SAN FRANCISCO – Soon, you could get fewer familiar ads following you around the internet -- or at least on Facebook. Facebook is launching a long-promised tool that lets you limit what the social network can gather about you on outside websites and apps. The company said Tuesday that it is adding a section where you can see the activity that Facebook tracks outside its service via its "like" buttons and other means. You can choose to turn off the tracking; otherwise, tracking will continue the same way it has been. Formerly known as "clear history," the tool will now go by the slightly clunkier moniker "off-Facebook activity."
After ML.NET Model Builder installation open your Visual Studio (in my case I'm using VS2019) After Project has been selected, enter your Project Name. Select Asp.Net Core template which you want to use, I'm using Web Application MVC. After the project has been created, we will start to build our model. Right-click on Project Add Machine Learning, ML.NET Model Builder tool GUI has been opened. After scenario selection, we will select the data set that will be used to train our model.
Sentiment analysis is not an easy task to perform. Text data often comes pre-loaded with a lot of noise. Sarcasm is one such type of noise innately present in social media and product reviews which may interfere with the results. Sarcastic texts demonstrate a unique behaviour. Unlike a simple negation, a sarcastic sentence conveys a negative sentiment using only positive connotation of words.
Diverse AI training data is imperative to building multilingual machine learning models, especially for morphologically complex languages like Korean. Because finding enough relevant data in Korean is difficult, we at Lionbridge have put together a comprehensive list of public Korean datasets for machine learning. National Institute of the Korean Language Corpus: This dataset contains frequency information on Korean, which is spoken by 80 million people. For each item, both the frequency (number of times it occurs in the corpus) and its relative rank to other lemmas is provided. Sentiment Lexicons for 81 Languages: This dataset contains both positive and negative sentiment lexicons for 81 languages, including Korean.
Facebook has agreed to pay a record $5 billion settlement to resolve an investigation into privacy violations, the Federal Trade Commission (FTC) announced Wednesday. The company will also create an "independent privacy committee" to ensure "greater accountability at the board of directors level," an FTC press release says. But the settlement won't affect Facebook's corporate governance structure, which lets Zuckerberg hold sway over the company's actions. Facebook has promised to clean up its act when it comes to privacy matters. But the social media giant's missteps have nonetheless cost it the trust of some users.
Cambridge Analytica may have become the byword for a scandal, but it's not entirely clear that anyone knows exactly what that scandal is. It's more like toxic word association: "Facebook", "data", "harvested", "weaponised", "Trump" and, in this country, most controversially, "Brexit". It was a media firestorm that's yet to be extinguished, a year on from whistleblower Christopher Wylie's revelations in the Observer and the New York Times about how the company acquired the personal data of tens of millions of Facebook users in order to target them in political campaigns. This week sees the release of The Great Hack, a Netflix documentary that is the first feature-length attempt to gather all the strands of the affair into some sort of narrative – though it is one contested even by those appearing in the film. "This is not about one company," Julian Wheatland, the ex-chief operating officer of Cambridge Analytica, claims at one point. "This technology is going on unabated and will continue to go on unabated.[…] There was always going to be a Cambridge Analytica. It just sucks to me that it's Cambridge Analytica."
The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers-- comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain--s representation, and many more.
CEOs and CFOs are decidedly more nervous when fielding questions about China during earnings calls this year. What's more, they are more likely to be deceptive with their answers. "Deception associated with questions on China has skyrocketed this quarter, up about 50% from last quarter and more than double a year ago," according to a study by text analytics provider Amenity Analytics. Amenity Analytics is one of a handful of companies that are applying natural language processing (NLP), sentiment analysis and machine learning to the financial sector, evaluating earnings calls and other public meetings to unearth information of value to an investor. It is also rare technology that offers a clear path to ROI.