Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine
This work addresses sentiment analysis for social media comments on the Rohingya crisis, but it is incremental as it applies an existing method to a new, small dataset.
The authors tackled sentiment analysis of comments on the Rohingya movement by developing a Support Vector Machine classifier, achieving improved results over a previous Naive Bayes approach on a custom dataset of 5,000 comments.
The Rohingya Movement and Crisis caused a huge uproar in the political and economic state of Bangladesh. Refugee movement is a recurring event and a large amount of data in the form of opinions remains on social media such as Facebook, with very little analysis done on them.To analyse the comments based on all Rohingya related posts, we had to create and modify a classifier based on the Support Vector Machine algorithm. The code is implemented in python and uses scikit-learn library. A dataset on Rohingya analysis is not currently available so we had to use our own data set of 2500 positive and 2500 negative comments. We specifically used a support vector machine with linear kernel. A previous experiment was performed by us on the same dataset using the naive bayes algorithm, but that did not yield impressive results.