An Application of Sentiment Analysis to Analyze the Performance of Players on Endorsed Brands Using Social Media

Sentiment analysis and Named Entity Recognition (NER) are two powerful NLP techniques. This study investigates the correlation between the sentiments of cricket players and their impact on the stock prices of endorsed brands. The study employs Spacy’s NER model to extract player names from Twitter data and conduct sentiment analysis to evaluate public sentiment towards the players. The main aim of the research is to determine whether there exists a correlation between player sentiment and the success of advertising campaigns launched by their brands. To achieve the objective, the study collected tweets associated with cricket players, preprocessed the text, and utilized NER to identify related entities. Sentiment analysis has been performed to evaluate public sentiment towards the players. The proposed approach has been used to analyze the sentiment towards both the players and their supported organizations. Analyzing sentiments towards players and their supported brands on Twitter has provided valuable insights into advertising campaign effectiveness and social media’s impact on stock prices.