Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets share...
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Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets shared by the people on social media can be mined to infer the valuable ‘mindset’ of the people which is much required for any ruling government in the world. The objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. The idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. The pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). The larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. The non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries’ sentiment and their corresponding reliance on Russian oil is performed.
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