Sentiment Analysis of Political Tweets for Israel using Machine Learning
This work addresses public opinion analysis for political researchers and stakeholders, but it is incremental as it applies existing methods to a new dataset.
The research tackled sentiment analysis of political tweets related to the Palestinian-Israeli conflict using machine learning algorithms, achieving comparative results from models like SVC, DT, and NB without specifying concrete performance numbers.
Sentiment Analysis is a vital research topic in the field of Computer Science. With the accelerated development of Information Technology and social networks, a massive amount of data related to comment texts has been generated on web applications or social media platforms like Twitter. Due to this, people have actively started proliferating general information and the information related to political opinions, which becomes an important reason for analyzing public reactions. Most researchers have used social media specifics or contents to analyze and predict public opinion concerning political events. This research proposes an analytical study using Israeli political Twitter data to interpret public opinion towards the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using Machine Learning algorithms like Support Vector Classifier (SVC), Decision Tree (DT), and Naive Bayes (NB). Finally, a comparative analysis is done based on experimental results from different models.