Stylometric Features Research Articles

Author gender identification from text

Text is still the most prevalent Internet media type. Examples of this include popular social networking applications such as Twitter, Craigslist, Facebook, etc. Other web applications such as e-mail, blog, chat rooms, etc. are also mostly text based. A question we address in this paper that deals with text based Internet forensics is the following: given a short text document, can we identify if the author is a man or a woman? This question is motivated by recent events where people faked their gender on the Internet. Note that this is different from the authorship attribution problem. In this paper we investigate author gender identification for short length, multi-genre, content-free text, such as the ones found in many Internet applications. Fundamental questions we ask are: do men and women inherently use different classes of language styles? If this is true, what are good linguistic features that indicate gender? Based on research in human psychology, we propose 545 psycho-linguistic and gender-preferential cues along with stylometric features to build the feature space for this identification problem. Note that identifying the correct set of features that indicate gender is an open research problem. Three machine learning algorithms (support vector machine, Bayesian logistic regression and AdaBoost decision tree) are then designed for gender identification based on the proposed features. Extensive experiments on large text corpora (Reuters Corpus Volume 1 newsgroup data and Enron e-mail data) indicate an accuracy up to 85.1% in identifying the gender. Experiments also indicate that function words, word-based features and structural features are significant gender discriminators.

Mining writeprints from anonymous e-mails for forensic investigation

Many criminals exploit the convenience of anonymity in the cyber world to conduct illegal activities. E-mail is the most commonly used medium for such activities. Extracting knowledge and information from e-mail text has become an important step for cybercrime investigation and evidence collection. Yet, it is one of the most challenging and time-consuming tasks due to special characteristics of e-mail dataset. In this paper, we focus on the problem of mining the writing styles from a collection of e-mails written by multiple anonymous authors. The general idea is to first cluster the anonymous e-mail by the stylometric features and then extract the writeprint, i.e., the unique writing style, from each cluster. We emphasize that the presented problem together with our proposed solution is different from the traditional problem of authorship identification, which assumes training data is available for building a classifier. Our proposed method is particularly useful in the initial stage of investigation, in which the investigator usually have very little information of the case and the true authors of suspicious e-mail collection. Experiments on a real-life dataset suggest that clustering by writing style is a promising approach for grouping e-mails written by the same author.

Ranking of predictor variables based on effect size criterion provides an accurate means of automatically classifying opinion column articles

We demonstrate an accurate procedure based on linear discriminant analysis that allows automatic authorship classification of opinion column articles. First, we extract the following stylometric features of 157 column articles from four authors: statistics on high frequency words, number of words per sentence, and number of sentences per paragraph. Then, by systematically ranking these features based on an effect size criterion, we show that we can achieve an average classification accuracy of 93% for the test set. In comparison, frequency size based ranking has an average accuracy of 80%. The highest possible average classification accuracy of our data merely relying on chance is ∼31%. By carrying out sensitivity analysis, we show that the effect size criterion is superior than frequency ranking because there exist low frequency words that significantly contribute to successful author discrimination. Consistent results are seen when the procedure is applied in classifying the undisputed Federalist papers of Alexander Hamilton and James Madison. To the best of our knowledge, the work is the first attempt in classifying opinion column articles, that by virtue of being shorter in length (as compared to novels or short stories), are more prone to over-fitting issues. The near perfect classification for the longer papers supports this claim. Our results provide an important insight on authorship attribution that has been overlooked in previous studies: that ranking discriminant variables based on word frequency counts is not necessarily an optimal procedure.

Dropping down the Maximum Item Set: Improving the Stylometric Authorship Attribution Algorithm in the Text Mining for Authorship Investigation

