Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which numerous client services are offered. In particular, Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants. AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs.

While many political scientists are itching to get their hands on data documenting how political ads on Facebook performed, the company says it won't be releasing that information. Facebook says it doesn't differentiate between political and commercial ads with this policy and that's not likely to change any time soon. Researchers want information on how much money candidates spent on ads, who saw them and how often they were clicked in order to assess how big of a role ads played in the election. And because online advertising allows for more specific demographic targeting than other methods, getting information on how ads changed across demographics is information that academics think would boost transparency. "The holy grail, I think, of political analysis for the 2016 election is to figure out which communications from which entities had an effect on which jurisdictions in the United States," Nathan Persily, a Stanford University professor, told Reuters.

Already, as of 2010, a quarter of Americans (24%) had posted product reviews or comments online, and 78% of Internet users had gone online for product research. But those are ancient stats. More recently, BrightLocal found in 2016 that 91% of consumers regularly or occasionally read online reviews, with 47% taking sentiment of local-business reviews -- the tonality of a review's text -- into account in purchasing decisions. Breaking out the figures, 74% of consumers say that positive reviews make them trust a local business more and 60% say that negative reviews make them not want to use a business, according to BrightLocal. So reviews are important, and the feelings expressed are key.

Sentiment analysis is extremely useful us to gain an overview of the public opinion behind certain topics and feedbacks. Automatically classifying text by sentiment allows you to easily find out the general opinions of people in your area of interest. For example, you might want to analyze reviews of a product to help you improve the customer experience, or to find the most or least popular product. The Obama used sentiment analysis to gauge public opinion to policy announcements and campaign messages ahead of 2012 presidential election. How can we get this?

Learn how to build a sentiment analysis solution for social media analytics by bringing real-time Twitter events into Azure Event Hubs. In this scenario, you write an Azure Stream Analytics query to analyze the data. Then you either store the results for later use or use a dashboard and Power BI to provide insights in real time. Social media analytics tools help organizations understand trending topics. Trending topics are subjects and attitudes that have a high volume of posts in social media.

This is the continuation of my mini-series on sentiment analysis of movie reviews, which originally appeared on recurrentnull.wordpress.com. Last time, we had a look at how well classical bag-of-words models worked for classification of the Stanford collection of IMDB reviews. As it turned out, the "winner" was Logistic Regression, using both unigrams and bigrams for classification. The best classification accuracy obtained was .89 So, bag-of-words models may be surprisingly successful, but they are limited in what they can do.

Editor's note: The following is an interview with University of Illinois professor and text analytics guru Bing Liu, conducted by marketing scientist Kevin Gray, in which Liu concisely outlines the current state of the field. Kevin Gray: I see "text analytics" and "text mining" used in various ways by marketing researchers and often used interchangeably. What do these terms mean to you? Bing Liu: My understanding is that the two terms mean the same thing. People from academia use the term text mining, especially data mining researchers, while text analytics is mainly used in industry. I seldom see academics use the term text analytics.

So you report with reasonable accuracies what the sentiment about a particular brand or product is. After publishing this report, your client comes back to you and says "Hey this is good. Now can you tell me ways in which I can convert the negative sentiments into positive sentiments?" – Sentiment Analysis stops there and we enter the realms of Opinion Mining. Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it. It will have information and valuable feedback that can literally help to build the next strategy.

This shinyapp is a live shiny/R web application (hosted on shinyapps.io) The web-application visualizes simple dictionary/word-count based sentiment-analysis scores for tweets (during Mar 17th - April 4th 2014) on smartphones in India in a few different ways. The shiny application can be found up and running here.

As part of my ramp up on Google APIs I wanted to create a project that would allow me some practical exercise in a context of a real application. All GCP services used in this example can be run under the GCP Free Tier plan. More more information see https://cloud.google.com/free/ The Go code, docs, and setup scripts are located in my GitHub repo.