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."

We are currently in an era of data explosion, where millions of tweets, articles, comments, reviews and the like are being published everyday. Developers are taking advantage of the abundance of data and using things like web scraping to do all kinds of cool things. Sometimes web scraping is not enough; digging deeper and analyzing the data is often needed to unlock the true meaning behind the data and discover valuable insights. On this tutorial we will cover how you can use MonkeyLearn and Scrapy to build a machine learning model that will help you analyze vast amounts of web scraped data in a cost-effective way. We will use Scrapy to extract hotel reviews from TripAdvisor and use those reviews as training samples to create a machine learning model with MonkeyLearn.

The inspiration for this week's clip in our "Get the Job Done!" series is the big-ticket procurement and implementation process--and all of those folks whose opinions you don't need. We hear all the time from prospective clients who've found themselves bogged down in the painful, protracted process of getting buy-in for enterprise text analytics platforms that offer something for everyone and come with a six-figure price tag. Oftentimes, this procurement process involves people in the organization who have lots of opinions but no research expertise and who, in cases, won't even be using the purchase in question. Worse yet, after everyone has had his/her say and the purchase has finally gone through, the original intended user finds the whole initiative mired in a lengthy, complicated implementation! It's 2017 and the one thing no one can afford to waste is time.

This is the last – for now – installment of my mini-series on sentiment analysis of the Stanford collection of IMDB reviews (originally published on recurrentnull.wordpress.com). So far, we've had a look at classical bag-of-words models and word vectors (word2vec). We saw that from the classifiers used, logistic regression performed best, be it in combination with bag-of-words or word2vec. We also saw that while the word2vec model did in fact model semantic dimensions, it was less successful for classification than bag-of-words, and we explained that by the averaging of word vectors we had to perform to obtain input features on review (not word) level. So the question now is: How would distributed representations perform if we did not have to throw away information by averaging word vectors?

Hi all, so I am a big fan of open source and definitely love the GitHub ecosystem which allows us to upload and share excellent software, research and inventions with people all over the world and in turn welcomes them to improve on existing repositories. Github has an excellent feature called "Trending in open source" where you can see trending repositories by language and time. I check it every once in a while to see exciting content being posted by users or as Github puts it, "See what the GitHub community is most excited about today." The book was launched officially last week and is available on all major distribution channels. For more information you can check out my other post.

Governments worldwide requested Facebook users' data nearly 60,000 times in the first half of 2016, a 27 percent increase over requests made in the second half of 2015, according to a Facebook bi-annual report published this week. In addition to government requests for user data, the report details which content Facebook restricts for violating local laws. The company says it studies each request carefully to determine whether or not it has merit, especially in emergency cases where imminent risk of serious injury or harm is involved. It ultimately handed over data in 80 percent of cases. The 27 percent jump for the latest reporting period compares to a 13 percent increase between the first and second halves of 2015, and 18 percent growth between the second half of 2014 and the first half of 2015.

This is the last – for now – installment of my mini-series on sentiment analysis of the Stanford collection of IMDB reviews (originally published on recurrentnull.wordpress.com). So far, we've had a look at classical bag-of-words models and word vectors (word2vec). We saw that from the classifiers used, logistic regression performed best, be it in combination with bag-of-words or word2vec. We also saw that while the word2vec model did in fact model semantic dimensions, it was less successful for classification than bag-of-words, and we explained that by the averaging of word vectors we had to perform to obtain input features on review (not word) level. So the question now is: How would distributed representations perform if we did not have to throw away information by averaging word vectors?

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.