Sentiment analysis of tweets related to the opening ceremony of the paris olympics 2024 using machine learning techniques.
| dc.contributor.author | FATIMA KHAN | |
| dc.date.accessioned | 2025-11-21T03:56:59Z | |
| dc.date.available | 2025-11-21T03:56:59Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This study employs machine learning techniques to analyze the sentiment of tweets about the 2024 Paris Olympics opening ceremony. The objective is to classify tweets as positive or negative, providing insights into public opinion and identifying areas of success and improvement for event organizers. A dataset of 1,000 tweets was collected, with 70% expressing positive sentiment and 30% reflecting negative sentiment. The Logistic Regression model was implemented for sentiment classification, with retweets and likes used as features. To guarantee correctness and consistency, the data underwent pre-processing procedures such as text cleaning, tokenization, lemmatization, stop word removal, and vectorization.. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results reveal a high positive sentiment toward the ceremony, with praise for performances, fireworks, and cultural elements. However, negative feedback pointed out sound issues, organizational delays, and lack of crowd enthusiasm. The Logistic Regression model achieved an accuracy of 72%, although it demonstrated bias toward positive sentiment, with challenges in identifying negative tweets. The findings provide valuable feedback for event organizers, highlighting areas to enhance future events and improve audience engagement. This study also emphasizes the importance of real-time sentiment tracking for monitoring public opinion during large-scale events. The limitations of the model include its inability to detect sarcasm and limited feature set, suggesting avenues for future research through advanced machine learning models and multilingual analysis. This study shows how machine learning can be used to analyze sentiment on social media. Providing actionable insights for event planners, sponsors, and stakeholders to enhance the event experience and audience satisfaction. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/11319 | |
| dc.language.iso | en | |
| dc.publisher | UMT.Lahore | |
| dc.title | Sentiment analysis of tweets related to the opening ceremony of the paris olympics 2024 using machine learning techniques. | |
| dc.type | Thesis |