Anton Ovchinnikov

A recent blog post by DataRobot discusses how sentiment analysis can be used to analyze the buzz around movies on Twitter. The post explains how sentiment classification can be used to determine whether tweets about a movie are positive or negative, and how this information can be visualized to gain insights into the public sentiment around a movie. The post also discusses how different visualization techniques can be used to compare the sentiment around different movies.

This blog post discusses the modeling phase in sentiment analysis, specifically focusing on the Naive Plus/Minus model and the Logistic regression model. The performance of these models is evaluated using ROC curves and Precision vs Recall curves.

Use our demo Twitter stream analytics app to learn the difference between good and bad training and test data sets; learn about natural language processing.

This article discusses the importance of selecting or building the right data dictionary for sentiment analysis in social media, specifically for analyzing movie reviews on Twitter. It explores three open source dictionaries and ultimately recommends using a root word dictionary as the primary choice.

This article discusses the first step in the data analysis process, which is understanding the data. It outlines the types of data needed for the analysis and provides examples of the data and potential challenges that may arise.

This article discusses the growing interest in data science and machine learning and introduces a demo application called “Social Movie Reviews” that can be used to teach the basics of data science and machine learning.

This article discusses the general data science process and how it was applied to a sentiment analysis project. It explains the different phases of the process, from business understanding to data modeling and evaluation.