Sentiment Analysis on Speaker Specific Speech Data
This work addresses the problem of audio sentiment analysis for conversations, which is incremental as it builds on existing text-based methods by incorporating speaker discrimination.
The paper tackles sentiment analysis on speaker-specific speech data by performing sentiment analysis on speaker-discriminated speech transcripts to detect individual speakers' emotions in conversations, analyzing different techniques for speaker discrimination and sentiment analysis to find efficient algorithms.
Sentiment analysis has evolved over past few decades, most of the work in it revolved around textual sentiment analysis with text mining techniques. But audio sentiment analysis is still in a nascent stage in the research community. In this proposed research, we perform sentiment analysis on speaker discriminated speech transcripts to detect the emotions of the individual speakers involved in the conversation. We analyzed different techniques to perform speaker discrimination and sentiment analysis to find efficient algorithms to perform this task.