This is my submission to the final assignment of the course Python for Text Analysis, taken at Vrije Universiteit in Amsterdam. I chose a binary classification task of training a model that discriminates between whether an article is a blog or news article. I compare my solution, in which I have cherry-picked features based on the task, with a model that trains on features automatically extracted by a bag-of-words model provided by Scikit-learn. The dataset from which I extracted these features is described below. In the end it turned out that my model trained on the 26 features I selected performs on par with the bag-of-words model, which extracts over 100 000 features.

pip3 install -r requirements.txt

http://research.signalmedia.co/newsir16/signal-dataset.html

The articles of the dataset were originally collected by Moreover Technologies (one of Signal's content providers) from a variety of news sources for a period of 1 month (1-30 September 2015). It contains 1 million articles that are mainly English, but they also include non-English and multi-lingual articles. Sources of these articles include major ones, such as Reuters, in addition to local news sources and blogs.

• Cherry-picked features
  1. Generate feature set with notebooks/build_feature_set.ipynb
  2. Visualize with notebooks/visualize_data.ipynb
  3. Text classification with notebooks/text_classification.ipynb (~75% test set accuracy)
• Scikit-learn feature extraction
  1. Generate feature set and classify with notebooks/bag_of_words_model.ipynb (~75% test set accuracy)