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.
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.
In the course of delivering many successful Continuous Performance Testing (CPT) implementations for enterprise customers, Grid Dynamics engineering teams have developed a number of basic design principles to guide their actions. Your requirements may be unique, but just as all custom race cars have a chassis, suspension, and wheels, all CPT implementations need to follow the six design principles we talk about in this post.
In previous posts we've talked about why Continuous Performance Testing (CPT) must be an integral part of managing the user experience. We've also discussed some of the reasons CPT is hard to implement.
The main reason website and application performance testing is not already continuous in many companies is clear: it’s hard to implement. Why? Let’s look at a few CPT implementation issues: