Sentiment extraction from natural audio streams

Automatic sentiment extraction for natural audio streams containing spontaneous speech is a challenging area of research that has received little attention. In this study, we propose a system for automatic sentiment detection in natural audio streams such as those found in YouTube. The proposed technique uses POS (part of speech) tagging and Maximum Entropy modeling (ME) to develop a text-based sentiment detection model. Additionally, we propose a tuning technique which dramatically reduces the number of model parameters in ME while retaining classification capability. Finally, using decoded ASR (automatic speech recognition) transcripts and the ME sentiment model, the proposed system is able to estimate the sentiment in the YouTube video. In our experimental evaluation, we obtain encouraging classification accuracy given the challenging nature of the data. Our results show that it is possible to perform sentiment analysis on natural spontaneous speech data despite poor WER (word error rates).