Machine Learning for Sentiment Analysis of Imported Food in Trinidad and Tobago

TL;DR

This study compares machine learning algorithms like CNN, LSTM, VADER, and RoBERTa for sentiment analysis of Twitter data about imported food in Trinidad and Tobago, revealing VADER's superior performance and notable sentiment shifts due to COVID-19.

Contribution

It provides a comprehensive evaluation of multiple sentiment analysis models on real-world social media data, identifying the most effective approach for monitoring public opinion on imported food.

Findings

VADER outperformed other models in accuracy.

Sentiment trends changed significantly after COVID-19.

Data balancing impacted model performance.

Abstract

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.