In this paper we do the first large scale analysis of writing style development among Danish high school students. More than 10K students with more than 100K essays are analyzed. Writing style itself is often studied in the natural language processing community, but usually with the goal of verifying authorship, assessing quality or popularity, or other kinds of predictions. In this work, we analyze writing style changes over time, with the goal of detecting global development trends among students, and identifying at-risk students. We train a Siamese neural network to compute the similarity between two texts. Using this similarity measure, a student's newer essays are compared to their first essays, and a writing style development profile is constructed for the student. We cluster these student profiles and analyze the resulting clusters in order to detect general development patterns. We evaluate clusters with respect to writing style quality indicators, and identify optimal clusters, showing significant improvement in writing style, while also observing suboptimal clusters, exhibiting periods of limited development and even setbacks. Furthermore, we identify general development trends between high school students, showing that as students progress through high school, their writing style deviates, leaving students less similar when they finish high school, than when they start.
Other uses of writing style analysis include distinguishing features of the writer (e.g. sex and age @cite_25 @cite_1 @cite_19 @cite_24 , demographics @cite_2 , or nationality @cite_11 ), using supervised learning algorithms such as SVMs, random forest, and neural networks. Other studies have investigated written conversations on online forums, trying to infer whether one person is trying to convince another @cite_12 .
@cite_1 @cite_24 @cite_19 @cite_2 @cite_25 @cite_12 @cite_11
2063205814 1988906723 1984010941 2406530401 2169719810 2135528482
This article explores differences between male and female writing in a large subset of the British National Corpus covering a range of genres. Several classes of simple lexical and syntactic features that differ substantially according to author gender are identified, both in fiction and in nonfiction documents. In particular, we find significant differences between male- and female-authored documents in the use of pronouns and certain types of noun modifiers: although the total number of nominals used by male and female authors is virtually identical, females use many more pronouns and males use many more noun specifiers. More generally, it is found that even in formal writing, female writing exhibits greater usage of features identified by previous researchers as 'involved' while male writing exhibits greater usage of features which have been identified as 'informational'. Finally, a strong correlation between the characteristics of male (female) writing and those of nonfiction (fiction) is demonstrated. A common characteristic of communication on online social networks is that it happens via short messages, often using non-standard language variations. These characteristics make this type of text a challenging text genre for natural language processing. Moreover, in these digital communities it is easy to provide a false name, age, gender and location in order to hide one's true identity, providing criminals such as pedophiles with new possibilities to groom their victims. It would therefore be useful if user profiles can be checked on the basis of text analysis, and false profiles flagged for monitoring. This paper presents an exploratory study in which we apply a text categorization approach for the prediction of age and gender on a corpus of chat texts, which we collected from the Belgian social networking site Netlog. We examine which types of features are most informative for a reliable prediction of age and gender on this difficult text type and perform experiments with different data set sizes in order to acquire more insight into the minimum data size requirements for this task. A syringe infusion or other patient health care fluid delivery arrangement or system having a substantially constant force syringe holder driver for a fluid-dispensing syringe to which an on-off valve and effectively bendably flexible tube connecting set are connected for delivery of a health care fluid to a patient delivery site at a preselected preset rate which is precision self-metered and determined by the fluid pressure and viscosity and the resistence to flow of fluid from the syringe and through the tube connecting set. The tube connecting set may be precision made to enable enhancement of predictability of fluid flow rate therethrough, and the various sets may be individually provided with indicia indicative of flow rate for given fluid and fluid pressure conditions. A single responsible health care professional may fill a syringe with a prescribed dosage and concentration of fluid to be administered to a patient and may preselect the delivery rate, by selecting and assembling a given syringe holder driver, fluid-filled syringe and tube connecting set. The syringe holder driver is powered by a pair of Neg'ator constant force springs which roll off and onto ball-bearing-mounted rotatable drums carried by a syringe plunger pusher or driver which is centered and guided in its longitudinal movement by the opposed Neg'ator springs and the syringe plunger, which springs are laterally stabilized by nonsliding strip-laying stabilizing retentive engagement with, and strip-pickup removal from engagement with, the syringe holder driver cover. Author profiling is the task of determining age, gender, native language or personality type of author by studying their sociolect aspect, that is, how lan- guage is shared by people. In this paper, we propose a Machine Learning ap- proach to determine unknown author's age and gender. The approach uses three types of features: content based, style based and topic based. We were able to achieve an accuracy of 64.08 , 64.30 for age and 56.53 , 64.73 for gender in English and Spanish respectively. For 20 years, information extraction has focused on facts expressed in text. In contrast, this paper is a snapshot of research in progress on inferring properties and relationships among participants in dialogs, even though these properties relationships need not be expressed as facts. For instance, can a machine detect that someone is attempting to persuade another to action or to change beliefs or is asserting their credibility? We report results on both English and Arabic discussion forums. In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy. Specifically, we first use automatic tools to ascertain frequencies of various stylistic idiosyncrasies in a text. These frequencies then serve as features for support vector machines that learn to classify texts according to author native language.