Scraping Twitter with Twint Twitter’s official API is powerful is some ways, but fairly restrictive in the volume and pace of data collection. While social scientists are often interested in data from months or years ago, Twitter’s Standard Search API only goes back seven days. The cost of purchasing historical Twitter data is often out of reach of the average social scientist and even aquiring an API key has become increasingly difficult.
Data management and exploration in pandas This section provides a brief introduction to pandas. The pandas library is a key component for doing data science in Python for a couple of reasons. Most importantly, it provides two data types, series and data frame, that allow you to store and manipulate data in a way that is useful for analysis. Second, it is incredibly useful for importing and exporting data in a wide variety of formats.
A traditional method of analyzing texts is to compute the proportion of the words have positive connotations, negative connotations or are neutral. This method is commonly referred to as sentiment analysis. The typical approach to sentiment analysis is to see how many words in a text are also in a predefined list of words associated with a sentiment. So “I am having a bad day.” might score a “1” on a negative sentiment scale for the presence of “bad” or a .