Paper Info
Title
Can prosody inform sentiment analysis? Experiments on short spoken reviews
Abstract
While most online content is created using textual interfaces, recent improvements in speech recognition accuracy allows the creation of content through speech. This technology allows users to share reviews about entities of interest without any delay, using mobile devices. This paper builds on the previous work on textual sentiment analysis to investigate whether information in the speech signal can be used to predict sentiment from short spoken reviews. For this purpose we collected a short spoken reviews from 84 speakers. Results show that models trained on features characterizing the review's pitch significantly outperform a majority class baseline, without textual information. When taking text-based sentiment predictions into account, our results suggest that prosody can alleviate the effect of speech recognition errors on sentiment detection, however a larger dataset is needed to test whether this can be done without harming performance on low word error rates.
Year
DOI
Venue
2012
10.1109/ICASSP.2012.6289066
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
prediction theory,signal detection,speech recognition,delay,low word error rate,majority class baseline,mobile device,prosody,sentiment detection,short spoken review,speech recognition,speech signal information,text-based sentiment prediction,textual information,textual interface,textual sentiment analysis,opinion mining,prosody,sentiment analysis
Prosody,Pattern recognition,Detection theory,Computer science,Textual information,Sentiment analysis,Speech recognition,Feature extraction,Mobile device,Natural language processing,Artificial intelligence,Hidden Markov model
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4673-0044-5
978-1-4673-0044-5
14
PageRank 
References 
Authors
0.68
10
3
Name
Order
Citations
PageRank
François Mairesse186248.29
Joseph Polifroni222144.97
Giuseppe Di Fabbrizio333044.45