An experimental study of sentiment classification using deep-based models with various word embedding techniques

Nowadays, sentiment analysis is concerned with identifying and analysing text sentiment. Sentiment analysis has been used in many fields because of its applications in various domains. In the last decade, with the success of machine learning and deep learning methods, many machine- and deep-based sentiment classification have been developed and performed well on various issues. Moreover, word embeddings are important for machine learning and deep learning models since they provide input features in downstream language tasks. This paper presents a comprehensive review of word embeddings and deep learning models. Additionally, we conduct an experimental study of sentiment classification using various deep learning models and word embeddings, in which five deep learning models with four embedding techniques are compared on eight benchmark datasets. In other words, 20 models are evaluated on datasets. Finally, we discuss the performance of models from different perspectives.

当下，情感分析（sentiment analysis）旨在识别与分析文本情感倾向。由于其在多领域的应用价值，情感分析已被广泛应用于诸多行业场景。近十年来，随着机器学习（machine learning）与深度学习（deep learning）方法的蓬勃发展，诸多基于机器学习与深度学习的情感分类模型被相继提出，并在各类任务中取得了优异表现。此外，词嵌入（word embeddings）对机器学习与深度学习模型至关重要，因其可为下游自然语言任务提供输入特征。本文首先对词嵌入与深度学习模型开展了系统性综述研究。此外，本文针对不同深度学习模型与词嵌入结合的情感分类任务开展了实验研究：将五种深度学习模型搭配四种嵌入技术，在八个基准数据集（benchmark datasets）上进行对比实验。换言之，总计20种模型组合在上述数据集上完成了性能评估。最后，本文从多个维度对各类模型的性能表现展开了讨论与分析。