This long article with a lot of source code was posted by Suraj V Vidyadaran. Suraj is pursuing a Master in Computer Science at Temple university primarily focused in Data Science specialization. His areas of interests are in sentiment analysis, data visualization, big data and machine learning. I was surprised to see the overlap with our recent article on top 10 machine learning algorithms. You can read the full article (with voluminous source code in R) here.

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.

Imagine I show you a book review, on amazon.com, Imagine I hide the number of stars, – all you get to see is the number of stars. And now I'm asking you, that review, is it good or bad? Well, it should be easy, for humans (although depending on the input there can be lots of disagreement between humans, too.) But if you want to do it automatically, it turns out to be surprisingly difficult.

But you probably know this already, if only from the preponderance of conference presentations, blogs and trade articles on the topic. Yes, text analytics are all the rage these days. You may feel under the gun to catch up, but if you're late to the game, you may be comforted to know that for many people, text analytics aren't living up to the hype. Nearly every researcher I come in contact with at conferences and through my professional network is at least actively investigating text analysis if they haven't already adopted a solution. And in either case, they're frequently underwhelmed. It's my experience that there are two primary reasons for this: How Do I Know Before I Buy?

Natural Language Processing (NLP) is a vast area of Computer Science that is concerned with the interaction between Computers and Human Language[1]. Within NLP many tasks are – or can be reformulated as – classification tasks. In classification tasks we are trying to produce a classification function which can give the correlation between a certain'feature' and a class . This Classifier first has to be trained with a training dataset, and then it can be used to actually classify documents. Training means that we have to determine its model parameters.

In our earlier posts we've discussed, and proven, empathys' growing importance in artificial intelligence. The next questions to ask are, "How do we make AI empathetic?", "How do we build emotion into our AIs?" and "Can we ever make AI feel?" At Kairos, we believe the answer to the "how" question is in face analysis. Facial recognition allows software to identify and verify human faces while emotion analysis allows software to measure and read the emotions on those found faces. More importantly, facial recognition and emotion analysis looks at each user as an individual and captures their specific human data.

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature.

Topic identification is a top-of-the-list need for organizations working with large volumes of online, social, and enterprise text. Along with entity resolution, relation extraction, summarization, and sentiment analysis, topic modeling is a key natural language processing (NLP) function. Premise number 2: Applied NLP -- text analytics -- remains as much art as science, requiring a combination of domain and technical expertise. How better to explore topic modeling and NLP advances than via an interview with a leading practitioner? This article features an interview with Lev Konstantinovskiy, a data scientist who is community manager for gensim, which offers open-source topic modeling for Python programmers.

The modern world seems really fast and dynamic with a multitude of new products being launched. Marketing agencies are making fortune by monitoring the markets and delivering reports on consumers' opinions. For today, the feedback analysis is a separate area, let's say a growing industry with an array of products and services. And the prices for those services are pretty exorbitant. So, do vendors have a chance to cut down expenses?

SAN FRANCISCO – U.S. police departments used location data and other user information from Twitter, Facebook and Instagram to track protesters in Ferguson, Missouri, and Baltimore, according to a report from the American Civil Liberties Union on Tuesday. Facebook, which also owns Instagram, and Twitter shut off the data access of Geofeedia, the Chicago-based data vendor that provided data to police, in response to the ACLU findings. The report comes amid growing concerns among consumers and regulators about how online data is being used and how closely tech companies are cooperating with the government on surveillance. "These special data deals were allowing the police to sneak in through a side door and use these powerful platforms to track protesters," said Nicole Ozer, the ACLU's technology and civil liberties policy director. The ACLU report found that as recently as July, Geofeedia touted its social media monitoring product as a tool to monitor protests.