###### Abstract  
Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners' perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand factors that can predict learners' satisfaction and their perceptions of these factors. To do this, this study employs a quantitative research methodology based on sentiment analysis and deep learning. Learners' review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners' perceptions of these factors. A total of 186,738 review sentences associated with 13 subject areas are analyzed, and consequently, seven course factors that learners frequently mentioned are found. These factors include: \"Platforms and tools\", \"Course quality\", \"Learning resources\", \"Instructor\", \"Relationship\", \"Process\", and \"Assessment\". Subsequently, each factor is assigned a sentiment value using lexicon-driven methodologies, and the topics that can influence learners' learning experiences the most are decided. In addition, learners' perceptions across different topics and subjects are explored and discussed. The findings of this study contribute to helping MOOC instructors in tailoring course design and implementation to bring more satisfactory learning experiences for learners. learners' perceptions; MOOCs; review data analysis; deep learning; sentiment analysis Article Understanding Learners' Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], H.  
obtain limited information, and the analysis results depend heavily on the questionnaire design. Additionally, it usually takes a long time to collect all necessary data, and thus a timely and dynamic analysis is impossible. Nowadays, many MOOC providers and platforms have integrated interactive technologies to allow learners to freely express their perceptions of and satisfaction with different aspects concerning MOOC design and implementation. This source of data is essential for tracking MOOCs' performance; thus, it is crucial to exploit rich information to allow a timely, dynamic, and automatic understanding of a MOOC's performance in satisfying learners [10]. Previous studies on MOOC data analysis for understanding learner satisfaction focus mainly on learners' demographics, personal characteristics, and disposition (e.g., [11; 12; 13]). Research on course review data analysis is primarily conducted based on qualitative analysis methodologies. Owing to the contininugly growing number of learner-generated reviews, it would be very time-consuming and labor-intensive to detect topics from learner-produced review data through manual evaluation [14]. Thus, alternative analysis methodologies based on natural language processing (NLP) and machine learning should be considered. Although there are studies that have touched upon topic mining, learner sentiment detection, and topic classification in the context of MOOC review analysis (e.g., [15; 16; 17; 18; 19; 20]), there are a lack of comprehensive and automated course review data analyses from topical and sentimental perspectives, and especially a lack of studies combining deep learning and sentiment analysis methodologies. Additionally, since learners' satisfaction can vary across subject areas due to differences in study objectives, different modes of assessment, etc. [21], and a comparison of learner dissatisfaction across different subjects are needed. To that end, the present study aims to understand learner satisfaction with MOOCs based on course review data analysis using sentiment analysis and deep neural networks, with a particular focus on the factors concerning MOOC design and implementation that can lead to learner satisfaction.