Table 3 Real case analysis.
Case number | Actual comments | Sentiment prediction | Prediction rating | Actual score | Analysis and discussion |
|---|---|---|---|---|---|
1 | “The movie was absolutely fantastic! The acting was superb, and the story kept me engaged the entire time.” | Positive | 8.5 | 9 | The model correctly identified the positive sentiment in the comments and predicted a high score, validating the accuracy of the model in positive sentiment comments. |
2 | “The movie was okay, but I felt that it was a bit too long. Some parts dragged a little.” | Neutral | 6 | 6 | The model successfully captures the neutral sentiment in the comments and gives reasonable rating predictions, indicating that the model can effectively handle comments that lack strong emotions. |
3 | “This movie was a complete waste of time. The acting was terrible, and the plot made no sense at all.” | Negative | 1 | 1 | In the negative sentiment reviews, the model accurately identified strong negative sentiments and predicted low ratings, demonstrating its robustness in negative sentiment analysis. |
4 | Action Movies: “An intense thrill ride with mind-blowing action sequences and an amazing cast!” | Positive | 8.5 | – | The model accurately identified the positive sentiment in the action movie and predicted a high rating. |
5 | Romance Movies: “A heartfelt story about love and loss, with incredible performances from the lead actors.” | Positive | 9 | – | The model was able to capture the positive sentiment in the romance film reviews and predicted a 9-star rating, demonstrating good sentiment recognition capabilities. |
6 | Comedy Movies: “The movie had some funny moments, but overall, it was just too predictable and cliché.” | Neutral | 6 | – | The model successfully handled neutral sentiment in comedy films and predicted a 6-star rating, demonstrating adaptability and diversity across different movie genres. |
