Summary of Machine Learning For Sentiment Analysis Of Imported Food in Trinidad and Tobago, by Cassandra Daniels and Koffka Khan
Machine Learning for Sentiment Analysis of Imported Food in Trinidad and Tobago
by Cassandra Daniels, Koffka Khan
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the performance of four machine learning algorithms (CNN, LSTM, VADER, and RoBERTa) for sentiment analysis on Twitter data related to imported food items in Trinidad and Tobago. The study compares the accuracy and efficiency of the algorithms, explores their optimal configurations, and evaluates their potential applications in monitoring public sentiment and its impact on the import bill. The dataset includes tweets from 2018 to 2024, divided into imbalanced, balanced, and temporal subsets to assess the effect of data balancing and the COVID-19 pandemic on sentiment trends. The results show that VADER outperformed the other models in multi-class and binary sentiment classifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well some machine learning algorithms can figure out what people are feeling about food imports from Twitter. They compared four different algorithms (CNN, LSTM, VADER, and RoBERTa) to see which one does best. The data they used includes tweets from 2018 to 2024, and they looked at how the algorithm’s performance changes when the data is balanced or unbalanced. They also checked if the COVID-19 pandemic changed how people felt about food imports. Overall, the results show that VADER did better than the other algorithms. |
Keywords
» Artificial intelligence » Cnn » Lstm » Machine learning
