frequency counter in text using struct

A Frequency Counter

Our word frequency counter allows you to count the frequency usage of each word in your text.

That MS Word add-on created a list of all the words in a document, ordered by frequency. It made it easy to

detect overuse and/or abuse of a certain word or expression. The little used words were also of help, because it

may find errors that the spelling checker does not detect.

Automated authorship detection is the process of using a computer program to analyze a large collection of

texts, one of which has an unknown author, and making guesses about the author of that unattributed text. The

basic idea is to use different statistics from the text -- called "features" in the machine learning community -- to

form a linguistic "signature" for each text. One example of a simple feature is Type-Token Ratio - the number

of different words used in a text divided by the total number of words. It's a measure of how repetitive the

vocabulary is.

The documents to be checked is just regular text files (i.e., sequences of characters) and it has one word per line

--- you do not have to extract a word. It means this project does not depend on previous lab projects. The

frequency counter reads in the file to get words from the file. Each word in the file is counted and sorted

properly to produce output:

 Word list in frequency order -- high to low (first 10 highest and last 10 lowest)

 Word list in alphabetical order -- high to low (first 10 in alphabetical order and last 10 in alphabetical

order)

 Type-Token Ratio

input:

frequency.txt.

output:

 Word list in frequency order -- high to low (first 10 highest and last 10 lowest)

 Word list in alphabetical order -- high to low (first 10 in alphabetical order and last 10 in alphabetical

order)

 Type-Token Ratio

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