Authorship attribution program free download

Here are 23 public repositories matching this topic Language: All Filter by language. Sort options. Star Updated Aug 23, Java. Updated Sep 25, Python. Updated Aug 17, Star 9.

Updated Nov 28, Python. Star 7. Updated Apr 21, Python. Star 5. Authorship Attribution with Machine Learning. Updated Feb 18, Python. Conversely, OSR investigators are expected to refuse authorship if offered under the same conditions. See also item 5 below. Indeed, this process should accompany the natural process of task assignment. It is acknowledged that the physiological evolution and the experimental requirements of any research project are in constant flux and that therefore authorships and especially co-authorships and acknowledgments , are subject to change, typically by addition, including during post-submission revision of manuscripts, and can be properly assessed at latter stages of project development or even upon manuscript drafting.

However, for the sake of transparency, discussions on authorship should occur as early as possible and as frequently as necessary to communicate such changes. The designated CA should be rigorously consistent across different manuscripts and projects in applying their own judgment and criteria in the establishment of authorship. OSR does not endorse the view that each author should be held responsible for the entire content of a manuscript. Skip to content. Star An authorship attribution project with particular emphasis on Twitter analysis 13 stars 7 forks.

Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats commits. Failed to load latest commit information. Stylometric Analysis and Obfuscation Using Python. Stylometry Capstone. View code. No worries if that terminology sounds too complex to learn, the core concepts are straightforward: Supervised Machine Learning involves training an algorithm to sort data into chosen categories by feeding in labeled examples of data text, images, etc that belong in whatever categories you wish to sort new data into.

In this case, we are feeding in tweets the data that are known to have come from one Twitter user in addition to ones that came from another the labels. The file read into the main Jupyter Notebook has the tweets labeled by the user they each came from in one of the CSV file columns.

Binary Text Classification simply means using a text classifier to sort new text samples into one category or another. In the case of Stylext, it is which of two people wrote a Tweet.

It then determines the best possible matches for your text. You can also use it to test and compare the effectiveness of various text analysis techniques. Furthermore, this application can be a dependable kickstarter for people unfamiliar with machine learning or any method of quantitative analysis, such as textometry or stylometry.

A reliable and powerful application that allows users to better understand the concepts behind text authoring and categorization.