Analysis of Affective Speech Signals for Emotion Extraction and Attitude Prediction

Abstract: Sentiment Analysis for Attitude Prediction is one of most emerging and globally accepted technique used in business intelligence. In many business intelligence applications, the huge amounts of telephone quality speech samples or telephone calls recorded by Business Process Organizations (BPOs) are processed for emotion extraction and attitude prediction. In this paper, we are using SVM (Support Vector Machine) as a classifier. Through emotion extraction and attitude prediction, customer opinions, various trends are mined, which helps in decision making for corporate industries. In this paper, the framework analysis of affective speech for emotion extraction and attitude prediction is proposed. The rigorous review of similar kind of existing systems is taken followed by the proposed framework.

Keywords: Emotion extraction; sentiment analysis; speech signal; business intelligence.