Although the quest for Artificial Intelligence (AI), equipping trading algorithms with human qualities such as self-learning, continues to fascinate, it will be the explosion of the Internet of Things that will soon re-energize trading in capital markets. The Internet of Things (IoT) is rapidly growing through the addition of sensors to machines that allow them to "feel." Once they are equipped with feelings-- particularly sight, sound and touch-- machines can behave more intelligently, for example optimizing operations to use less fuel or predicting when they need maintenance. However, an interesting side effect is that the data from the IoT could be a new source of "insider" data for trading firms. For example, if combine harvesters (accessorized with sensors) signal a bumper wheat cropin the U.S. grain belt, traders can take advantage of this information before the crop report is issued.

February 7, 2017 Written by: Susan C. Daffron IBM surveyed more than 600 decision-makers about their cognitive initiatives and 62 percent of respondents stated that the results of their cognitive implementations exceed expectations*. Cognitive services, like those offered byIBM Watson, can help you find out how your customers feel and help you predict what they might do. With Watson, IBM is pioneering the development of models that can tell you about different and often hidden, aspects of an individual. These insights can then be used by an organization to deepen relationships, shape initiatives and drive innovation. REST APIs, like Watson Personality Insights and Watson Emotion Analysis, allow organizations to learn about an individual's: Organizations can now train apps to quickly analyze and interpret large volumes of unstructured sensory data.

Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author's position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.

Did you know that text analytics is used for everything from enriching customer insights to identifying fraud to determining sentiment about products and services? The much-valued "360-degree" view of the business can't possibly exist without unstructured information. Instead, you're more likely to have a 180-degree blind spot. By automatically identifying key concepts, extracting entities, and analyzing sentiment – with multi-language support – text analytics adds structure to the unstructured so it can be added to a knowledge graph, along with the structured data. Download the 5-Minute Guide to Text Analytics to learn how cognitive solutions surface the untapped business value typically hidden in unstructured content.

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

There exist two main approaches to automatically extract affective orientation: lexicon-based and corpus-based. In this work, we argue that these two methods are compatible and show that combining them can improve the accuracy of emotion classifiers. In particular, we introduce a novel variant of the Label Propagation algorithm that is tailored to distributed word representations, we apply batch gradient descent to accelerate the optimization of label propagation and to make the optimization feasible for large graphs, and we propose a reproducible method for emotion lexicon expansion. We conclude that label propagation can expand an emotion lexicon in a meaningful way and that the expanded emotion lexicon can be leveraged to improve the accuracy of an emotion classifier.

Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current state of the art al- gorithms for learning word embeddings learn vector representations from large corpora of text documents in an unsu- pervised fashion. This paper introduces SWESA (Supervised Word Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via word embeddings. SWESA leverages document label infor- mation to learn vector representations of words from a modest corpus of text doc- uments by solving an optimization prob- lem that minimizes a cost function with respect to both word embeddings as well as classification accuracy. Analysis re- veals that SWESA provides an efficient way of estimating the dimension of the word embeddings that are to be learned. Experiments on several real world data sets show that SWESA has superior per- formance when compared to previously suggested approaches to word embeddings and sentiment analysis tasks.

Mining such data to determine how people feel about your product, brand, or service, is called Sentiment Analysis. When applied to social media channels, it can be used to identify spikes in sentiment, thereby allowing you to identify potential product advocates or social media influencers. Companies such as Microsoft, IBM and smaller emerging companies offer REST APIs that integrate easily with your existing software applications. For example, using the following publicly available Sentiment Analysis REST API from a small start-up called Social Opinion, we pass in the text, "this phone is awesome", to the following URL: In the response, we can see the text has been identified as expressing positive emotion, with a 64% probability of that being true.

The core of the Bing News template starts with an Azure Logic App, which polls for news articles from the Bing News API at a preset schedule (5 minutes) on a list of user specified topics. As the data makes its way through the Logic App, the actual news article text is retrieved and sent through a series of Azure Functions for basic data transformation. Next, the Microsoft Text Analytics Cognitive Service is used for keyphrase and sentiment extraction over the text body. These text enrichments could alternately be performed in the Azure ML portion of the pipeline using the "Extract Key Phrases from Text" module. At this point, the data along with some basic enrichments are stored in an Azure SQL database.

Britons might soon be able to request that their embarrassing social media posts be taken down and records of their existence wiped, according to new proposals outlined today. The new bill will transfer the European Union's General Data Protection Regulation into UK law, as well as making a few additions and amendments. It's currently possible to delete any of your own posts manually, but that doesn't necessarily remove the information from social media companies' databases. According to Facebook's terms and conditions, "some things can only be deleted when you permanently delete your account." While not all requests for deletion will be granted – companies can decline on the grounds of freedom of expression, and when the information of scientific or historical importance – those involving information posted by or collected from children will nearly always be honoured.