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Text Analytics vs Sentiment Analysis: Key Differences & Applications

SurveySensum

Amongst many in the market, two techniques stand out Text analysis and Sentiment Analysis. What is Sentiment Analysis? Sentiment analysis , also called opinion mining, is a specialized form of text analysis that focuses on detecting the emotional tone behind a piece of text. What They Analyze?

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Sentiment Analysis: Guide to Boost CX & Drive Business Insights

SurveySensum

With the right tools and techniques, analyzing your survey data can reveal not just what your customers are saying, but how they truly feel about your products, services, and brand as a whole. Thats where sentiment analysis comes in – turning raw feedback into actionable insights. What is Sentiment Analysis?

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Unstructured Data: Mining the Gold

InteractionMetrics

Unstructured data presents a goldmine of information, but mining that gold is no easy task—it requires coding with detailed text analysis. To be clear, unstructured data includes customer survey text comments, customer service calls, emails, chats, reviews, and other narrative sources of information.

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Lesson #3 Revisited: AI and the Quest for a Single Source of Truth in CX Feedback

PeopleMetrics

Sentiment Analysis (Happening) AI-powered sentiment analysis helps companies gauge customer emotions through unstructured data (open ended comments) across feedback channels. These programs flag issues from unstructured customer feedback and summarize key themes and sentiment in real-time.

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Focus on Customer Experience Management to Improve Customer Retention

SurveySensum

Future is demanding and present era is flooded with unstructured data from various online or offline sources! For this reason, there is a growing need for technology that can break the complex and comprehensive customer feedback data into simple and useable insights.

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Top 5 Text Analytics Tools: Features, Benefits, and Use Cases

SurveySensum

In simple terms, text analytics tools leverage machine learning, NLP, and other AI capabilities to break down unstructured data from customer feedback, online reviews, customer support chat, etc. This helps extract meaningful insights from the feedback by identifying recurring patterns, themes, and sentiments.

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How Artificial Intelligence and Predictive Analytics Can Help You Reduce Customer Churn

CSAT.AI

Revuze’s software utilizes NLP, a form of artificial intelligence, and computational linguistics to filter for words that reveal customer attitudes and emotion, which are revealed in a sentiment analysis report. The second challenge lies in whether it’s structured or unstructured data.