Summary

Maybe you want to see how people are responding to a story or blog of yours – do they love it or hate it; does it make them excited? Maybe you want to detect the tone of people mentioning your company or product. Or perhaps you want to keep tabs on the mood of stocks in order to make money. We can use sentiment analysis for all of these things.

In this multi-part series, we will look at different methods of sentiment and emotion analysis in both Python and R. We will compare performance on a standard dataset, and also scrape our own live tweets for analysis. Finally, we will check performance on stock-related text snippets from news headlines and stocktwits.

Summary

I scraped all of leafly.com’s reviews, as well as 20,000 chemistry measurements of cannabis products. I used the reviews to make a collaborative recommendation engine, as well as a similar-strain recommender (still in progress). While studying the reviews and chemistry, I found that the reviews and chemistry data tend to group best into 3 groups. Two of the groups are similar, but one is a high-CBD group that is talked about a lot for pain and anxiety.

The app is live at cannadvise.me!

Summary

Learn how to use R to explore medium-big, real-world data sets, and how to train an XGBoost model to predict Grupo Bimbo’s sales volume for individual customers.

Summary

Make hydroponics pumps with timers (can be set to arbitrary time on/off).

Scraping Glassdoor

Glassdoor is a great site for checking out potential companies and jobs, as people can leave anonymous reviews on there. They have a decent API, but of course they don’t let you get at the most valueable part–the reviews! So, I did some Python programming to get at them for a company I applied to, and made word clouds of the top pros and cons.