This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. Below is our fourth edition. Top 20 Big Data Experts to Follow (Includes Scoring Algorithm) Text Classification & Sentiment Analysis tutorial / blog Learn Everything about Sentiment Analysis using R 1.5 TB dataset of anonymized user interactions released by Yahoo Fuzzy Matching Algorithms To Help Data Scientists Match Similar Data

Andrew Greenstein, an app developer from San Francisco, started journaling a few months ago. He tries to write for five minutes every day, but it's challenging to set aside the time. Still, he's read that journaling reduces stress and can help with goal-setting, so he's trying to make it a habit. At the Disrupt London Hackathon, Greenstein and his team built The Emotion Journal, a voice journaling app that performs real-time emotional analysis to detect the user's feelings and chart their emotional state over time. By day, Greenstein is the CEO of SF AppWorks, a digital agency.

Text Classification for Sentiment Analysis – Eliminate Low Informat... Why and how to use random forest variable importance measures (and ...

With dozens of powerful text mining features, including access to free and premium Gnip Twitter data, DiscoverText provides software tools to quickly and accurately evaluate text data. Data scientists know that cleaning data can be very time consuming. Users of DiscoverText build custom machine classifiers or "sifters" to find the most relevant items. DiscoverText shortens a process that used to last weeks or months; our machine-learning sifters are created in hours or just a few minutes. We support technical integrations with Twitter and SurveyMonkey.

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.

The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."

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. Without any doubts, there's always an opportunity to start personal volcanic activities on feedback collection and analysis.

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language. This one-to-one mapping between words in different languages is too strict. Also the context is not considered at all. In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary. We conduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset, employing English as source language, and Chinese as target language. Our method does not rely on the parallel corpora and the experimental results show that our approach is more competitive than the state-of-the-art methods in cross-lingual sentiment classification.

Keras is a great library for hands-on on neural networks, and it has a ton of great examples that makes it very easy to create ANNs & DNNs. So easy in fact, that you could even build one without knowing what's going on. I used the CNN model from this Keras blog post to create a simple sentiment analysis model. But to fully understand what I had just done, I had to dig a little deeper. The basic model outlined in the post is using pre-trained word embeddings of the text to train a CNN for sentiment analysis. I have shown it below, with a few minor changes to padding sizes (border_mode'same'), so that the convolution output size stays the same as its input (for simplicity).

The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."