## 3 Methods
### Course Attributes Extraction  
Previous studies mainly used the LDA topic model or word embedding to extract and classify product attributes, where word embedding methods do not require manual interpretation of each topic [49]. Therefore, we used the word embedding method to extract and classify course attributes. Our approach consisted of the following steps:  
Step 1: Nouns were extracted from all the reviews for a course, that is, words that start with \"n\" in the POS tags. Course attributes are usually nouns, so we extract the nouns in course reviews as our alternative attribute words. The number of extracted nouns is usually large, and the main attributes of a course may be limited to a few, so we needed to find a way to cluster these nouns and then treat each cluster as an attribute. Clustering algorithms usually take input in vector form, so we needed to convert nouns into vector form.  
Step 2: To convert the selected nouns into a vectorized format, we employed the FastText word embedding approach. Word embedding is a technique that maps words onto a vector space, allowing each word to be represented by a unique vector. Furthermore, words with similar meanings tend to have word vectors that are proximate in Euclidean space. This characteristic makes word embedding an ideal approach for converting words into vectors and subsequently clustering them. A representative word embedding method is FastText [62], which considers the morphology of words, that is, the n-grams of word characters. For example, the 2-g of the word \"where\" includes \"wh\", \"he\", \"er\", and \"re\". FastText for words that do not occur during training can be represented by the sum of its character n-grams, which allows FastText to represent any word. The pre-trained Chinese  
Figure 1: Overall steps of our method.  
FastText word vectors ([[https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300)]([https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300)). bin.gz (accessed on 1 February 2024)) are used to represent the nouns in our work.  
Step 3: For noun clustering, we employed a combined approach incorporating Affinity Propagation (AP) clustering [63] and manual intervention. AP is one of the most classical and widely used clustering methods that clusters the close words into the same cluster and the far words into different clusters by calculating the distance. A significant advantage of AP clustering is that it does not require the specification of the number of clusters upfront. After AP clustering, we manually proofread the clustering results to make the meaning of words in each cluster as close as possible and delete meaningless words.  
Step 4: The words in each cluster after clustering are induced to summarize the course attributes. By summarizing the nouns in each cluster into attributes, we can know the main attributes of a course that learners pay attention to.  
Step 5: The nouns' frequency of different attributes was counted, as was the total word frequency of an attribute. This approach is based on the assumption that a higher frequency of an attribute word indicates greater learner attention and importance placed on that specific attribute.