Text mining and text analysis

Identifying to which of a set of categories a text belongs. Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year etc.) e.g. Text categorization models for identifying unproven cancer treatments on the web Text categorization models for identifying unproven cancer treatments on the web

Grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense or another) to each other than those in other groups (clusters). e.g. Clustering and classification of maintenance logs using text data mining

Locating and classifying elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. e.g. Named entity recognition in Tweets: an experimental study.

Text analytics used to build taxonomies by providing suggested terms that a taxonomist can then edit. e.g. Text mining for taxonomy construction.

Determining the attitude of a speaker or writer with respect to some topic or the overall contextual polarity of a document. A basic task in sentiment analysis is classifying the polarity of a given text as positive, negative, or neutral. e.g. Text Analysis 101: Sentiment analysis in Tableau & R.

Process of reducing a text document with a computer program in order to create a summary that retains most important points of the original document. e.g. Build your own summary tool.

Identifying, analysing and reporting patterns (themes) within data. A theme captures something important about the data in relation to the research question. - e.g. Using thematic analysis in psychology