Micro-blogging sources such as the Twitter social network provide valuable
real-time data for market prediction models. Investors' opinions in this
network follow the fluctuations of the stock markets and often include educated
speculations on market opportunities that may have impact on the actions of
other investors. In view of this, we propose a novel system to detect positive
predictions in tweets, a type of financial emotions which we term
"opportunities" that are akin to "anticipation" in Plutchik's theory.
Specifically, we seek a high detection precision to present a financial
operator a substantial amount of such tweets while differentiating them from
the rest of financial emotions in our system. We achieve it with a three-layer
stacked Machine Learning classification system with sophisticated features that
result from applying Natural Language Processing techniques to extract valuable
linguistic information. Experimental results on a dataset that has been
manually annotated with financial emotion and ticker occurrence tags
demonstrate that our system yields satisfactory and competitive performance in
financial opportunity detection, with precision values up to 83%. This
promising outcome endorses the usability of our system to support investors'
decision making.

我们提出了一种新的系统，用于检测微博社交网络中的积极预测，这是一种金融情绪，类似于 Plutchik 理论中的 “预期”，通过应用自然语言处理技术提取有价值的语言信息，利用三层堆叠的机器学习分类系统和复杂的特征来实现高检测精度。在手动注释了金融情绪和股票发生标签的数据集上的实验结果表明，我们的系统在金融机会检测方面表现出了令人满意和有竞争力的性能，精度值高达 83%。这个有希望的结果证明了我们的系统在支持投资者决策方面的可用性。