Automatic audio sentiment extraction using keyword spotting

Most existing methods for audio sentiment analysis use automatic speech recognition to convert speech to text, and feed the textual input to text-based sentiment classifiers. This study shows that such methods may not be optimal, and proposes an alternate architecture where a single keyword spotting system (KWS) is developed for sentiment detection. In the new architecture, the text-based sentiment classifier is utilized to automatically determine the most powerful sentiment-bearing terms, which is then used as the term list for KWS. In order to obtain a compact yet powerful term list, a new method is proposed to reduce text-based sentiment classifier model complexity while maintaining good classification accuracy. Finally, the term list information is utilized to build a more focused language model for the speech recognition system. The result is a single integrated solution which is focused on vocabulary that directly impacts classification. The proposed solution is evaluated on videos from YouTube.com and UT-Opinion corpus (which contains naturalistic opinionated audio collected in real-world conditions). Our experimental results show that the KWS based system significantly outperforms the traditional architecture in difficult practical tasks.