Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning

TL;DR

This paper enhances a Viterbi-based sentiment extraction method for tweets by integrating transfer learning, confidence scoring, and fine-tuning, leading to more explainable and accurate identification of emotionally significant tweet segments.

Contribution

The study introduces a transfer learning approach and confidence scoring to improve a Viterbi-based sentiment extraction model for tweets, with a focus on explainability and fine-tuning.

Findings

Model achieves higher explainability through confidence scores.

Fine-tuning improves the accuracy of sentiment segment extraction.

Enhanced model better identifies emotionally significant tweet parts.

Abstract

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.