1.8 KiB
4 Chan Webscraper, Version 2
Consider doing your own data analysis. If you save your CSV, and make a pull request, I can add it to this repository for plotting word usage changes over time.
Highlights:
- Written in R.
- Objective: Datamining text, and displaying word frequencies.
- Uses the following libraries: rvest, tidyverse, tidytext, ggplot2, wordcloud, tinytex, syuzhet, scales, reshape2, and dplyr.
- If you don't have these installed in your RStudio software, then install them.
- After installing, and running this script into your RStudio IDE, you can download all posts.
- Downloaded posts are then manipulated to show word frequencies.
- Differs from V1 by scraping all replies to OP, and has a much larger noise filter.
- Sentiment analysis is also performed.
html_text vs html_text2 from rvest
I did an experiment comparing the tidy_pol_fixed2 output of text.
html_text = 21776 observations
html_text2 = 20004 observations
I will continue using html_text because it contains more observations, which I can later filter out the noise as needed. There were no substantial differences that I noticed in the graphs, so retaining a greater number of observations seems better than less.
From the rvest::html_text website:
There are two ways to retrieve text from a element: html_text() and html_text2(). html_text() is a thin wrapper around xml2::xml_text() which returns just the raw underlying text. html_text2() simulates how text looks in a browser, using an approach inspired by JavaScript's innerText(). Roughly speaking, it converts
to "\n", adds blank lines around <p> tags, and lightly formats tabular data.
html_text2() is usually what you want, but it is much slower than html_text() so for simple applications where performance is important you may want to use html_text() instead.