News Headline Classification through multiple machine learning model and comparison of results.
Models implemented:
- Multinomial Naive Bayes
- Support Vector Machines
- Neural Network with Softmax Layer
Metrics used to evaluate the performance of models:
- Precision
- Recall
- F1 Score
We evaluate each classifier's ability to select the appropriate category given an article’s title and a brief article description. The confusion matrix is created to explore the results and calculate the metrics.
The collection of text documents is converted to a matrix of token counts using count vectorize that produces a sparse representation of the counts.
TFIDF,term frequency–inverse document frequency, is the statistic that is intended to reflect how important a word is to a document in our corpus. This is used to extract the most meaningful words in the Corpus.
Link to Dataset: News Article Dataset
TagMyNews Datasets is a collection of datasets of short text fragments that we used for the evaluation of our topic-based text classifier. This is a dataset of ~32K english news extracted from RSS feeds of popular newspaper websites (nyt.com, usatoday.com, reuters.com). Categories are: Sport, Business, U.S., Health, Sci&Tech, World and Entertainment.
Packages required:
- Pandas
- sklearn
- Numpy
