Is it time to bring text analytics in-house? A director of analytics at Visa, who is slated to speak at the 13th Annual Text Analytics Summit West in San Francisco next week, advises CIOs to consider these three questions before making a move. Text analytics and the CIO don't ordinarily go hand-in-hand. Instead, the emerging field of text analytics, or the mining of text to derive business insights, is typically farmed out to experts. But if the business is serious about investing in tools and bringing the technology in-house, CIOs should be involved, according to Ramkumar Ravichandran, a director of analytics at Visa Inc.

Earlier this year I wrote a post that showed how to perform sentiment analysis in Dynamics CRM using Microsoft Azure Text Analytics. Azure Text Analytics makes it incredibly easy to use sentiment analysis (with English text only), but the full Azure Machine Learning offering is much more powerful. In today's post I will show how to create a custom predictive web service in Azure ML and make predictions with it in Dynamics CRM. One of the exciting announcements about Dynamics CRM 2016 is that it includes some sort of integration with Azure ML, so what's the point of this blog post? For this demonstration I am using data from the AdventureWorks data warehouse sample database to build a model to predict whether a contact in CRM is likely to be a bicycle buyer.

A lot of discussion around Matt Jockers' Syuzhet package (involving Annie Swafford, Ted Underwood, Andrew Piper, Scott Weingart and many others) has focused on issues of validity -- whether sentiment analysis is accurate enough for the task, whether the Fourier transform is an appropriate method for dimensionality reduction, whether the emotional trajectories themselves are valid measurements of anything at all (Scott has a good enumeration of the various issues here.) Andrew's discussion of the validity of inherently subjective measurements inspired me to solicit at least one data point from readers that we can use for one question under discussion with Syuzhet: what does a human judgment of the "emotional trajectory" of a work look like, and how often do readers agree with each other on this task? This method of soliciting human judgments for inherently subjective tasks is at the core of NLP and a lot of machine learning -- syntactic parsing, part of speech tagging, named entity recognition, topic classification, sentiment analysis, and lots of other tasks all rely on humans making judgments that are often surprisingly difficult in practice; learning algorithms in these cases are not so much learning any notion of "truth" but simply to reproduce the human judgments they're given. Agreement rates between humans is often seen as a proxy for the complexity of the task; if humans can't agree, it can be a sign that the task is ill-defined or underspecified. Word sense disambiguation is one good example of this, with low inter-annotator agreement rates [Snyder and Palmer 2004]; while sentiment analysis was originally designed with product/movie reviews in mind (does person X like product Y?) -- i.e., attitude with respect to a particular target -- I think the more general sentiment-as-tone problem (is this tweet happy or sad?) is much less well specified as a problem with an answer that can be judged by anyone but the original author. One aspect of those kind of annotations that I think is much less explored (which Piper points to and I think would be an extremely interesting area to work on) is the case where multiple judgments are simultaneously valid -- different interpretations of the same phenomenon, each backed by their own argument.

What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered.

I want to play around with twitter sentiment analysis and my main hesitation is on obtaining a dataset that's been classified for supervised learning. Do you know if a decent one exists already, or would I have to create one myself (or use mechical turk)?

Machine learning makes sentiment analysis more convenient. This post would introduce how to do sentiment analysis with machine learning using R. In the landscape of R, the sentiment R package and the more general text mining package have been well developed by Timothy P. Jurka. You can check out the sentiment package and the fantastic RTextTools package. Actually, Timothy also writes an maxent package for low-memory multinomial logistic regression (also known as maximum entropy).

With the big data growing bigger and bigger and social media penetrating every facet of the society, construing and monitoring data is one of the biggest challenges faced by the enterprises. Gone are those days when customers have to lodge a formal complaint to register the malfunctioning of any product/services provided by the business enterprise, rather, users these days take it to the social media forum to express their dissatisfaction and anguish towards any improper services/products. Inputs such as tweets, facebook comments could be of significant value to the enterprise to analyze their products/services/ performances, customer behavior and demands. Below is a small case study on Flipkart and Snapdeal performance when'World Book Day' was trending on Twitter. Below is the screenshot of'Flipkart' and'Snapdeal' on the occasion of'World Book Day'.

Twitter is an extremely popular online social networking and micro-blogging service. Users communicate through "tweets" - these are short 140-character messages or opinions about different topics. This site is a mine of information about users and their interests - their profile, views, attitudes, observations, people they follow on the site, etc. Apart from being used as a channel of communications between family and friends, Twitter is also used for real-time news updates, recommendations and sharing content. Processing all this information will provide marketers and opinion leaders with a wealth of knowledge about consumers and their behavior and enable them to design effective marketing strategies. Join this webinar to learn how to extract, analyse and utilize this data by implementing machine learning algorithm on the available information.

Data preprocessing is an umbrella term that covers an array of operations data scientists will use to get their data into a form more appropriate for what they want to do with it. For example, before performing sentiment analysis of twitter data, you may want to strip out any html tags, white spaces, expand abbreviations and split the tweets into lists of the words they contain. When analyzing spatial data you may scale it so that it is unit-independent, that is, so that your algorithm doesn't care whether the original measurements were in miles or centimeters. However, preprocessing data does not occur in a vacuum. This is just to say that preprocessing is a means to an end and there are no hard and fast rules: there are standard practices, as we shall see, and you can develop an intuition for what will work but, in the end, preprocessing is generally part of a results-oriented pipeline and its performance needs to be judged in context.

This blog was originally published on our Text Analysis blog, the blog post set out to analyze and visualize 4 million tweets collected during Superbowl XLIX. Not surprisingly, Superbowl XLIX generated a huge amount of chatter on social networks with Twitter Estimating that over 28.4 million posts made with terms relating to the Superbowl. At AYLIEN, we collected just under 4 million Tweets from Hashtags, Handles and Keywords we were monitoring. To keep our sample clean, we removed any reTweets and spam from the Tweets collected and only worked with those Tweets that were written in English. We were left with about 3.5 million Tweets to play with.