Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big data sets that are best analyzed in parallel and distributed computational environments. Indeed, current approaches to parallel inference either don't converge to the correct posterior or require storage of large dense matrices in memory. We present a novel sampler that overcomes both problems, and we show that this sampler is faster, both empirically and theoretically, than previous Gibbs samplers for LDA. We do so by employing a novel Pólya-Urn-based approximation in the sparse partially collapsed sampler for LDA. We prove that the approximation error vanishes with data size, making our algorithm asymptotically exact, a property of importance for large-scale topic models. In addition, we show, via an explicit example, that - contrary to popular belief in the topic modeling literature - partially collapsed samplers can be more efficient than fully collapsed samplers. We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora. Keywords: Bayesian inference, Big Data, computational complexity, Gibbs sampling, Latent Dirichlet Allocation, Markov Chain Monte Carlo, natural language processing, parallel and distributed systems, topic models.

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.

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135 times more data and 10 times more topics without sacrificing model quality.

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.

This shinyapp is a live shiny/R web application (hosted on shinyapps.io) The web-application visualizes simple dictionary/word-count based sentiment-analysis scores for tweets (during Mar 17th - April 4th 2014) on smartphones in India in a few different ways. The shiny application can be found up and running here.

In this blog post, the third one of our Topic Models series, we are showcasing how you can use BigML Topic Models to improve your model performance. We are using movie reviews extracted from the IMBD database to predict if a given review has a positive or a negative sentiment. Notice that in this post we will not dive into all the configuration options that BigML offers for Topic Models, for that we recommend that you read our previous post. The dataset contains 50,000 highly polarized movie reviews labeled with their sentiment class: positive or negative. This dataset was built by Stanford researchers for their paper from 2011, which achieves an accuracy of 88.89%.

As part of my ramp up on Google APIs I wanted to create a project that would allow me some practical exercise in a context of a real application. All GCP services used in this example can be run under the GCP Free Tier plan. More more information see https://cloud.google.com/free/ The Go code, docs, and setup scripts are located in my GitHub repo.

Machine learning is used for generating information and knowledge via a technical system, e.g., a software. The system learns patterns or structures using previous examples and is subsequently able to independently evaluate and classify information and data. Sentiment analysis, content monitoring, technology categorization, predictive coding, clustering, alerting, and documents search. Machine learning has already become very important in the context of big data since it enables processing large amounts of data quickly and easily.

For a more technical explanation, this and this article can be read. Here you can find a good explanation as well as a list of the mostly used Kernel functions.

Using code KDNUGGETS to save - early reg by May 31.