Twitter was not always an advertising business, and it doesn't want to limit itself to promoted tweets. Under second-time CEO Jack Dorsey, the company has been reinvigorating its foundation as a real-time data service, and the revenue is following. Since Dorsey arrived last summer, he has been cultivating relationships with developers -- particularly those who pay for Twitter's data. In February, Dorsey listed developers as his fifth priority, behind the more obvious need to address the core service, live video, creators and safety. But interestingly, this fifth priority is the one that's already been bringing in more cash and continues to make Twitter relevant.

Avanti Recruitment is working with a successful and rapidly growing tech start-up in London to recruit a Lead Machine Learning Scientist to join their team. You will be responsible for defining and implementing the company's Machine Learning strategy and architecture for both supervised and unsupervised applications. You will be the link between the Computational Linguistic team and the Software Engineering team. You will work on sentiment analysis and real-time opinion streaming products. To be considered you will have demonstrable experience working on Machine Learning algorithms ideally for Natural Language Processing applications.

I have been trying to use ML for sentiment analysis of sentences, I have been successful with Naive Bayes and SVM but I would like to implement Neural Networks for Sentiment Analysis but couldn't find a way to convert words as input for neural networks. I know that representing word as a numerical is not efficient. How is nlpnet implemented, I tried to understand that but that flew over my head.

However, most importantly, the restaurant owner has the most scope for extracting valuable snippets of insights from customer reviews with ratings between 3-4/5. I recently had a chance to deliver a talk in a conference titled'Understanding Consumers in the Digital World', held at IIM Lucknow, Noida Campus on 16-17th November 2015. The audience mainly comprised of marketers, market research professionals and academics whose work is primarily focused on obtaining deep insights by understanding the online consumers. My talk was titled'Decoding Ratings for superior service in restaurants – Using text to understand customers'. The focus was quite simple – convince and demonstrate how to read and understand customers from their reviews, not ratings. Our product, Lunchbox, a complete restaurant management solution was showcased as well.

In an earlier blog post on Making the Business Case for Text Analytics, I had spoken of the importance of Social Media Analytics and specifically Text Analytics within the context of Social Media.for Social Media plays a critical role in today's world in understanding, measuring and influencing the real time perception of your company and/or brand. Social Media contains a wealth of information which needs to be analyzed and understood in a broader social and demographic context including, trend identification and receiving feedback from segments beyond what the traditional marketer or customer service center is accustomed to for receiving feedback. Given the sheer volume of data and the large number of users talking (posting, tweeting, etc.) about any given topic, visualization can be used very effectively in Social Media Analytics to effectively sort through the clutter and make sense of what is being said. Visualization enabled Analytics can be used to identify the trends and key influencers that may not otherwise evident.

Anyone interested in the future of design should have a look at the new Batmobile. I was lucky enough to be photographed alongside it at the London Film and Comic Convention earlier this year, and my inner geek was impressed. Built like a tank and armour-plated, with twin machine guns mounted on a bat-black body, it's a seriously cool-looking piece of kit. But looks can be deceptive. The Batmobile certainly looked rough and tough, but it was roped off from the crowds so no one could get too close.

The question is very similar to building a taxonomy to classify user questions into a number of categories, called tags. Interestingly, we face the same problem at Data Science Central: automatically attaching tags (from a set of 5,000 potential tags - e.g. We might hire someone to do this! In short, this is nothing more than automatically putting a structure on unstructured text.

Identify the top four metrics that drive sales among the metrics that I have suggested in this article (by all means, please do not ignore external data sources - including a sentiment analysis index by product, that is, what your customers write about your products on Twitter), and create a simple regression model. You could get it done with Excel (use the data analysis plug-in or the linest functions) and get better forecasts than using a much more sophisticated model based only on internal data coming from just one of your many silos. Get confidence intervals for your sales forecasts: more about this in a few days; I will provide a very simple, model-free, data-driven solution to compute confidence intervals.

"Data science is so new that there are no textbooks on the subject, and no university curricula designed to turn out data scientists.

In this article we will show how you can build a simple Sentiment Analysis tool which classifies tweets as positive, negative or neutral by using the Twitter REST API 1.1v and the Datumbox API 1.0v.