This recipe shows how to scrape comments from a YouTube video to analyze.
This recipe shows how to conduct dictionary-based sentiment analysis on a collection of passages, such as tweets or reviews. It uses pre-existing dictionaries of positive and negative words, and loads a text file of passages to analyze.
This recipe shows how to analyze the sentiment of a simple passage of text, such as a tweet.
This is a recipe for analyzing the mood or opinion of a text or corpus.
Let's say that you have a large collection of texts and you want to use a computer to help you classify those texts into two or more groups, such as "Philosophical" and "Other". One technique to accomplish this task is to use supervised learning whereby you train a computer to classify texts for you. Training involves manually classifying a subset of your texts, having the computer analyze features in each subset, and then having the computer try to classify texts that haven't already been classified.
This is a recipe for looking at the changes that have taken place in a wikipedia article over time, and generating a corpus of the different edited versions.
The technique known as "indexing" plays a fundamental role in search engines like Google and Yahoo, and can help researchers rapidly expedite their data analysis. This recipe will describe the steps one can follow in order to index data with the Python package Whoosh.
This recipe will guide you in developing a digital archival file collection suitable for deposit in an academic institution’s digital archive, including descriptive XML metadata documentation and a readme document.
This recipe is a guide to developing a detailed summary for text analysis or other research-oriented tools, based on primary and secondary sources. It is particularly useful for providing information on legacy tools that are no longer available, but is also extensible to modern tools.
This recipe is a guide to developing a review for a text analysis tool that will enable other users to decide whether that tool is suitable for their research tasks.