Abstract
The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.
Highlights
With the rapid development of Internet technology, the online learning platform Massive Open Online Courses (MOOC) has attracted wide attention
The accuracy rate was adopted as the standard to evaluate the results of putting the samples of comments for MOOC courses into sentiment analysis
While CNN-LSTM ignored the issue of polysemous words, which resulted in its poor performance in terms of the data set of comments, ALBERT could generate different word vectors according to different semantic dynamics, which solved a series of problems such as polysemous words and the upgraded significance of familiar words, so as to obtain a more accurate results of sentiment analysis of comments
Summary
With the rapid development of Internet technology, the online learning platform Massive Open Online Courses (MOOC) has attracted wide attention. A high labor cost was inevitable when extracting features manually with the traditional machine learning method, so it was not suitable to be used for exploring and analyzing massive data of curricular comments at the present stage. Devlin et al [5] proposed the BERT textual pre-training model which operated well in performing the task of textual classification. It has abandoned the traditional structure of convolution and recurrent neural network (RNN), and used the Transformer structure to build the overall network model. There are still few sentiment analyses which were made by using ALBERT pre-training model to study the comments for MOOC courses. We proposed the ALBERT-BiLSTM model for the sentiment analysis of comments for MOOC courses
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