Podcast Beta
Questions and Answers
Match the following NLP technique with its description:
Topic analysis = Identifies recurrent themes or topics in text data Topic modeling = Organizes and understands large collections of text data by assigning categories according to each individual text’s topic or theme Topic extraction = Breaks down human language to find patterns and unlock semantic structures within texts Natural language processing (NLP) = Uses language processing to extract meaning from text and help make data-driven decisions
Match the following challenges with their solutions in text analysis:
Dealing with large volumes of unstructured text = AI-guided topic analysis makes it easier, faster, and more accurate to analyze unstructured data Manual sorting through large amounts of data = Leads to mistakes and inconsistencies; doesn’t scale well Analyzing huge amounts of text data = Too big a task to do manually; tedious, time-consuming, and expensive AI-guided topic analysis = Makes it easier, faster, and more accurate to analyze unstructured data
Match the following text sources with their examples:
Support tickets = One of the sources that businesses deal with large volumes of unstructured text every day Online reviews = One of the sources that businesses deal with large volumes of unstructured text every day Social media posts = One of the sources that businesses deal with large volumes of unstructured text every day Emails = One of the sources that businesses deal with large volumes of unstructured text every day
Match the following benefits with their descriptions in topic analysis:
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Match the following terms with their definitions in topic analysis:
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