DKE 2021 Home Project

The documentation describes the retrieval of training data and how my classifier can be trained and applied.

This repository contains the home project for the Data and Knowledge Engineering class 2021 at Heinrich Heine University.
The project exercise can be completed at home, using the expertise and skill sets acquired in the DKE 2021 lectures.
Use this repository to prepare your solution.

A grade improvement of 0.3 is given when the following criteria are met:

• You provided a working solution to the problem and your results are reproducible.
• You submitted the solution in time and in the specified format.
• Your classification algorithm yields a reasonable performance on the classification task. Score to beat: 0.545 accuracy
• You presented your approach to the class during the DKE 2021 lecture on 29.06.

Topic: PLD Media Bias Classification

The home project deals with the classification of media bias of PLDs linked in tweets. I.e. given a link to e.g. a news outlet in a tweet, is this news outlet left-biased or right-biased? For this project, we will use the citing tweets and their metadata for classification. Other data, e.g. the textual content of linked targets, may also be used.

PLD stands for 'Pay-Level Domain': the sub-domain of a public top-level domain which is usually under control of a single user or organization as this usually is the unit one pays for to control it. For example, the PLD of 'www.example.com' is 'example.com'.

Timeline:

We will release the test set and evaluation script on 16.06., 11am CEST time zone. You can run the evaluation script using gradle: gradle run
Provide the source code and generated output via your Git repository by 19.06., 3:00pm CEST time zone.
Present your solution (approach, results) to the class during the DKE2021 lecture on 29.06.2021 (detailed arrangements about presentations will be announced later in the lecture).

Folder Structure:

• input_data: contains the training data: one CSV file listing all PLDs with left vs. right bias classification, respectively. The test data will be added to this folder in the same format.
• output_data: folder that must be populated with the results of the home project. Please export all PLDs your algorithm classifies as left-biased to a file called 'left.csv' and all PLDs it classifies as right-biased to 'right.csv'. Please refer to the task description in the assignment folder for information on the format.
• src: add your code here in the suitable subfolder(s) (depending on whether you use Java or Python).
• assignment: contains a more detailed description of the task and the required output.

Notes

• You do not need to submit the classification results of your algorithm on the training data or other data in TweetsCOV-19. Point 6 in the task description is only to make sure that you include a method to actually output your predictions in the desired format for a given input dataset, i.e. list of PLDs, so that you will be able to apply your algorithm on the test data when it is released and execute the evaluation script . While developing, you may rely on cross-validation on the training set or create your own training / test splits
• The task is to classify PLDs, not (necessarily) individual tweets

Hints

• You might find a suspicious limit of 10,000 rows for your query results (why?). In this case, you might want to have a look at pagination