AUDIO SENTIMENT ANALYSIS

Abstract

: Audio sentiment analysis using automatic speech recognition is an emerging research area where opinion or sentiment exhibited by a speaker is detected from natural audio. It is relatively under-explored when compared to text based sentiment detection. Extracting speaker sentiment from natural audio sources is a challenging problem. Generic methods for sentiment extraction generally use transcripts from a speech recognition system, and process the transcript using text-based sentiment classifiers. In this study, we show that this baseline system is sub-optimal for audio sentiment extraction. Alternatively, new architecture using keyword spotting (KWS) is proposed for sentiment detection. In the new architecture, a text-based sentiment classifier is utilized to automatically determine the most useful and discriminative sentiment-bearing keyword terms, which are then used as a term list for KWS. In order to obtain a compact yet discriminative sentiment term list, iterative feature optimization for maximum entropy sentiment model is proposed to reduce model complexity while maintaining effective classification accuracy. A new hybrid ME-KWS joint scoring methodology is developed to model both text and audio based parameters in a single integrated formulation. For evaluation, two new databases are developed for audio based sentiment detection, namely, YouTube sentiment database and another newly developed corpus called UT-Opinion Opinion audio archive. These databases contain naturalistic opinionated audio collected in real world conditions. The proposed solution is evaluated on audio obtained from videos in youtube.com and UT-Opinion corpus. Our experimental results show that the proposed KWS based system significantly outperforms the traditional ASR architecture in detecting sentiment for challenging practical tasks.