Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user’s emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction.

随着全球各地大学和学生对电子学习的要求越来越高，虚拟学习环境的重要性正日益提高。本文研究如何使用情感分析技术自动评估该领域的用户体验。为此，已经建立了一个语料库，该语料库由总共583位用户（107位英语使用者和476位西班牙语使用者）针对不同课程中的三种学习管理系统给出了意见。所有收集的意见均由三个人工注释者手动标记了极性信息（正面，负面或中性），包括整体意见和句子级别。我们已经应用了经过不同语义域的语料库（Twitter语料库）训​​练的最新情感分析模型，来研究跨域模型在此任务中的使用。已经测试了基于深度神经网络（卷积神经网络，变压器编码器和注意力BLSTM模型）的跨域模型。为了对比我们的结果，还测试了用于同一任务的三个商业系统（MeaningCloud，Microsoft Text Analytics和Google Cloud）。获得的结果非常有希望，并且它们为继续进行将情感分析工具应用于用户体验评估的研究提供了见识。这是一个开创性的想法，它可以在与虚拟学习环境进行交互时提供对人类需求的更好，准确的了解，并且是朝着开发自动工具迈出的一步，该工具可以捕捉用户感知的反馈，从而设计出以用户情绪为中心的虚拟学习环境，信念，偏好，看法，回应，