基于改进LDA算法的电力用户咨询文本分类算法

Text classification algorithm of power user consultation based on improved LDA algorithm

摘要:

针对目前情感极性分析中电力咨询短文本的准确性较低的问题，提出一种基于改进潜在狄利克雷分配(LDA)算法的电力用户咨询文本分类算法。在分析电力咨询短文本与情感的关联关系基础上，定义了基于情感词共现袋、主题特殊词以及主题关系词的概念；为提高语义分析的质量，设计了改进LDA算法的电力用户咨询文本分类算法执行流程。实验表明，所提模型表现出优异性能，平均精确度和平均召回率为90.91%和85.03%。所提模型可充分发挥多模型集成优势，有效提升模型性能。

Abstract:

In response to the current issue of low accuracy in sentiment polarity analysis of short texts in power consulting, this paper proposes an improved Latent Dirichlet Allocation (LDA) algorithm-based classification algorithm for power user consulting texts. Based on the analysis of the relationship between power consulting short texts and sentiment, concepts such as sentiment word co-occurrence bags, topic-specific words, and topic relationship words are defined. To improve the quality of semantic analysis, an execution process for the improved LDA algorithm for classifying power user consulting texts is designed. Experiments show that the proposed model demonstrates excellent performance, with an average precision of 90.91% and an average recall rate of 85.03%. The proposed model can fully leverage the advantages of multi-model integration, effectively enhancing the model performance.