对比多种模型，发现VADER最适合分析特立尼达和多巴哥民众对进口食品的舆情。
Machine Learning for Sentiment Analysis of Imported Food in Trinidad and Tobago
- 用CNN、LSTM、VADER和RoBERTa对比分析推文情感
- VADER在多分类和二分类任务中准确率最高
- 发现疫情前后民众对进口食品态度明显变化，可为政策提供依据
本研究探讨了多种机器学习算法（CNN、LSTM、VADER和RoBERTa）在特立尼达和多巴哥进口食品相关推文情感分析中的表现。数据集涵盖2018至2024年的推文，分为不平衡、平衡和时间序列子集，以评估数据平衡及新冠疫情对情感趋势的影响。共开展十组实验，评估不同配置下的模型性能。结果显示，VADER在多分类和二分类情感识别中均优于其他模型。研究揭示了疫情前后情感趋势的显著变化，对进口政策制定具有重要参考价值。
原文摘要 · Abstract (English)
This research investigates the performance of various machine learning algorithms (CNN, LSTM, VADER, and RoBERTa) for sentiment analysis of Twitter data related to imported food items in Trinidad and Tobago. The study addresses three primary research questions: the comparative accuracy and efficiency of the algorithms, the optimal configurations for each model, and the potential applications of the optimized models in a live system for monitoring public sentiment and its impact on the import bill. The dataset comprises tweets from 2018 to 2024, divided into imbalanced, balanced, and temporal subsets to assess the impact of data balancing and the COVID-19 pandemic on sentiment trends. Ten experiments were conducted to evaluate the models under various configurations. Results indicated that VADER outperformed the other models in both multi-class and binary sentiment classifications. The study highlights significant changes in sentiment trends pre- and post-COVID-19, with implications for import policies.
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