Yes, a word that appears frequently in a document (TF) is one key indicator. Nevertheless, these are clearly not related to a document’s subject matter as keywords. Ironically, these junk words may hold the key to unlocking a world of information about a particular text. We could stop here and say that keyword generation is: “The words that appear most frequently are most important in a document.” While there is some merit to this idea, what we’ll see is that the most frequent words are just the words that appear frequently in all text: junk words like ‘to’, ‘a’, ‘and’, ‘you’, ‘me’, etc. How frequent is a given term in a document? This is exactly what we calculated in the concordance.
![textify word count textify word count](https://digital-transformation.media/en/wp-content/uploads/2019/10/Display-word-count-while-typing-1024x747.png)
Term frequency is one that we are already quite familiar with. Is there a way we could automatically generate keywords or tags for an article based on its word counts? Let’s consider a corpus of wikipedia articles. One common application of a text concordance is TF-IDF or term frequency–inverse document frequency. Here is a text concordance example and its source code. Remember that thing called a JavaScript object? var obj = Most programming languages and environments have specific classes or objects for a variety of data structures (a dictionary is just one example). Each element of the dictionary consists of a String paired with a number. A dictionary is the perfect data structure to hold this information. The fundamental building block of just about every text analysis application is a concordance, a list of all words in a document along with how many times each word occurred. For example, you could keep a list of student IDs ( student name/id) or a list of prices ( product name/price) in a dictionary. It’s just like having a dictionary of words and when you look up, say, Sue the definition is 24.Īssociative arrays can be incredibly convenient for various applications. In programming, this kind of data structure is often referred to as an “associative array”, “map”, “hash” or “dictionary.” It’s a collection of key/value pairs. What if, however, instead of numbering the elements of an array we could name them? This element is named “Sue”, this one “Bob”, this one “Jane”, and so on and so forth. log ( ' Is your name ' + nameList + ' ? ' ) var nameList = // Is your name Sue? console. Each element of an array is numbered and accessed by its numeric index. You know that thing we call an array? Yes, that’s right, an ordered list of data. For example, if you look at all the e-mails on the ITP student list, can you determine who is similar? Consider using properties in addition to word count, such as time of e-mails, length of e-mails, who writes to whom, etc.
![textify word count textify word count](https://i.pinimg.com/736x/cd/04/cd/cd04cd4fc6954b063cb7e1317cea8535.jpg)
Use the ideas to find similarities between people.Create a page sketch that analyzes the use of pronouns. For example, heavy use of the pronoun “I” is an indicator of “depression, stress or insecurity”. Pennebaker’s book The Secret Life of Pronouns, Pennebaker describes his research into how the frequency of words that have little to no meaning on their own (I, you, they, a, an, the, etc.) are a window into the emotional state or personality of an author or speaker. Implement some of the ideas specific to spam filtering to the bayesian classification example.not only how many times do the words appear in the source text, but where do they appear each time.) Expand the information the concordance holds so that it keeps track of word positions (i.e.Visualize the results of a concordance using canvas (or some other means).Lyrical Indicators and Parsing the State of the Union by Jonathan Corum.Nicholas Felton’s 2013 Annual Report, NY Times Article.An Intuitive Explanation of Bayes’ Theorem by Eliezer S.Paul Graham’s A Plan for Spam and Better Bayesian Filtering.Secret Life of Pronouns, Pennebaker Ted Talk.
#TEXTIFY WORD COUNT CODE#
![textify word count textify word count](https://i.stack.imgur.com/4dECn.jpg)