In first two articles, you learned everything about types and problems of sentiment analysis algorithm, but you still don't know how to actually create one. So, in this article, we'll get our hands dirty and start with the simple one - rule-based!
In our last article, we explained different types of sentiments analysis, and now, we’ll focus on problems!
In our series of articles, you’ll learn everything about sentiment analysis and how to use it! First, we’ll start with types.
Let’s talk about text classification – one of the most important and typical tasks in data science. We’ll explain its benefits, where to use it and show how to make order and organize some articles.
Today Deep Learning is the main trend in AI and neural networks are number one machine learning algorithms in the field of image classification and speech recognition. We decided to share our experience in this area and help you to start your own first deep learning project.
In our previous article, we started the discussion about text mining and talked about the most powerful text mining techniques for natural language processing. Now the time has come to talk about problems and difficulties appearing with processing any language that isn’t English.
Recently word “mining” is heard in every corner. The reason is not only the bitcoin hype but also a wave of interest in machine learning tools and instruments to make customer behavior analysis. Text mining is one of such instruments. In the series of following articles, we will share with you the most powerful text mining techniques and identify some of the most used language processing tools. Also, we will clarify common issues with the text processing.
Today people’s profiles in social media can give a lot of information about their personality, preferences, style of life, products they would buy or things they would like. If you know how analyse user profiles using data science you get a magic key to the real picture of your customers. Find out how someones’s writing style in social media is defined by gender, age and personality.