  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.

Artificial Intelligence;Computation and Language