Facebook is embroiled in another privacy scandal, although this time it's not of the company's direct making. UpGuard researchers have discovered over 540 million Facebook interaction records left exposed by third parties using Amazon's cloud services. Nearly all of them come from Mexican media company Cultura Colectiva, which recorded account names, comments, Facebook IDs and likes, among other details. Another exposure comes from At the Pool, a long-defunct app that left 22,000 passwords unprotected in addition to other sensitive details. UpGuard didn't have much success getting Amazon to take down the content.

Researchers discovered hundreds of millions of Facebook users' data was left unprotected once again, this time on Amazon's servers. The information exposed was stuff like names, passwords, comments, interests, and likes. The tl;dr: Facebook doesn't seem to have much control over what third parties do with your data, basically ever, so you might want to lock down those privacy settings. President Trump has hosted everyone from foreign dignitaries to sitting members of Congress at his home away from home--Mar-a-Lago. But after a woman was arrested for sneaking in this week, it raised the question: How safe is this place where Donald Trump conducts major presidential business?

Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers can submit their reviews along with a specific polarity rating. In some instances, there is a mismatch between the review and the rating. To identify the reviews with mismatched ratings we performed sentiment analysis using deep learning on Amazon.com product review data. Product reviews were converted to vectors using paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit. Our model incorporated both semantic relationship of review text and product information. We also developed a web service application that predicts the rating score for a submitted review using the trained model and if there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the reviewer.

Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernel with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for evolving Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that simultaneously considers two quality metrics and the computational time spent by the kernels. Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.

Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentiment-distinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN.

Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results.

This study's goal is to create a model of sentiment analysis on a 2000 rows IMDB movie comments and 3200 Twitter data by using machine learning and vector space techniques; positive or negative preliminary information about the text is to provide. In the study, a vector space was created in the KNIME Analytics platform, and a classification study was performed on this vector space by Decision Trees, Na\"ive Bayes and Support Vector Machines classification algorithms. The conclusions obtained were compared in terms of each algorithms. The classification results for IMDB movie comments are obtained as 94,00%, 73,20%, and 85,50% by Decision Tree, Naive Bayes and SVM algorithms. The classification results for Twitter data set are presented as 82,76%, 75,44% and 72,50% by Decision Tree, Naive Bayes SVM algorithms as well. It is seen that the best classification results presented in both data sets are which calculated by SVM algorithm.

Federal prosecutors are conducting a criminal investigation into data deals Facebook struck with some of the world's largest technology companies, intensifying scrutiny of the social media giant's business practices as it seeks to rebound from a year of scandal and setbacks. A grand jury in New York has subpoenaed records from at least two prominent makers of smartphones and other devices, according to two people who were familiar with the requests and who insisted on anonymity to discuss confidential legal matters. Both companies had entered into partnerships with Facebook, gaining broad access to the personal information of hundreds of millions of its users. We'll tell you what's true. You can form your own view.

The New York Times reports that federal prosecutors are conducting a criminal investigation into Facebook's data deals with major electronics manufacturers. The newspaper says a grand jury in New York has subpoenaed information from at least two companies known for making smartphones and other devices, citing two unnamed people familiar with the request. It reports that both companies had data partnerships with Facebook that gave them access to the personal information of hundreds of millions of users. Facebook describes those data deals as innocuous efforts to help smartphone makers provide Facebook features to users before the social network had its own app. The Times reports that it is not clear when the inquiry began or exactly what it is focusing on.

The newspaper says a grand jury in New York has subpoenaed information from at least two companies known for making smartphones and other devices, citing two unnamed people familiar with the request. It reports that both companies had data partnerships with Facebook that gave them access to the personal information of hundreds of millions of users.