in usage of digital media has led people to share their opinions about specific entity through audio. In this paper, an approach to detect the sentiment of an online spoken reviews based on relative prosody features is presented. Most of the existing systems for audio based sentiment analysis use conventional audio features, but they are not problem specific features to extract the sentiment. In this work, relative prosody features are extracted from normal and stressed regions of audio signal to detect the sentiment. Stressed regions are identified using the strength of excitation. Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers are used to build the sentiment models. MOUD database is used for the proposed study. Experimental results show that, the rate of detecting the sentiment is improved with relative prosody features compared with the prosody and Mel Frequency Cepstral Coefficients (MFCC) because the relative prosody features has more sentiment specific discrimination compared to prosody features.
ISSN:2572-6129
DOI:10.1109/IC3.2017.8284296