Abstract
Telecom services are at the core of today's societies' everyday needs. The availability of numerous online forums and discussion platforms enables telecom providers to improve their services by exploring the views of their customers to learn about common issues that the customers face. Natural Language Processing (NLP) tools can be used to process the free text collected. One way of working with such data is to represent text as numerical vectors using one of many word embedding models based on neural networks. This research uses a novel dataset of telecom customers' reviews to perform an extensive study showing how different word embedding algorithms can affect the text classification process. Several state-of-the-art word embedding techniques are considered, including BERT, Word2Vec and Doc2Vec, coupled with several classification algorithms. The important issue of feature engineering and dimensionality reduction is addressed and several PCA-based approaches are explored. Moreover, the energy consumption used by the different word embeddings is investigated. The findings show that some word embedding models can lead to consistently better text classifiers in terms of precision, recall and F1-Score. In particular, for the more challenging classification tasks, BERT combined with PCA stood out with the highest performance metrics. Moreover, our proposed PCA approach of combining word vectors using the first principal component shows clear advantages in performance over the traditional approach of taking the average.
Abstract (translated)
电信服务是当今社会日常需求的核心。众多在线论坛和讨论平台的可用性使电信提供商能够通过探索客户意见来改进其服务，从而了解客户面临的常见问题。自然语言处理（NLP）工具可以用来处理收集到的自由文本。处理此类数据的一种方法是使用基于神经网络的多种词嵌入模型之一将文本表示为数值向量。这项研究利用了一套新颖的电信客户评论数据集，对不同词嵌入算法如何影响文本分类过程进行了详尽的研究。考虑了包括BERT、Word2Vec和Doc2Vec在内的几种最先进的词嵌入技术，并结合了几种分类算法。此外还探讨了特征工程和降维这一重要问题，并探索了几种基于PCA的方法。另外，这项研究还调查了不同词嵌入模型的能耗情况。 研究表明，某些词嵌入模型可以导致在精度、召回率和F1分数方面表现更好的文本分类器。特别是对于更具挑战性的分类任务，BERT与PCA结合使用时表现出最高的性能指标。此外，我们提出的基于PCA的方法，即利用第一主成分组合词向量，在性能上明显优于传统的平均方法。
URL
https://arxiv.org/abs/2504.13653
