uncleflo
Registered since September 28th, 2017
Has a total of 4281 bookmarks.
Showing top Tags within 5 bookmarks
howto information development guide reference administration design website software solution service online product business uk tool company linux code server system application web list video marine create data experience tutorial description explanation learn technology build article blog world boat project download windows lookup security free performance javascript technical london control network beautiful tools support course file research purchase image library programming youtube example php construction opensource install community html quality profile computer feature power browser music platform mobile process work manage professional user share database hardware buy industry internet dance advice developer installation camera search 3d access customer material travel money test standard develop css review documentation engineering photography engine webdesign device digital speed api source event question management program client phone discussion story content simple water marketing yacht app account setup idea interface package fast communication cheap compare script market study easy live google resource operation demonstration startup monitor
Tag selected: words.
Looking up words tag. Showing 5 results. Clear
How TF-IDF algorithm determines keyword importance - arbitrue Blog
Saved by uncleflo on December 23rd, 2018.
There are many tools in the developer’s toolbox when it comes to automatic data extraction. A good example is TF-IDF algorithm (Term Frequency – Inverse Document Frequency) which helps the system understand the importance of keywords extracted using OCR. Here’s how TF-IDF can be used for invoice and receipt recognition. In this article we focus on other techniques in order to make this text file “understandable” to a computer. For this purpose, we must delve into the world of NLP or Natural Language Processing. We will focus mainly on how we can transform our file of raw text into a format that will easily be understandable by our algorithm. In a nutshell, TF-IDF is a technique for understanding how important a word is in a document which is often used as a weighting factor for numerous use cases. TF-IDF takes under consideration how frequent a word appears in a single document in relation to how frequent that word is in general. Search engines can use TF-IDF to determine which results are the most relevant for a search query.
bigram tf-idf toolbox categorical algorithm classify assign vocabulary document extraction words procedure frequency count extracted word numerical development technical analysis article blog consider language process exraction important explanation
2 Sentiment analysis with tidy data | Text Mining with R
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
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.
quantify tidy calculate corpus document words frequency calculating verbs numerical examine occur text weight quantity approach mining collection keyword tag analyse development howto data principle useful technical analysis developer code explanation article
Greek Swear Words
Saved by uncleflo on December 13th, 2011.
Ante gamisou: go fuck yourself, As to thialo: Go to hell, Ay gamisou: go fuck off, Fae skata kai psofa re malaka: Eat shit and die you wanker, Fila mou to kolo: kiss my ass, Gamo tin panagia sou: literally : I fuck your virgin Mary, Gamw to mouni pou se petage: I fuck the cunt that threw you, Glipse Tin Poutsamou Skila: Suck My Dick Bitch, Malaka: Wanker
swear fun greek funny hilarious bad words sentences translation community information guide language
Tips for Writing a Letter of Reference
Saved by uncleflo on July 19th, 2011.
In today's competitive job market, job applicants are forced to use every available tool to be successful. A letter of recommendation must be taken seriously. It could mean the difference between a student or new graduate being hired or being rejected. Here are some tips for writing your letter of recommendation.
tips information letter reference recommendation guide jobs writing boss employer applicant powerful power words
No further bookmarks found.