I've made an interesting observation recently while talking to people about Thinkudo Enlighten. It regards the misunderstanding between Text Analytics and Machine (automated) Translation. More than once people've asked "How did you do the Chinese translation?" So in this post, I'd like clarify the difference between them. Whether or not Machine Translation should be a substudy of Text Analytics, I will leave it to the readers within academia to discuss.

Find out how AI, Machine Learning and Sentiment Analysis are being applied to Finance in a new conference organized by UNICOM Seminars Ltd in Hong Kong on 14-15 March 2017. Technology innovations meet greatest success in business when these are entirely'client focussed'. Developments in the retail sector, which is consumer-led, are addressing client demand for more personalised, faster and competitive services. Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which these services are offered. In particular, Financial Organisations are creating and leveraging such innovation in the domain of wealth management.

We live in an age where there is a vast, over-abundance of data available on the web. The problem is that sifting through all of it to find and make sense of whatever is deemed relevant is an incredibly time-consuming task. But it may soon become easier, as Massachusetts Institute of Technology researchers recently revealed in a paper that introduces a new artificial intelligence system that would be capable of learning, on its own, in extracting useful information from online sources. Recently presented at the conference of the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing in Austin, the researchers' paper describes a new information extraction system that's able to automatically extract structured information from unstructured machine-readable documents. Put simply, the program can do what humans are good at: When faced with a gap in information or something we don't understand, we go and search for another document to digest that will add to our understanding or further our knowledge.

Scaling a model in response to user demand is crucial for bringing a machine learning model into production. In this blog post, we follow up on our previous post by showing how to scale this model in production using Pivotal Cloud Foundry (PCF). Pivotal Cloud Foundry makes it easy to scale an application using the command line interface (CLI) or the Apps Manager with no downtime. We utilize Apps Manager to horizontally scale out (spinning up new instances of our model) our application automatically utilizing PCF's load balancer, which reroutes new requests to appropriate instances of our model. Using the sentiment analysis analysis model we've built with Pivotal Greenplum and Python, we built a dashboard for analyzing live Tweets from the Twitter firehose.

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.

Looking at this text, we already see complexity emerging. As a human reader, I'm sure you'll say this is a negative review, and undoubtedly there are some clearly negative words ("dreadful", "confusing", "terrible"). But to a high degree, negativity comes from negated positive words: "lacking achievement", "wasn't very funny", "not as good as she could have given". So clearly we cannot just look at single words in isolation, but at sequences of words – n-grams (bigrams, trigrams, …) as they say in natural language processing. The question is though, at how many consecutive words should we look?

Text mining predictive methods help organizations enhance the value of unstructured information by deploying insight from text analysis in software applications and business processes. Once textual information is transformed into a set of structured data using text mining (or text analytics) it can be combined with traditional data mining algorithms to generate new insight for sentiment analysis and predictive analytics. Whether it is marketing and competitive intelligence, customer relationship management, social media monitoring, operational risk mitigation or threat discovery, big data is a key element for understanding where you are and where you're going. Text mining predictive methods support organizations in staying competitive. It helps them improve the ability to quickly react to customer feedback, market changes, competitive landscape evolutions, etc. This is precisely why enterprises should embed text analytics and predictive analytics into their business processes.

A pet collar that communicates whether a dog is happy or sad. A pair of jeans that gives directions. These are all real things that real people can supposedly really purchase one day,on show at this year's CES, the annual electronics show in Las Vegas. Almost all of the major electronics brands – Sony, Samsung, LG and like – are present at CES, but the real fun is exploring the smaller stalls. There are products for almost everything people could ever need.

Watson AI is expected to improve productivity by 30%, Fukoku Mutual says. The company was encouraged by its use of similar IBM technology to analyze customer's voices during complaints. The software typically takes the customer's words, converts them to text, and analyzes whether those words are positive or negative. Similar sentiment analysis software is also being used by a range of US companies for customer service; incidentally, a large benefit of the software is understanding when customers get frustrated with automated systems.

The interplay between AI and Sentiment analysis is also a new area. There are already many synergies between AI and Sentiment analysis because many functions of AI apps need sentiment analysis features. "The common interest areas where Artificial Intelligence (AI) meets sentiment analysis can be viewed from four aspects of the problem and the aspects can be grouped as Object identification, Feature extraction, Orientation classification and Integration. The existing reported solutions or available systems are still far from being perfect or fail to meet the satisfaction level of the end users. The main issue may be that there are many conceptual rules that govern sentiment and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being."