# Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning

arXiv: https://arxiv.org/abs/2308.05973 · HTML: https://mlsanity.com/paper/2308.05973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://mlsanity.com/paper/1805.05181/) (2018)
- [External knowledge transfer deployment inside a simple double agent Viterbi algorithm](https://mlsanity.com/paper/2110.00433/) (2021)
- [Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text](https://mlsanity.com/paper/2110.15707/) (2021)

## Abstract (arXiv)

Tweet sentiment extraction extracts the most significant portion of the sentence, determining whether the sentiment is positive or negative. This research aims to identify the part of tweet sentences that strikes any emotion. To reach this objective, we continue improving the Viterbi algorithm previously modified by the author to make it able to receive pre-trained model parameters. We introduce the confidence score and vector as two indicators responsible for evaluating the model internally before assessing the final results. We then present a method to fine-tune this nonparametric model. We found that the model gets highly explainable as the confidence score vector reveals precisely where the least confidence predicted states are and if the modifications approved ameliorate the confidence score or if the tuning is going in the wrong direction.