Sentiment Analysis
The use of natural language processing to automatically determine whether a piece of customer feedback expresses positive, negative, or neutral sentiment.
Sentiment analysis uses natural language processing (NLP) and machine learning to automatically classify text-based feedback as positive, negative, or neutral. It enables organizations to process thousands or millions of customer comments at scale, turning unstructured text into quantifiable data.
Modern sentiment analysis goes beyond simple positive/negative classification. Advanced models detect sentiment intensity (slightly negative vs extremely negative), aspect-based sentiment (positive about the product but negative about support), and emotional tone (frustrated, delighted, confused, grateful).
In customer feedback programs, sentiment analysis is applied to open-ended survey responses, support ticket conversations, social media mentions, app store reviews, and any other text-based customer communication. It complements quantitative metrics like NPS and CSAT by revealing the "why" behind the numbers.
The most actionable sentiment analysis is paired with theme extraction. Knowing that 30% of comments are negative is useful; knowing that negative comments are primarily about "slow load times" and "confusing pricing" is actionable. AI-powered analysis tools can perform both sentiment and theme analysis simultaneously.
Related Terms
Text Analytics
AnalysisThe broad discipline of extracting meaningful insights from unstructured text data, including sentiment, themes, intent, and urgency.
Theme Extraction (Topic Modeling)
AnalysisThe automated process of identifying recurring topics and themes across large volumes of customer feedback text.
Verbatim Analysis
AnalysisThe process of reviewing and analyzing customers’ exact words from open-ended survey responses and other text-based feedback.
Voice of the Customer (VoC)
MethodologyA research methodology that captures customers’ expectations, preferences, and aversions through direct and indirect feedback channels.
Related Resources
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