The simplest solutions are usually the most powerful ones, and Naive Bayes is a good proof of that. In spite of the great advances of the Machine Learning in the last years, it has proven to not only be simple but also fast, accurate and reliable. It has been successfully used for many purposes, but it works particularly well with natural language processing (NLP) problems. […]
Analyzing 10 years of startup news with Machine Learning
Analyzing startup news — part 3
This is the final part in a series where we use machine learning and natural language processing to analyze articles published in tech news sites in order to gain insights about the state of the startup industry. […]
Creating machine learning models to analyze startup news
Analyzing startup news — part 2
GalleryThis is the second part in a series where we analyze thousands of articles from tech news sites in order to get insights and trends about startups.
Last time around we scraped all the articles ever published in TechCrunch, VentureBeat and Recode using Scrapy. We then filtered out all the articles that weren’t about startups, […]
Filtering startup news with Machine Learning
Analyzing startup news — part 1
On this new post series, we will analyze hundreds of thousands of articles from TechCrunch, VentureBeat and Recode to discover cool trends and insights about startups.
What are the hottest industries for startups right now?
Do machine learning startups get more press than fintech startups?
What is the startup segment with most acquisitions?
These are the […]
Introducing Google Sheets add-on for MonkeyLearn
Today, we’re excited to announce our brand new integration with Google Sheets!
Doing things with machine learning often feels like having superpowers. You can find out what people think about the different candidates on a major presidential election, analyze millions of hotel reviews to find out that London hotels have the worst food and […]
How to create text classifiers with Machine Learning
GalleryBuilding a quality machine learning model for text classification can be a challenging process. You need to build a training dataset, test different parameters for your model, fix the confusions, among other things.
On this post, we will describe the process on how you can successfully train text classifiers with machine learning using MonkeyLearn […]
Analyzing the conversation during the US Election Day
Donald Trump has been elected 45th president of the United States of America and the surprising outcome has been reflected on the conversation on social media.
We recently released a simple tool called Tarsier that uses machine learning to analyze the conversation around the US elections […]
Donald Trump vs Hillary Clinton: sentiment analysis on Twitter mentions
Election day looms closer and closer every week. US Politics are rapidly becoming the preferred conversation topic for millions of Americans and non-Americans worldwide. What are these people saying? What do they think? What are their opinions? How do they feel?
We are using machine learning to find out! For the past few months, we’ve […]
Know your followers with Machine Learning
GalleryTell me who your followers are and I tell you who you are.
You have lots of followers, congrats! You’re popular, maybe an influencer. But, do you actually know who’s following you?
If you have thousands of followers, that could be a tricky question to answer. Let’s use Machine Learning to try to answer that question.
MonkeyLearn’s Twitter […]
Training a sentiment analysis classifier using a web scraping visual tool
GalleryContributed by Quentin Simms from ParseHub.
In a previous blog post, you learned how to make quick work out of training and deploying your own custom sentiment analysis model. In the past, this kind of project would have taken a team of machine learning experts a long time to finish, and it can now be […]