During the past week I was able to:
- Improved the average accuracy, recall, and F1-score of my MLP classifier to 80%
- Implemented goose3, a news scraping API to query raw texts from news articles, handled the data, vectorized the sentences and pass them to the classifier. However, the classifier currently fails to identify a lot of liberal or conservative sentences on news sites, and usually outputs 100% neutral sentiment even for historically biased sites such as Breitbart and Huffington Post. This result is surprising given the test results attained from the given dataset. My hypothesis is that these news articles employs metaphors and perhaps subtly biased sentences that make it difficult for an MLP classifier to detect.
- During the next week, I plan to us Native Messaging to connect the Chrome Extension I made with the classifier so that the Chrome Extension has its most important functionality.