Resources such as labeled corpora are necessary to train automatic models within the natural language processing (NLP) field. Historically, a large number of resources regarding a broad number of problems are available mostly in English. One of such problems is known as Personality Identification where based on a psychological model (e.g. The Big Five Model), the goal is to find the traits of a subject's personality given, for instance, a text written by the same subject. In this paper we introduce a new corpus in Spanish called Texts for Personality Identification (TxPI). This corpus will help to develop models to automatically assign a personality trait to an author of a text document. Our corpus, TxPI-u, contains information of 416 Mexican undergraduate students with some demographics information such as, age, gender, and the academic program they are enrolled. Finally, as an additional contribution, we present a set of baselines to provide a comparison scheme for further research.
While some researchers have gathered their own resources to conduct their investigations, there are few main resources broadly used: i) a corpus collected by Pennebaker @cite_2 and Mairesse @cite_25 () that consists of 2479 essays from psychology students, ii) , a collection of Facebook's posts @cite_9 , and iii) the developed in the framework of the PAN 2015 http: pan.webis.de clef15 pan15-web author-profiling.html author profiling shared task @cite_26 , where Twitter data is provided in several languages including Spanish. These freely available datasets are different in their nature, whilst has long texts and has been gathered through several years, and the corpora are examples of a massive short-texts data gathered from social media domains.
@cite_9 @cite_26 @cite_25 @cite_2
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The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts to learn about the author of the text through subtle variations in the writing styles that occur between gender, age and social groups. Such information has a variety of applications including advertising and law enforcement. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to identify the gender of users on Twitter using Perceptron and Nai ve Bayes with selected 1 through 5-gram features from tweet text. Stream applications of these algorithms were employed for gender prediction to handle the speed and volume of tweet traffic. Because informal text, such as tweets, cannot be easily evaluated using traditional dictionary methods, n-gram features were implemented in this study to represent streaming tweets. The large number of 1 through 5-grams requires that only a subset of them be used in gender classification, for this reason informative n-gram features were chosen using multiple selection algorithms. In the best case the Naive Bayes and Perceptron algorithms produced accuracy, balanced accuracy, and F-measure above 99 . In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. 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. Online social media yields a large-scale corpora which is fairly informative and sometimes includes many up-to-date entities. The challenging task of expanding entity sets on social media text is to extract more uncommon entities only using several seeds already in hand. In this paper, we present an approach which is able to find novel entities by expanding a small initial seed set on Twitter text. Our method first generates candidate sets on the basis of the semantic similarity feature. Then it jointly utilizes 2 text-based features and other 12 ones which carry social media specific information. With the scores on those features, a ranking model is learned by a supervised algorithm to synthetically score each candidate terms and then the final ranked list is taken as the target expanded set. We do experiments with 24 entity classes on the Twitter corpus and in the expanded sets there come many novel entities which have not been completely detected in previous researches. And the experimental results on the datasets of different years can perfectly consist with the objective law that fresh entities change as time goes on.