"We have a responsibility to protect your data, and if we can't then we don't deserve to serve you." Mark Zuckerberg wrote that in a Facebook post on March 21, at the height of the furor over Cambridge Analytica's use of ill-gotten personal information from Facebook users to help in its work for political campaigns, possibly including Donald Trump's. On Friday, Facebook announced a massive security breach--probably the most significant in its history. Taking advantage of three separate bugs in tandem, hackers gained full control of at least 50 million users' Facebook accounts. That meant that, in theory, the users could do just about anything on those accounts: post status updates for you, download your photos, send messages to your friends, download your friends' nonpublic photos, and much more.

"It's always the company's responsibility to identify when UK citizens have been affected as part of a data breach and take steps to reduce any harm to consumers," ICO Deputy Commissioner of operations, James Dipple-Johnstone said http://bit.ly/2y1ahTQ.

Scroll down to know more. Our APIs make it easy for developers to build applications and leverage the power of ML/AI to develop features like gender classification, sentiment analysis and card detection. Detect the presence of any card in an image and classify the type of card, e.g. Classify the polarity of a given text, sentence or expressed opinion, as positive, negative,or neutral.

We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including inference and granularity. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you'll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school. Start with a problem tied directly to a specific business outcome. Make sure the subject of your pilot is small enough to tackle and clear enough to measure.

The AFINN lexicon is perhaps one of the simplest and most popular lexicons that can be used extensively for sentiment analysis. The current version of the lexicon is AFINN-en-165. You can find this lexicon at the author's official GitHub repository. The author has also created a nice wrapper library on top of this in Python called afinn, which we will be using for our analysis. Let's look at some visualisations now.

Automated sentiment classification (SC) on short text fragments has received increasing attention in recent years. Performing SC on unseen domains with few or no labeled samples can significantly affect the classification performance due to different expression of sentiment in source and target domain. In this study, we aim to mitigate this undesired impact by proposing a methodology based on a predictive measure, which allows us to select an optimal source domain from a set of candidates. The proposed measure is a linear combination of well-known distance functions between probability distributions supported on the source and target domains (e.g. Earth Mover's distance and Kullback-Leibler divergence). The performance of the proposed methodology is validated through an SC case study in which our numerical experiments suggest a significant improvement in the cross domain classification error in comparison with a random selected source domain for both a naive and adaptive learning setting. In the case of more heterogeneous datasets, the predictability feature of the proposed model can be utilized to further select a subset of candidate domains, where the corresponding classifier outperforms the one trained on all available source domains. This observation reinforces a hypothesis that our proposed model may also be deployed as a means to filter out redundant information during a training phase of SC.

A number of techniques have been proposed for aspect discovery using part of speech tagging (Hu and Liu, 2004), syntactic parsing (Lu et al., 2009), clustering (Mei et al., 2007; Titov and McDonald, 2008b), data mining (Ku et al., 2006), and information extraction (Popescu and Etzioni, 2005). Various lexicon and rule-based methods (Hu and Liu, 2004; Ku et al., 2006; Blair-Goldensohn et al., 2008) have been adopted for sentiment prediction together with a few learning approaches (Lu et al., 2009; Pappas and Popescu-Belis, 2017; Angelidis and Lapata, 2018). As for the summaries, a common format involves a list of aspects and the number of positive and negative opinions for each (Hu and Liu, 2004). While this format gives an overall idea of people's opinion, reading the actual text might be necessary to gain a better understanding of specific details. Textual summaries are created following mostly extractive methods (but see Ganesan et al. 2010 for an abstractive approach), and various formats ranging from lists of words (Popescu and Etzioni, 2005), to phrases (Lu et al., 2009), and sentences (Mei et al., 2007; Blair-Goldensohn et al., 2008; Lerman et al., 2009; Wang and Ling, 2016). In this paper, we present a neural framework for opinion extraction from product reviews. We follow the standard architecture for aspect-based summarization, while taking advantage of the success of neural network models in learning continuous features without recourse to preprocessing tools or linguistic annotations.

Sentiment analysis is perhaps one of the most popular applications of NLP, with a vast number of tutorials, courses, and applications that focus on analyzing sentiments of diverse datasets ranging from corporate surveys to movie reviews. The key aspect of sentiment analysis is to analyze a body of text for understanding the opinion expressed by it. Typically, we quantify this sentiment with a positive or negative value, called polarity. The overall sentiment is often inferred as positive, neutral or negative from the sign of the polarity score. Usually, sentiment analysis works best on text that has a subjective context than on text with only an objective context.