# Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features
## 4 Experimental Section  
### Datasets and Evaluation Metric  
Given the paucity of Chinese datasets with comment-reply structures and sentiment labels at the comment level, data for this article were obtained from the Toutiao media platform. The site includes news articles, reviews, and information on the corresponding users of reviews. The news articles focus on multiple areas such as technology, finance and entertainment, with a particular focus on Huawei-related news published between March and December 2019. Multiple comments are included in each news article, and these comments reflect users' attitudes toward the content of the news article. For this reason, this paper categorizes the sentiment of comments into positive, neutral, and negative. An example of a conflict occurring in a news article is shown in Figure 3. In this example, the news article \(N\) belongs to the topic \(T\), and multiple comments in the news article express different views of the users. Figure 3 shows that comments are labelled as positive, neutral or negative depending on the user's attitude towards the information. There is a case where the comment \(C_{3-1}\) expresses a negative sentiment, but in fact supports the content of the news article \(N\). This is because in the structure of the comment tree, \(C_{3-1}\) refutes the viewpoint of \(C_{3}\) towards news article \(N\), while \(C_{3}\) expresses an opposing opinion towards news article N. Therefore, we consider comments like \(C_{3-1}\) to have a positive sentiment towards news article N. In data preprocessing, we manually confirmed each comment's label based on its content and contextual feedback. If one of the two comments expresses a positive attitude toward the news while the other one expresses a negative attitude, we consider this to be a conflict of interest. Edges between comments with no conflict indicate that both comments have the same sentiment. Furthermore, this paper does not consider edges between neutral comments and other types of comments in the news. As shown in Table 1, we collected a total of 511 news articles and 103,787 reviews for the entire dataset. In some special cases, there are comments without content or publication time. We conjecture that these comments have been removed by users. In the case of such comments, their labels can only be determined on the basis of the labels of their child comments. Depending on the needs of the task, the focus of this paper is on the conflicting content in hot news among all news stories. For this purpose, we extracted three data subsets for experimentation. In particular, we first found the two most active users who made the most comments under different news stories, denoted by \(u_{1}\) and \(u_{2}\), respectively. To simplify the notation, we represent the actuality and corresponding comments commented by \(u_{1}\) as  
Figure 3: News and related Comments. Toutiao#1, and the other subset consisting of all the news items commented by \(u_{2}\) such as Toutiao#2. Toutiao#1 is connected to each news item in Toutiao#2 in this paper to form a larger news commentary dataset. The text-feature vector of the Topic node is the vectorised representation of the word \"Huawei\". Table 1 shows the statistics for the two subsets and the entire dataset. The evaluation criteria used in this article are _accuracy_, _macro-precision_, _macro-recall_, and _macro-F_1.