Predicting user personality by mining social interactions in Facebook
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TP2010, a Facebook application, is developed with the goal of inferring personality from the analysis of user interactions within social networks, and the results show that the classifiers have a high level of accuracy, making the proposed approach a reliable method for predicting the user personality.About:
This article is published in Journal of Computer and System Sciences.The article was published on 2014-02-01 and is currently open access. It has received 113 citations till now. The article focuses on the topics: Personality & User modeling.read more
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Sentiment analysis in Facebook and its application to e-learning
TL;DR: A new method for sentiment analysis in Facebook is presented, starting from messages written by users, to extract information about the users' sentiment polarity (positive, neutral or negative), as transmitted in the messages they write, and to model the Users' usual sentiment pol parity and to detect significant emotional changes.
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Predicting user behavior in electronic markets based on personality-mining in large online social networks
TL;DR: A personality-based product recommender (PBPR) framework to analyze social media data in order to predict a user’s personality and to subsequently derive its personality- based product preferences is proposed.
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The Measure of All Minds: Evaluating Natural and Artificial Intelligence
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The effects of social media on students' behaviors; Facebook as a case study
Tuğberk Kaya,Huseyin Bicen +1 more
TL;DR: The findings highlight that Facebook is used for communication entertainment and sharing news, pictures and songs and students were aware that swearing is considered a form of misconduct, which is a good sign.
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C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.