Purpose

Sina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.

Design/methodology/approach

An enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.

Findings

By comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.

Originality/value

Two sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

中文翻译：

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探索公众对商品市场的情绪：新浪微博和推特上用户行为的比较研究

目的

新浪微博和推特是拥有数十亿用户的顶级微博平台。因此，这两个平台可用于了解公众情绪。在本文中，作者希望讨论如何在新浪微博和Twitter上生成和比较公众情绪。讨论了公众情绪对商品市场的预测能力，作者希望解决在预测原油价格时如何在新浪微博和Twitter之间进行选择的问题。

设计/方法/方法

实施了一种考虑了词权重的增强型潜在Dirichlet分配模型，以从新浪微博和Twitter生成主题。格兰杰因果关系检验和长期短期记忆神经网络模型用于证明新浪微博和推特上的公众情绪与商品合同相关。

发现

通过比较新浪微博和Twitter上的主题和公众情绪，作者发现这两个网站上的用户行为存在显着差异。此外，作者证明新浪微博和推特上的公众情绪分别与上海国际能源交易所和纽约商品交易所的原油合约价格相关。

创意/价值

介绍了中文（新浪微博）和英文（Twitter）帖子的两种情感分析方法，这些方法可重用于其他语义分析任务。此外，作者为商品市场的实际参与者提供了一种预测模型，并介绍了针对某些预测任务在新浪微博和Twitter之间进行选择的方法。