Sentiment Analysis for Film Reviews Based on Random Forest

Abstract

Sentiment analysis of film reviews has been a popular research topic, and previous researchers have investigated it on the IMDb dataset using a variety of machine learning models, however, the classification results are not satisfactory. Therefore this study aims to construct an effective sentiment analysis model and explore whether the Random Forest algorithm can be applied to the task of sentiment analysis on the IMDb dataset. In this study, after preprocessing the data, the Random Forest model was trained using a training set and evaluated using a test set to explore the accuracy and performance of the Random Forest model in film review sentiment analysis. The study also plotted word clouds to visualize the decision-making effect of the model. The Random Forest Model achieves an impressive 86% accuracy in sentiment analysis, while the word cloud plots provide a visually appealing depiction of its classification task. This indicates that the Random Forest model performs well in the film review sentiment analysis task with high accuracy and performance.