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.

翻译：推文情感提取旨在提取句子中最能体现情感倾向的关键部分，以判断其情感为正面或负面。本研究致力于识别推文句子中引发情感波动的具体成分。为实现该目标，我们持续改进作者先前改进的Viterbi算法，使其能够接收预训练模型参数。我们引入置信度分数与置信度向量作为两项内部评估指标，在评估最终结果之前对模型内部进行量化评价。随后提出一种对此非参数模型进行微调的方法。研究发现，置信度分数向量能精确揭示预测状态中置信度最低的位置，并指示修正方案是否确实提升了置信度分数，或调优方向是否存在偏差，从而使模型获得高度可解释性。