uncleflo

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Some cool dude. Higher order of decision making. Absolute.

Registered since September 28th, 2017

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2 Sentiment analysis with tidy data | Text Mining with R

https://www.tidytextmining.com/sentiment.html

Saved by uncleflo on December 23rd, 2018.

In the previous chapter, we explored in depth what we mean by the tidy text format and showed how this format can be used to approach questions about word frequency. This allowed us to analyze which words are used most frequently in documents and to compare documents, but now let’s investigate a different topic. Let’s address the topic of opinion mining or sentiment analysis. When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion like surprise or disgust. We can use the tools of text mining to approach the emotional content of text programmatically, as shown in Figure 2.1.

tidy sentiment normalize toolbox sentiments analyze calculate words analysis development code ruby developer howto technical book demonstration frequency lingual language emotional approach investigate compare keyword useful web


Term Frequency and Inverse Document Frequency (tf-idf) Using Tidy Data Principles

https://cran.r-project.org/web/packages/tidytext/vignettes/tf_idf.html

Saved by uncleflo on December 23rd, 2018.

A central question in text mining and natural language processing is how to quantify what a document is about. Can we do this by looking at the words that make up the document? One measure of how important a word may be is its term frequency (tf), how frequently a word occurs in a document. There are words in a document, however, that occur many times but may not be important; in English, these are probably words like “the”, “is”, “of”, and so forth. We might take the approach of adding words like these to a list of stop words and removing them before analysis, but it is possible that some of these words might be more important in some documents than others. A list of stop words is not a sophisticated approach to adjusting term frequency for commonly used words. Another approach is to look at a term’s inverse document frequency (idf), which decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. This can be combined with term frequency to calculate a term’s tf-idf, the frequency of a term adjusted for how rarely it is used. It is intended to measure how important a word is to a document in a collection (or corpus) of documents. It is a rule-of-thumb or heuristic quantity; while it has proved useful in text mining, search engines, etc., its theoretical foundations are considered less than firm by information theory experts.

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CSSTidy

http://csstidy.sourceforge.net/

Saved by uncleflo on October 15th, 2016.

CSSTidy is an opensource CSS parser and optimiser. It is available as executeable file (available for Windows, Linux and OSX) which can be controlled per command line and as PHP script (both with almost the same functionality).
In opposite to most other CSS parsers, no regular expressions are used and thus CSSTidy has full CSS2 support and a higher reliability.

css tools optimization opensource software web compression tidy solution application tool small code clean parse reliable execute optimise command line control


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