Sentiment analysis with twitter data on south china sea politics

Sentiment Analysis is the process of analyzing text and determine the author's attitude towards a particular topic by automatically classifying textual data. Understanding whether the expressed opinion in a document, a sentence, or a feature level is positive, negative, or neutral. The scale of social network data generated and processed is increasing exponentially in the Big Data era. Twitter currently produces 6000 tweets per second alone. Because of this huge amount of open data availability, sentiment analysis over social media data has been hugely popular to understand recent marketing trends, political shape-shifting, etc. In this thesis, three different open-source Python sentiment analysis tool was applied on a custom Twitter dataset. We tried to observe an event from last year and analyzed its reaction on Twitter. From the analysis, analyzed what was the general sentiment of the people from different countries. Our main focus here is to model country-based analysis of public opinion over different Chinese foreign policy measures which directly or indirectly affect China's near neighbors. Our analysis showed that not only China's neighboring countries but also the USA and the UK were very much more interested in the events happening around the South China Sea region. Also, the most negative sentiment expressing all countries has positive US diplomatic influence.