LinkedIn has begun to offer brand marketers more data to build content. Annalect, the global analytics arm of Omnicom Group, recently launched Professional Audiences based on that data. It gives marketers a better understanding of the content consumed by business audiences using LinkedIn data related to industry, company size, location, and title. Professional Audiences, a tool for media trends and insights within the Omni platform, is integrated across the Omnicom network and uses the LinkedIn Audience Engagement API to build B2B audiences in the same way it uses other to data to build B2C audiences. It uses LinkedIn's Audience API to reach the 645 million members.

Until recently, many of the social media privacy concerns that seem to swirl around Facebook on a regular basis never seemed to extend to Instagram, which is owned by Facebook. But all that could be changing as the result of a recent Instagram data scraping case that is attracting a lot of attention from privacy and security experts. A trusted Facebook marketing partner, HYP3R, had been scraping data from Instagram, storing it on its own servers, and then re-packaging all of that social media data for advertisers. The Instagram data scraping in question included physical locations, bio information, and photos – as well as some content (such as Instagram Stories) that were specifically intended to disappear after 24 hours. As might be imagined, Instagram is facing a firestorm of controversy over this HYP3R Instagram data scraping case.

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.

Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such representations becomes highly challenging in NLP tasks since the nuisance factor is entangled in a raw text. To our knowledge, a major issue is also that only few NLP datasets allow assessing the impact of such factor. In this paper, we introduce two generalization metrics to assess model robustness to a nuisance factor: \textit{generalization under target bias} and \textit{generalization onto unknown}. We combine those metrics with a simple data filtering approach to control the impact of the nuisance factor on the data and thus to build experimental biased datasets. We apply our method to standard datasets of the literature (\textit{Amazon} and \textit{Yelp}). Our work shows that a simple text classification baseline (i.e., sentiment analysis on reviews) may be badly affected by the \textit{product ID} (considered as a nuisance factor) when learning the polarity of a review. The method proposed is generic and applicable as soon as the nuisance variable is annotated in the dataset.

Episode 167 – Kevin Craine and Billee Howard discuss the use of nuero-powered technology to quantify, measure and understand human thought. Explore using artificial intelligence and sentiment analysis to connect customer emotion directly to business performance. Understand the convergence of'big emotion' and'big data' and how it is valuable from a strategic and marketing perspective. Stay tuned for three action items in the second half.

Due to the increased availability of online reviews, sentiment analysis had been witnessed a booming interest from the researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e. aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Persian is a language with more than 110 million speakers, to the best of our knowledge, there is not any public dataset on aspect-based sentiment analysis in Persian. This paper provides a manually annotated Persian dataset, Pars-ABSA, which is verified by 3 native Persian speakers. The dataset consists of 5114 positive, 3061 negative and 1827 neutral data samples from 5602 unique reviews. Moreover, as a baseline, this paper reports the performance of some state-of-the-art aspect-based sentiment analysis methods with a focus on deep learning, on Pars-ABSA. The obtained results are impressive compared to similar English state-of-the-art.

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.

What are the products with the highest ratings? Or in SQL What are the products with the highest ratings?