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. On the first post, we collected all the articles published in TechCrunch, VentureBeat and Recode since 2007. We also filtered out all the ones that aren't about startups. On the second post, we trained machine learning models that can tell what event is described in a piece of news (product launch, acquisition, fundraising, etc) and what industries the startup the article is about belongs to (Fintech, Machine Learning, and so on). Now, we are finally ready to conduct our analysis on more than 270,000 articles, let's go over the results!
This 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, so we now have only the publications relevant to our analysis. Finally, we'll combine these classifiers to be ready to analyze all of our data. For the first part of this analysis, it'd be great to know for each piece of startup news what "event" it is describing.
This 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. So, if a sample mentions an IoT pacemaker startup, it should get the IoT tag in addition to the Health tag. Tagging the data was a similar process to the previous classifier, except that this time we took special care in tagging every sample with all the relevant categories. At this point, we are ready to repeat the same experiment we did in the previous post: classifying 100 articles and seeing what happens.
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. These are the types of questions we aim to answer with this analysis. On this first post, we will cover how Scrapy can be used to get all the articles ever published on these tech news sites and how MonkeyLearn can be used for filtering these crawled articles by whether they are about startups or not. We want to create a dataset of startup news articles that can be used for studying trends later on. On the second post, we will create text classifiers that do analysis on the actual content of the startup articles. Is it a news about acquisition?