The sentiment analysis task has various applications in practice. In the
sentiment analysis task, words and phrases that represent positive and negative
emotions are important. Finding out the words that represent the emotion from
the text can improve the performance of the classification models for the
sentiment analysis task. In this paper, we propose a methodology that combines
the emotion lexicon with the classification model to enhance the accuracy of
the models. Our experimental results show that the emotion lexicon combined
with the classification model improves the performance of models.

本文提出一种将情感词典与分类模型相结合的方法，以提高情感分析任务的准确性，并实验证明此方法能够提高分类模型的性能。

通过情感词典方法提高越南文本的情感分析

Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts

Expressive speech synthesis, like audiobook synthesis, is still challenging
for style representation learning and prediction. Deriving from reference audio
or predicting style tags from text requires a huge amount of labeled data,
which is costly to acquire and difficult to define and annotate accurately. In
this paper, we propose a novel framework for learning style representation from
abundant plain text in a self-supervised manner. It leverages an emotion
lexicon and uses contrastive learning and deep clustering. We further integrate
the style representation as a conditioned embedding in a multi-style
Transformer TTS. Comparing with multi-style TTS by predicting style tags
trained on the same dataset but with human annotations, our method achieves
improved results according to subjective evaluations on both in-domain and
out-of-domain test sets in audiobook speech. Moreover, with implicit
context-aware style representation, the emotion transition of synthesized audio
in a long paragraph appears more natural. The audio samples are available on
the demo web.

本文提出了一种利用自我监督学习从大量纯文本中学习风格表达的新框架，采用情感词典和对比学习和深度聚类，最终在多风格变换文本到语音系统中得到了更好的结果。