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How to Analyze Call Sentiment With Open-Source NLP Libraries
This story was originally published on HackerNoon at: https://hackernoon.com/how-to-analyze-call-sentiment-with-open-source-nlp-libraries.
Unlock call sentiment analysis using open-source NLP. Discover how to analyze customer emotions, improve service, and gain valuable insights from voice data.
Check more stories related to data-science at: https://hackernoon.com/c/data-science.
You can also check exclusive content about #nlp, #natural-language-processing, #call-sentiment, #open-source-nlp, #customer-service, #call-sentiment-analysis, #ai-for-customer-support, #sentiment-analysis, and more.
This story was written by: @devinpartida. Learn more about this writer by checking @devinpartida's about page,
and for more stories, please visit hackernoon.com.
Call sentiment analysis uses natural language processing (NLP) to surface those signals at scale. Sentiment signals often fall into three broad categories: polarity, intensity and temporal shifts. When applied across large call volumes, sentiment metrics reveal systemic trends that individual call reviews rarely uncover.
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By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/how-to-analyze-call-sentiment-with-open-source-nlp-libraries.
Unlock call sentiment analysis using open-source NLP. Discover how to analyze customer emotions, improve service, and gain valuable insights from voice data.
Check more stories related to data-science at: https://hackernoon.com/c/data-science.
You can also check exclusive content about #nlp, #natural-language-processing, #call-sentiment, #open-source-nlp, #customer-service, #call-sentiment-analysis, #ai-for-customer-support, #sentiment-analysis, and more.
This story was written by: @devinpartida. Learn more about this writer by checking @devinpartida's about page,
and for more stories, please visit hackernoon.com.
Call sentiment analysis uses natural language processing (NLP) to surface those signals at scale. Sentiment signals often fall into three broad categories: polarity, intensity and temporal shifts. When applied across large call volumes, sentiment metrics reveal systemic trends that individual call reviews rarely uncover.