![]() In the past, NLP algorithms were primarily based on statistical or rules-based models that provided direction on what to look for in data sets. Analytical models are then run to generate findings that can help drive business strategies and operational actions. The upfront work includes categorizing, clustering and tagging text summarizing data sets creating taxonomies and extracting information about things like word frequencies and relationships between data entities. ![]() However, one of the first steps in the text mining process is to organize and structure the data in some fashion so it can be subjected to both qualitative and quantitative analysis.ĭoing so typically involves the use of natural language processing ( NLP) technology, which applies computational linguistics principles to parse and interpret data sets. Text mining is similar in nature to data mining, but with a focus on text instead of more structured forms of data. Increasingly, text mining capabilities are also being incorporated into AI chatbots and virtual agents that companies deploy to provide automated responses to customers as part of their marketing, sales and customer service operations. Mining and analyzing text helps organizations find potentially valuable business insights in corporate documents, customer emails, call center logs, verbatim survey comments, social network posts, medical records and other sources of text-based data. Text mining has become more practical for data scientists and other users due to the development of big data platforms and deep learning algorithms that can analyze massive sets of unstructured data. It's also known as text analytics, although some people draw a distinction between the two terms in that view, text analytics refers to the application that uses text mining techniques to sort through data sets. It also offers a form of escapism from reality through the sub cultures and fan communities created.Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data. Poaching blurs the line between producer and consumer by giving the reader power to produce their own work based upon their own interpretation. Jenkins' extension of the term “poaching” discusses how a fan can simultaneously interpret a text through both the dominant and oppositional reading, allowing readers to stick as closely to the ‘canon’ (official rules and principles put forward in the original text) as they wish. Jenkins’ book then takes the active audience theory and applies it to fan cultures which ‘poach’ from their beloved text to create new texts such as fan fiction, filk (folk songs) and manuals/dictionaries to ‘fill-out’ further details not originally explained in the text. This ‘poaching’ is a resistance strategy for the individual, however it is inherently weak compared to the dominant culture and will generally be an act, like poaching, which is pushed underground. De Certeau links audience members to poachers by describing how they, “move across lands belonging to someone else, like nomads poaching their way across fields they did not write, despoiling the wealth of Egypt to enjoy it for themselves.”(1984) The Practise of Everyday Life discusses how people individualise mass culture by interpreting texts beyond the dominant meaning which has been decided by the elite (academics, teachers, authors etc) who monopolise the readings. ![]() This follows Stuart Hall’s encoding/decoding model of communication (1980) where each person will create their own meaning from the same text, depending on their situation and unique background. ![]() De Certeau argues that audiences are not passive consumers but instead active interpreters. The term “textual poaching” was first developed by the French scholar Michel de Certeau in The Practise of Everyday Life (1984) and later developed by Henry Jenkins in Textual poachers: Television Fans and Participatory Culture (1992).
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