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Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output.'High quality' in text mining usually refers to some combination of relevance, novelty, and interest.Therefore, initiatives have been taken such as Nature's proposal for an Open Text Mining Interface (OTMI) and the National Institutes of Health's common Journal Publishing Document Type Definition (DTD) that would provide semantic cues to machines to answer specific queries contained within text without removing publisher barriers to public access.Academic institutions have also become involved in the text mining initiative: among technical reports.The issue of text mining is of importance to publishers who hold large databases of information needing indexing for retrieval.
Additionally, on the back end, editors are benefiting by being able to share, associate and package news across properties, significantly increasing opportunities to monetize content.
Content analysis has been a traditional part of social sciences and media studies for a long time.
The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items.
Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents.
showing how different topics have different gender biases and levels of readability; the possibility to detect mood patterns in a vast population by analyzing Twitter content was demonstrated as well.