Open Text Analysis

Open-ended responses often contain the most valuable insights but also the most complexity. Artemis uses AI-powered semantic analysis to interpret, summarize, and structure qualitative feedback at scale.

Instead of manually reading hundreds or thousands of comments, users can request structured insights directly.

Artemis can:

• Summarize recurring themes

• Detect positive, negative, and mixed sentiment

• Cluster comments by meaning

• Highlight emerging concerns

• Extract representative quotes

• Group open texts results along with other survey question answers

🔎 Theme Discovery & Summaries

• “Summarize the main themes from the Comment Question.”

• “What are customers talking about the most based on the Comment Question?”

• “Provide a high-level overview of recent comments based on the Comment Question.”

• “Identify the top 5 recurring feedback themes from the Comment Question.”

• "Provide the main themes of the Comment Question and group them by the Country Question"

Outputs may include structured summaries, categorized themes, and concise narrative overviews.

😊 Sentiment Analysis

• “Analyze sentiment in recent comments based on the Comment Question.”

• “Show positive vs negative feedback distribution based on the Comment Question.”

• “Highlight the most critical comments based on the Comment Question.”

• “Identify strongly positive testimonials based on Testimonial Question.”

Artemis provides sentiment breakdowns and contextual interpretation, not just raw tagging.

🧩 Comment Clustering

• “Cluster comments by topic.”

• “Group similar feedback together.”

• “Identify patterns in service-related complaints.”

• “Segment comments into key discussion categories.”

This helps transform unstructured feedback into actionable insight groups.