Problem statement: Stylometric authorship attribution is an approach concerned about analyzing texts in text mining, e.g., novels and plays that famous authors wrote, trying to measure the authors style, by choosing some attributes that shows the author style of writing, assuming that these writers have a special way of writing that no other writer has; thus, authorship attribution is the task of identifying the author of a given text. In this study, we propose an authorship attribution algorithm, improving the accuracy of Stylometric features of different professionals so it can be discriminated nearly as well as fingerprints of different persons using authorship attributes. Approach: The main target in this study is to build an algorithm supports a decision making systems enables users to predict and choose the right author for a specific anonymous author's novel under consideration, by using a learning procedure to teach the system the Stylometric map of the author and behave as an expert opinion. The Stylometric Authorship Attribution (AA) usually depends on the frequent word as the best attribute that could be used, many studies strived for other beneficiary attributes, still the frequent word is ahead of other attributes that gives better results in the researches and experiments and still the best parameter and technique that's been used till now is the counting of the bag-of-word with the maximum item set. Results: To improve the techniques of the AA, we need to use new pack of attributes with a new measurement tool, the first pack of attributes we are using in this study is the (frequent pair) which means a pair of words that always appear together, this attribute clearly is not a new one, but it wasn't a successive attribute compared with the frequent word, using the maximum item set counters. the words pair made some mistakes as we see in the experiment results, improving the winnow algorithm by combining it with the computational approach, achieved by using the CV statistical tool as a conditional threshold for attribute selecting; by doing so, the frequent pair result improved from 50% error to 0% in the improved frequent pair with a clear higher score result compared with the frequent word attribute. Conclusion/Recommendations: The new CV algorithm results improvement may lead to several new attributes usage that gave unsatisfying results before that might improve the direction for solving some hard cases couldn't be solved till now.

“The Naming of Cats”: Automated Genre Classification

This paper builds on the work presented at the ECDL 2006 in automated genre classification as a step toward automating metadata extraction from digital documents for ingest into digital repositories such as those run by archives, libraries and eprint services (Kim & Ross, 2006b). We have previously proposed dividing features of a document into five types (features for visual layout, language model features, stylometric features, features for semantic structure, and contextual features as an object linked to previously classified objects and other external sources) and have examined visual and language model features. The current paper compares results from testing classifiers based on image and stylometric features in a binary classification to show that certain genres have strong image features which enable effective separation of documents belonging to the genre from a large pool of other documents.

Author Identification using Stylometric Features

In this work we present a strategy for author identification for documents written in Portuguese. It takes into account a writer-independent model which reduces the pattern recognition problem to a single model and two classes, hence, makes it possible to build robust system even when few genuine samples per writer are available. We also introduce a stylometric feature set, which is based on the conjunctions of the Portuguese language. Experiments on a database composed of short articles from 10 different authors and Support Vector Machine (SVM) as classifier demonstrate that the proposed strategy can produced results comparable to the literature.

NEWS AND COMMENT ON RECENT DEVELOPMENTS FROM AROUND THE WORLD : DTI seeks views on software and business patents

Text is still the most prevalent Internet media type. Examples of this include popular social networking applications such as Twitter, Craigslist, Facebook, etc. Other web applications such as e-mail, blog, chat rooms, etc. are also mostly text based. A question we address in this paper that deals with text based Internet forensics is the following: given a short text document, can we identify if the author is a man or a woman? This question is motivated by recent events where people faked their gender on the Internet. Note that this is different from the authorship attribution problem.In this paper we investigate author gender identification for short length, multi-genre, content-free text, such as the ones found in many Internet applications. Fundamental questions we ask are: do men and women inherently use different classes of language styles? If this is true, what are good linguistic features that indicate gender? Based on research in human psychology, we propose 545 psycho-linguistic and gender-preferential cues along with stylometric features to build the feature space for this identification problem. Note that identifying the correct set of features that indicate gender is an open research problem. Three machine learning algorithms (support vector machine, Bayesian logistic regression and AdaBoost decision tree) are then designed for gender identification based on the proposed features. Extensive experiments on large text corpora (Reuters Corpus Volume 1 newsgroup data and Enron e-mail data) indicate an accuracy up to 85.1% in identifying the gender. Experiments also indicate that function words, word-based features and structural features are significant gender discriminators.

Maphaeus Vegius and Vergil's Aeneid: A Metrical Comparison

Previous articleNext article No AccessMaphaeus Vegius and Vergil's Aeneid: A Metrical ComparisonGeorge E. DuckworthGeorge E. Duckworth Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Classical Philology Volume 64, Number 1Jan., 1969 Article DOIhttps://doi.org/10.1086/365437 Views: 2Total views on this site Citations: 3Citations are reported from Crossref Copyright 1969 The University of ChicagoPDF download Crossref reports the following articles citing this article:Benjamin Nagy Metre as a stylometric feature in Latin hexameter poetry, Digital Scholarship in the Humanities 36, no.44 (Feb 2021): 999–1012.https://doi.org/10.1093/llc/fqaa043Leah Whittington Qui Succederet Operi: Completing the Unfinished in Maffeo Vegio’s Supplementum Aeneidos, I Tatti Studies in the Italian Renaissance 21, no.22 (Dec 2018): 217–244.https://doi.org/10.1086/699711David Scott Wilson-Okamura Virgil’s Iliad, (): 191–247.https://doi.org/10.1017/CBO9780511762581.010