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  2. Content analysis - Wikipedia

    en.wikipedia.org/wiki/Content_analysis

    While methods in quantitative content analysis in this way transform observations of found categories into quantitative statistical data, the qualitative content analysis focuses more on the intentionality and its implications. There are strong parallels between qualitative content analysis and thematic analysis. [6]

  3. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...

  4. Thematic analysis - Wikipedia

    en.wikipedia.org/wiki/Thematic_analysis

    Thematic analysis provides a flexible method of data analysis and allows for researchers with various methodological backgrounds to engage in this type of analysis. [1] For positivists, 'reliability' is a concern because of the numerous potential interpretations of data possible and the potential for researcher subjectivity to 'bias' or distort ...

  5. Text mining - Wikipedia

    en.wikipedia.org/wiki/Text_mining

    Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." [1] Written resources may include websites, books, emails, reviews, and ...

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

  7. Co-occurrence network - Wikipedia

    en.wikipedia.org/wiki/Co-occurrence_network

    A co-occurrence network created with KH Coder. Co-occurrence network, sometimes referred to as a semantic network, [1] is a method to analyze text that includes a graphic visualization of potential relationships between people, organizations, concepts, biological organisms like bacteria [2] or other entities represented within written material.

  8. Today's Wordle Hint, Answer for #1251 on Thursday, November ...

    www.aol.com/lifestyle/todays-wordle-hint-answer...

    If you’re stuck on today’s Wordle answer, we’re here to help—but beware of spoilers for Wordle 1251 ahead. Let's start with a few hints.

  9. Topic model - Wikipedia

    en.wikipedia.org/wiki/Topic_model

    In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. . Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text

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