Natural language processing (NLP) is a technology spawned from the need for machines to understand and communicate with humans in human language, not formal computer languages. The concept behind NLP is simple: if and when machines can understand and communicate with humans in natural (human) language, it democratizes data science, enabling humans to access, analyze, and leverage data more intelligently and become more efficient as they offload redundant, data-heavy tasks to machines. NLP is most commonly understood as a user interface (UI) technology, enabling two-way communications with computers via speech or text. However, NLP is also a critical technology for extracting insights and analysis from a vast amount of previously unindexed and unstructured data; mining video and audio files, emails, scanned documents, and more. NLP adoption is accelerating, but not because of the creation of new NLP algorithms, as the data science in that regard is mature.

France's data privacy watchdog may fine WhatsApp if it does not comply with an order to bring its sharing of user data with parent company Facebook into line with French privacy law. CNIL, the French data protection authority, has told WhatsApp to comply with the order within one month, and pay particular attention to obtaining users' consent. If WhatsApp doesn't comply, it could sanction the company, CNIL said. France's data privacy watchdog may fine WhatsApp if it does not comply with an order to bring its sharing of user data with parent company Facebook into line with French privacy law (stock image) WhatsApp said it would begin sharing some user data with the Facebook in 2016, drawing warnings from European privacy watchdogs about getting the appropriate consent. In October, European Union privacy regulators criticised WhatsApp for not resolving their concerns over the messaging service's sharing of user data with Facebook a year after they first issued a warning.

After the EU slapped it with a €110 million fine over unlawful WhatsApp data sharing, you'd think Facebook would be eager to comply with local privacy laws. But France says it has not cooperated with data protection authority CNIL, and could face another sanction if it doesn't get its act together within 30 days. The social network is still transferring Whatsapp data for "business intelligence," it claims, and the only way that users can opt out is by uninstalling the app. The French regulator noticed that WhatsApp was sharing user data like phone numbers to Facebook for "business intelligence" reasons. When it repeatedly asked to see the data, Facebook said that it is stored in the US, and "it considers that it is only subject to the legislation of the country," according to the CNIL.

Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram features when learning from textual data. They are also compatible with the use of word embeddings so that word similarities can be accounted for. While the original any-gram kernels are implemented on top of tree kernels, we propose a new approach which is independent of tree kernels and is more efficient. We also propose a more effective way to make use of word embeddings than the original any-gram formulation. When applied to the task of sentiment classification, our new formulation achieves significantly better performance.

The regulator accepted that the transfer of user data for security purposes seemed to be essential to the functioning of the application. But the watchdog also said the same did not apply for "business intelligence" purposes which aim to improve the apps' performance.

Machine learning is in; Flash is out. Data scientists are in great demand, specialized developers, not so much. These are just a few of the trends LinkedIn picked up in its 2017 Emerging Jobs Report. It's no surprise that jobs in tech are growing faster than any other industry. The fastest growing job over the last five years is machine learning engineer, as the number of open positions on LinkedIn has multiplied by nearly 10X.

The modern world seems really fast and dynamic with a multitude of new products being launched. Marketing agencies are making fortune by monitoring the markets and delivering reports on consumers' opinions. For today, the feedback analysis is a separate area, let's say a growing industry with an array of products and services. And the prices for those services are pretty exorbitant. So, do vendors have a chance to cut down expenses?

No knowledge of machine learning is required for using Signals model builder. Just choose from a variety of indicators, ranging from traditional technical analysis to deep learning or sentiment analysis based on media monitoring and combine them together. However, if you happen to be a developer or a data scientist you can develop new trading indicators from scratch and monetize your data science skills through Signals indicator marketplace.

News have been de-duplicated based on the title. Finally, TICKER, PUBLICATION_DATE and SUMMARY columns were kept. Sentiment Analysis was performed on the SUMMARY column using Loughran and McDonald Financial Sentiment Dictionary for financial sentiment analysis, implemented in the pysentiment python library. This library offers both a tokenizer, that performs also stemming and stop words removal, and a method to score a tokenized text.

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg" about users' true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users' profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users' latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.