Speech sentiment analysis - how, why, and why not

What exactly is speech sentiment analysis?

Speech sentiment analysis is a technology that gauges the emotional tone of spoken language by analyzing tone of voice, word choice, and other linguistic cues. In the BPO industry, it's used to analyze customer interactions, helping companies promptly address concerns and improve satisfaction. But it's not just about customers; sentiment analysis is also valuable for evaluating agent performance. By analyzing agent interactions, BPO companies gain insights into individual strengths and weaknesses. Low sentiment scores might indicate a need for additional training, while high scores could signify exceptional communication skills. Analyzing trends in sentiment scores can also reveal broader patterns, such as times of day or types of calls that consistently result in negative sentiment. In essence, speech sentiment analysis acts as a diagnostic tool for BPO companies, allowing them to delve into the emotions behind interactions. By understanding these nuances in both customer and agent communication, companies can make informed decisions to improve customer satisfaction, enhance agent performance, and drive business success.

How does it work?

The process is very similar to the way we described general sentiment analysis in our previous blog with a few notable differences seeing as the starting medium is different. So, the four phases of speech sentiment analysis are:

• Speech recognition: The analysis begins with converting spoken language into text using automatic speech recognition (ASR) technology. ASR transcribes the audio, capturing the words and phrases spoken by both customers and agents.
• Text preprocessing: The transcribed text undergoes preprocessing to clean and normalize it. This involves removing filler words, correcting errors, and standardizing language to ensure accurate analysis.
• Sentiment analysis: Once the text is preprocessed, sentiment analysis algorithms are applied. These algorithms use various techniques, such as lexicon-based methods, machine learning models, or a combination of both, to identify and quantify the emotional tone present in the text. They look for specific words, phrases, and patterns associated with different emotions, assigning sentiment scores to the text segments.
• Reporting: The sentiment scores obtained from the analysis are then aggregated and analyzed to derive meaningful insights. These insights can be presented in various formats, such as reports, dashboards, or visualizations, allowing stakeholders to easily understand the emotional trends and patterns within the analyzed conversations.

Where would you apply speech sentiment analysis?

We see now why and how understanding the emotional undercurrents of spoken language has become increasingly valuable for businesses seeking to refine their strategies and strengthen customer relationships. Speech sentiment analysis, as we've explored, provides a powerful tool for decoding these nuanced emotions. But where exactly can this technology be applied to reap the most significant benefits? Here are two of our most impactful examples: training and QA.