Course Project: Wikipedia Analysis
You will analyze the Wikipedia documents and generate interesting statistics about Wikipedia content and structure. The project will be done in two phases:
Phase 1: Data pre-processing --- Due November 19th
The class will be split into different groups, each group will be assigned a task and derive output that will be shared among all students. The tasks are the following:
- 1. Identify pages that have infoboxes. You will scan the Wikipedia pages and generate a CSV file named infobox.csv with the following format for each line corresponding to a page that contains an infobox: <cade>page_id, infobox_text
If a page does not have an infobox, it will not have a line in the CSV file. The infobox_text should contain all text for the infobox, including the template name, attribute names and values.
- 2. Extract links from Wikipedia pages. You will scan the Wikipedia pages and generate a CSV file named links.csv where each line corresponds to an internal Wikipedia link, i.e., a link that points to a Wikipedia page. Each line in the file should have the following format:
- 3. Extract text from Wikipedia pages. You will scan the Wikipedia pages and generate a CSV file named content.csv where each line corresponds to a Wikipedia page and contains the page id and its content. The content should be pre-processed to remove the Wiki markup. Each line in the file should have the following format:
- 4. Extract metadata from Wikipedia pages. You will scan the Wikipedia pages and generate an XML file named metadata.xml with where each element corresponds to a Wikipedia page. The metadata is represented in the file using XML markup. You can use the same schema, e.g.,
<page> <title>Albedo</title> <ns>0</ns> <id>39</id> <revision> <id>519638683</id> <parentid>519635723</parentid> <timestamp>2012-10-24T20:53:50Z</timestamp> <contributor> <username>Jenny2017</username> <id>17023873</id> </contributor> </page>
In addition, you will add information about the categories assigned to the page as well as the cross-language links. Here's a sample of the categories: [[Category:Climate forcing]] [[Category:Climatology]] [[Category:Electromagnetic radiation]] [[Category:Radiometry]] [[Category:Scattering, absorption and radiative transfer (optics)]] [[Category:Radiation]]
And here's a sample of the cross-language links: [[als:Albedo]] [[ar:بياض]] [[an:Albedo]] [[ast:Albedu]] [[bn:প্রতিফলন অনুপাত]] [[bg:Албедо]] [[bs:Albedo]] [[ca:Albedo]] [[cs:Albedo]] [[cy:Albedo]] [[da:Albedo]] [[de:Albedo]] [[et:Albeedo]]
- Start by writing and testing the code on your own machine using a small data sample. Once that is working, you can move to AWS.
- We have placed the Wikipedia data set on S3. The data is split into 27 file. See instructions on how to access it: Accessing Wikipedia data on S3
- Here are instructions on how to work with AWS (we distribute the AWS tokens next class): AWS Setup
- As you work on your task, take note of the issues you encounter so that you can discuss them during your presentation. You should also discuss the rationale for your design, in particular, how you designed your experiment and selected the number of machines and their configuration.
Phase 2: Data Analysis
Given the data products derived in phase 1, all groups will:
- Count number of pages that have an infobox, and the number of infoboxes that do not use a template
- Group pages according to the kind of infobox they contain
- Count the number of tables in all the Wikipedia pages
- Build a histogram that shows the distribution of cross-language links across different languages, i.e., for each language show the total number of links for that language
- Build a histogram that shows the recency of the pages, i.e., the histogram should how many pages have been modified on a particular date. You can use the last date of change for the pages.
- Group articles based on their recency and display a tag cloud for each group. You can decide on the granularity of the group based on the recency histogram.
- Compute page rank for each wikipedia page and display the top 100 with the highest page rank
- Build a histogram that shows the distribution of pages across categories, i.e., the number of pages in each category
- Some pages are assigned to multiple categories. Build a histogram that shows the number of pages that have 1, 2, 3, ..., n categories.
- Compute the word co-occurrence matrix (i.e., number of times word w_i occurs with word w_j within an article)
There are different ways to present the results of some of the analysis tasks. You are free to use your creativity to come up with visualizations that make it easy to convey the results.
You can also come up with your own questions, analysis and visualization ideas. Your colleagues will vote to select the most interesting ideas and the prize will be extra credit!
Submitting Code and Results
We will announce this soon!
We thank Amazon for the AWS in Education Coursework grant.