Improving Campus Social Networks: A New Way to Track Opinions

Public opinion can spread like wildfire on campus social networks. Traditional methods of monitoring and searching these networks can be slow and wasteful. To solve this problem, researchers have introduced a new approach. They've combined dynamic deletion with the shortest path algorithm to classify network texts more efficiently. This method helps to prevent resource waste. Next, they've built a unique text sentiment classification model using a mix of convolutional neural networks and recurrent neural networks. But wait, there's more! They've also added an attention mechanism to improve the model's classification power. The results? The dynamic deletion-shortest path algorithm had impressive scores: an average precision rate of 97. 30%, a recall rate of 79. 55%, and an F-value of 87. 53%. Plus, it worked at a speed of 397 KB/s, outperforming traditional methods. When it came to classifying long texts, the variant recurrent neural network model showed high accuracy and F-values, both over 84%. The addition of the attention mechanism boosted the text sentiment classification model's accuracy by 3. 89%. In simple terms, the new dynamic deletion-shortest path algorithm and the sentiment classification model with the attention mechanism are game-changers. They offer superior performance and can be incredibly useful for making decisions about opinion risks in campus social networks.