Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.
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AI-generated content summary
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How it works
Paste the text you’d like to analyze
From PDF snippets to customer support tickets, paste the text you’d like to run analysis on.
Choose your summary type
Filter by the type of text you want to summarize so we can tailor the results to be more relevant.
Copy the results out
Copy the results out to wherever you work. Re-run the summary if you’d like another set of results.
Ways to use AI content analysis
AI content analysis can you help quickly process data across a variety of usecases.
Analyze support tickets
Extract key points from open-ended support tickets.
Analyze survey responses
Quickly extract key takeaways from open-ended surveys to speed up your customer analysis.
Analyze academic research
Boost your research efficiency by easily finding key insights in academic papers through summarization.
AI content analysis involves using artificial intelligence and natural language processing (NLP) techniques to understand and extract meaningful information from text. Initially, the text is ‘cleaned’ to remove any unimportant elements, like punctuation or special characters. Then, algorithms look at aspects like word significance and sentiment and identify topics, sentiments, and categories within the text.
AI content analysis accuracy varies based on the task and the quality of the algorithms and training data. For tasks like sentiment analysis, where goals are clear, accuracy is high. However, understanding complex language or context is more challenging, and accuracy may be lower. Although AI offers useful insights, it’s not perfect, and you should take results into account while being aware of possible biases and limitations.
AI content analysis can be applied to many different types of text, including but not limited to:
Articles and blogs: Analyzing content to identify key themes, sentiments, or topics in articles and blog posts.
Social media posts: Understanding sentiments, extracting trends, or analyzing user opinions from social media text.
Customer reviews: Evaluating sentiments and extracting valuable information from product or service reviews.
Research papers: Identifying key concepts, themes, or trends within academic and research documents.
News articles: Summarizing news articles, categorizing topics, or analyzing sentiments in news content.
Emails and correspondence: Extracting information, sentiments, or key topics from emails and other written correspondence.
Legal documents: Analyzing legal texts for key information, sentiment, or categorization of content.
Surveys and feedback forms: Extracting insights from responses in surveys or feedback forms.
Creative writing: Analyzing themes or sentiments in creative pieces such as fiction, poetry, or storytelListItemStyledng.
Business reports: Extracting insights, sentiments, or key findings from business reports and documents.