For many years, chatbots have been quietly moving from the screens of sci-fi movie to commercial applications. Companies like Google, Amazon, Apple, IBM, and Microsoft, along with an ever-increasing array of AI startups, have been gradually perfecting Conversational User Interfaces (CUIs) that allow people to interact with computers in natural language. In 2011, Apple’s Siri become the first mass-market chatbot application to be widely distributed, as part of iOS. It captured the imagination of millions of consumers and showed that practical AI applications had a place in everyday life. Then -- Wham! -- in the holiday season of 2016, the Echo became Amazon’s best-selling consumer product, thus signaling that the era of CUIs had finally arrived in a big way.
In the previous blog post we explained our overall approach to the DevOps stack used for the deployment and management of the In-Stream Processing blueprint. In this post we’ll focus on more details of Mesos and Marathon, and provide you with scripts to provision the complete computational environment on any cloud of your choice.
The advantage of Docker containers over configuration management tools for DevOps operations:
In online commerce, catalog navigation functionality is one of the key aspects of the overall user experience. Customers spend the majority of their time on the site discovering and selecting products and making purchase decisions. The mission of quality search and browse functionality is to provide the customer with the smoothest and shortest path to the most relevant products. Removing frustration from the product discovery and selection experience is a big part of this mission.
In the previous post we discussed which models we tried for sentiment classification and which one has demonstrated the best performance. In this post, we’ll show you how to visualize our under-the-hood findings so that others can see the results of our analysis. You can see our twitter sentiment analysis insights with our demo application here.
In previous posts we have discussed the steps needed to understand and prepare the data for Social Movie Reviews. Finally, it is time to run the models and learn how to extract meanings hidden in the data. This blog post deals with the modeling step in the Data Scientist’s Kitchen.
In the previous post we discussed how we created an appropriate data dictionary. In this post we’ll address the process of building the training data sets and preparing the data for analysis.
Post 4: Constructing a data dictionary for Twitter stream sentiment analysis of Social Movie Reviews
In the previous post we discussed the structure of the tweet data. In this post we’ll address the process of selecting or building the right data dictionary for our purpose.
Post 3: Understanding the structure of the data in Twitter streams for sentiment analysis applications
In the previous post we outlined the basic scientific method used and formalized the problem statement we are solving, which is, “Based on of the tweets of English-speaking population of the United States related to selected new movie releases, can we identify patterns in the public’s sentiments towards these movies in real-time and track the progression of these sentiments over time?” In this post we address the first step in the process, focused on the understanding of the data.
Our goal in the earliest stage of the project is to understand as much as we can about the data: what data sources are available; how much of the data is being produced; how is it captured and transmitted, with what latencies and on what channels; how long it stays available; how secure is it; how accurate it is, and so on. In our case, we need the following types of data:
As we explained in our introduction to this series of posts, we are exploring a data scientist’s methods of extracting hidden patterns and meanings from big data in order to make better applications, services, and business decisions. We will perform a simple sentiment analysis of a real public tweet stream, and explain how the data science project is organized. In the process, we will build several models for the sentiment analysis, starting with the simplest one possible, and will compare their performance so you’ll see a gain, or its absence, from more comprehensive modeling. All through the process, based on what we learn, we will continue to refine the answer to our main question: what business value can be mined from this data source? In this blog post, we discuss the general-purpose scientific process behind data science and how it was applied to our project.
There is a broad and fast-growing interest in data science and machine learning. It is fueled by an explosion in business applications that rely on automated detection of patterns and behaviors hidden in the data, that can be found by software and exploited to dramatically improve the way we market and sell products, optimize our inventory and supply chain, and detect fraud and support customers. In short, data science and machine learning improve how we make decisions in a wide range of situations based on patterns found in data.