Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning

Determining the sentiment of a tweet can be a laborious task for NLP specialists, as they need to identify the specific segment of the sentence that accurately reflects the sentiment and its boundaries. It can be challenging to accomplish this task when the sentences are lengthy and the intended emotion is conveyed using multiple words or placed at the start or end. Information extraction and sentiment analysis are indispensable for processing news feeds and posts from public profiles of celebrities and ordinary persons to determine the sentiment of a tweet. When automated, these activities allow the categorization of tweets into several predefined classes and perhaps avoid the diffusion of fake news or toxic posts. Emotional writing can engage users and encourage them to spend more time browsing a website or getting more information about a product. However, it can also negatively impact the reader's mood, especially when they come across a toxic text with a high frequency of negative emotions, such as insulting comments or discriminatory remarks from followers on social media. Detecting such infractions early can increase the audience number on a web page and avoid unsubscribing clicks. When it comes to opinion mining, analyzing public opinion can be highly beneficial in assessing satisfaction and agreement with political decisions and programs. This type of analysis can offer valuable insights into a candidate's popularity and even aid in predicting their likelihood of winning an election compared to their competitors.