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 for enhancing the accuracy of the models. Our experimental results show that the emotion lexicon combined with the classification model improves the performance of models.

翻译：情感分析任务在实践中有各种不同的应用。 在情感分析任务中, 代表积极和消极情绪的词句和词句很重要。 找到文本中代表情感的词句可以改善情绪分析任务分类模型的性能。 在本文中, 我们提出一种方法, 将情感词汇和分类模型结合起来, 以提高模型的准确性。 我们的实验结果表明, 情感词汇与分类模型相结合, 能够改善模型的性能 。