Sentiment analysis, a crucial component of natural language processing (NLP), involves the classification of subjective information by extracting emotional content from textual data. This technique plays a significant role in the movie industry by analyzing public opinions about films. The present research addresses a gap in the literature by conducting a comparative analysis of various machine learning algorithms for sentiment analysis in film reviews, utilizing a dataset from Kaggle comprising 50,000 reviews. Classifiers such as Logistic Regression, Multinomial Naive Bayes, Linear Support Vector Classification (LinearSVC), and Gradient Boosting were employed to categorize the reviews into positive and negative sentiments. The emphasis was placed on specifying and comparing these classifiers in the context of film review sentiment analysis, highlighting their respective advantages and disadvantages. The dataset underwent thorough preprocessing, including data cleaning and the application of stemming techniques to enhance processing efficiency. The performance of the classifiers was rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score. Among the classifiers, LinearSVC demonstrated the highest accuracy at 90.98%. This comprehensive evaluation not only identified the most effective classifier but also elucidated the contextual efficiencies of various algorithms. The findings indicate that LinearSVC excels at accurately classifying sentiments in film reviews, thereby offering new insights into public opinions on films. Furthermore, the extended comparison provides a step-by-step guide for selecting the most suitable classifier based on dataset characteristics and context, contributing valuable knowledge to the existing literature on the impact of different machine learning approaches on sentiment analysis outcomes in the movie industry.

In the realm of urban public affairs management, the necessity for accurate and intelligent distribution of resources has become increasingly imperative for effective social governance. This study, drawing on crime data from Chicago in 2022, introduces a novel approach to public affairs distribution by employing Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and their integration. By extensively preprocessing textual, numerical, boolean, temporal, and geographical data, the proposed models were engineered to discern complex interrelations among multidimensional features, thereby enhancing their capability to classify and predict public affairs events. Comparative analysis reveals that the hybrid LSTM-MLP model exhibits superior prediction accuracy over the individual LSTM or MLP models, evidencing enhanced proficiency in capturing intricate event patterns and trends. The effectiveness of the model was further corroborated through a detailed examination of training and validation accuracies, loss trajectories, and confusion matrices. This study contributes a robust methodology to the field of intelligent public affairs prediction and resource allocation, demonstrating significant practical applicability and potential for widespread implementation.