AI-Powered Text Analysis: unlocking sentiment, emotions, and actionable feedback

Unlock valuable insights from unstructured customer feedback with AI-powered tools

Enhance sentiment understanding, identify pain points, and drive data-informed decisions

Save time, reduce bias, and gain actionable insights effortlessly.

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Unlocking valuable insights

Text analysis is a powerful tool that can unlock valuable insights from customer feedback, helping businesses understand customer sentiment, identify pain points, and make data-driven decisions. With the integration of AI, text analysis capabilities have been further enhanced to deliver even greater benefits. Qualtrics’ AI-powered solutions, Text iQ, can help manage human bias in data interpretation, automate the analysis of open-ended feedback, and ultimately save staff time while providing more accurate and actionable insights for businesses.

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Qualtrics’ enriched Text iQ capabilities

AI-powered text analysis tools like Text iQ offer users a wealth of valuable benefits, including the ability to extract detailed topic hierarchies and gain a deeper understanding of sentiment, actionability, emotions, and the effort associated with feedback. By leveraging AI technology, users can unlock valuable insights from unstructured data, such as customer feedback, to identify pain points, make data-driven decisions, and mitigate human bias in data interpretation.

Text iQ features

Some of the more notable capabilities offered by Text iQ include:

Sentiment Analysis: assess the overall sentiment (positive, negative, neutral) expressed in the text data

Actionability: the ability to label specific comments, indicating whether they require action (e.g., a response is needed) or include suggestions for improvement

Effort Analysis: examination of responses in terms of the level of ‘effort’ exerted by the respondent. Notably, high effort is often correlated with customer churn

Emotion Labelling: categorisation of responses into desirable emotions (e.g., joy, love, happiness), undesirable emotions (e.g., anger, confusion, disgust, disappointment), or context-dependent emotions (which may be positive or negative depending on the situation, such as surprise or anticipation)

Emotional Intensity: expressing emotional intensity as Low, Medium, or High, providing further context to any emotion expressed in the responses

Hierarchical Topic Modelling: the system has been enhanced to include organise topics into a hierarchical structure of up to 5 levels, as opposed to the current two levels. This means that broader categories can have subcategories, providing a more detailed and organised view of the themes within the text data.

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