Fake news has been widely spread especially on social media as facts as often been manipulated with a various anonymous source. The spread of these fake news could give a huge and serious impacts on our society as it can affect political election, public figures and many more. Therefore, this study aims to sentiment analysis on fake news spreader classifier. A total about 23481 entries that consists fake news and abput 21417 entries that consists true news are collected from Kaggle website. This study applies Natural Processing Language as a pre-processing of the texts in sentiment analysis. N-grams and stop words are used to further analyse the fake news classifier. Sentiment analysis on fake news classifier can be useful for a future opportunity on real world. On the other hand, it is also can be used for everyone to have some platform to detect whether the news is fake or true. The results provide the classification of true and fake news of all the entries, word by word.

1. Farah Afifah binti Zulkefli
2. Manisha Nadiha binti Mohd Yusof