Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of peo...Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post. Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic. Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.展开更多
Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a finan...Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a financial crisis. Therefore, it is particularly important to identify whether a candidate is eligible for receiving a loan. In this paper, we apply Random Forest and XGBoost algorithms to train the prediction model and compare their performance in prediction accuracy. In the feature engineering part, we use the variance threshold method and Variance Inflation Factor method to filter out unimportant features, and then we input those selected features into Random Forest and XGBoost models. It turns out that Random Forest and XGBoost show little difference in the accuracy of their predictions since both get high accuracy of around 0.9 in the loan default cases.展开更多
文摘Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post. Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic. Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.
文摘Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a financial crisis. Therefore, it is particularly important to identify whether a candidate is eligible for receiving a loan. In this paper, we apply Random Forest and XGBoost algorithms to train the prediction model and compare their performance in prediction accuracy. In the feature engineering part, we use the variance threshold method and Variance Inflation Factor method to filter out unimportant features, and then we input those selected features into Random Forest and XGBoost models. It turns out that Random Forest and XGBoost show little difference in the accuracy of their predictions since both get high accuracy of around 0.9 in the loan default cases.
