## 4 Methodology
### Data Analysis  
We analyzed our data using three processes, i.e., data preprocessing, sentiment analysis, and topic modeling. Data preprocessing was performed with the Regex library ([[https://regexlib.com](https://regexlib.com)]([https://regexlib.com](https://regexlib.com)); accessed on 12 May 2022) to tokenize, clean, and format the text of each subset. The first step was to tokenize the data, removing special characters and hyperlinks and segmenting sentences into individual words. Then, all stopwords, i.e., commonly used non-emotional words (e.g., I, a, the), were removed. We adapted NLTK's _stopwords_ corpus to identify and filter the stopwords. Next, the remaining tokens were standardized, with all capital letters converted into lowercase, contractions reverted to their composite words (e.g., don't to do not), and numbers replaced by their corresponding word (e.g., 1 to one). We adapted NLTK's _tokenize_ package ([[https://www.nltk.org/api/nltk.tokenize.html](https://www.nltk.org/api/nltk.tokenize.html)]([https://www.nltk.org/api/nltk.tokenize.html](https://www.nltk.org/api/nltk.tokenize.html)); accessed on 12 May 2022) to implement this standardized process. The final step, called lemmatization, initialized standardized tokens into their root word form (e.g., taught to teach). Sentiment analysis is an approach for automatically extracting and classifying sentiment from textual documents using natural language processing (NLP), textual analysis, and computational techniques [30]. This approach can avoid taking keywords out of context and consider users' feelings at the post level. This study selected VADER (Valence  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subreddit** & **Populations** & **Creation Time** & **Subscribers** & **Descriptions** \\ \hline r/funnyonlinclasses & Learners, Educators, Parents & 28 March 2020 & 14.8k & Anything funny about online classes in the wake of the COVID-19 quarantine. Biggest community forum run by teenagers for teenagers. & \\ r/teenagers & Learners & 27 February 2010 & 30.8k &
\begin{tabular}{c} English Aware Dictionary and sEntiment Reasoner) because it is specifically tuned to the social media context and supports web expressions such as emoticons and shorthand [31]. The result returned by VADER was a dictionary of four keys: _neg_, _neu_, _pos_, and _compound_. _Compound_ is computed by normalizing the other three scores. By adjusting the threshold based on the dataset to the compound value, the sentiment of a complete post could be split into negative, neutral, and positive. To evaluate the effectiveness of the application of VADER to our dataset, we sampled 100 pieces of data from the global data set and labeled it manually into three classes, i.e., \"negative,\" 'neutral,' and 'positive,' and calculated the prediction accuracy. We used accuracy as the metric because the classes of our data set were relatively balanced in terms of label distributions. In addition, there was no difference between the classes for decision-making implications in our research context, as we only used the classification results to plot the sentiment-time line chart. Therefore, we did not calculate the terms of precision and recall. We used the following equation to calculate the accuracy. \[Accuracy=(cp+0.5 ipn)/total\ numberof\ samples \tag{1}\]  
The \(cp\) in the above equation (1) indicates the correct number of predictions, and _ipn_ indicates the incorrect number of predictions involving the 'neutral' manual labels. The reason to add the _0.5 ipn_ part to calculate the accuracy is to cater to the ranking relations in the three classes, i.e., \"negative,' 'neutral,' and 'positive.' Therefore, it will be fairer to assign half of the marks to the failed 'neutral' cases, compared to giving the same zero mark to the failed cases predicting \"negative\" while the true value is \"positive\" and vice versa. Topic modeling is a statistical approach for discovering \"topics\" hidden in the text corpus, which is the cleaned dataset in this research.