Financial investors make trades based on available information. Previous research has proved that microblogs are a useful source for supporting stock market decisions. However, the financial domain lacks specific sentiment lexicons that could be utilized to extract the sentiment from these microblogs. In this research, we investigate automatic approaches that can be used to build financial sentiment lexicons. We introduce weighted versions of the Pointwise Mutual Information approaches to build sentiment lexicons automatically. Furthermore, existing sentiment lexicons often neglect negation while building the sentiment lexicons. In this research, we also propose two methods (Negated Word and Flip Sentiment) to extend the sentiment building approaches to take into account negation when constructing a sentiment lexicon. We build the financial sentiment lexicons by leveraging 200,000 messages from StockTwits. We evaluate the constructed financial sentiment lexicons in two different sentiment classification tasks (unsupervised and supervised). In addition, the created financial sentiment lexicons are compared with each other and with other existing sentiment lexicons. The best performing financial sentiment lexicon is built by combining our Weighted Normalized Pointwise Mutual Information approach with the Negated Word approach. It outperforms all the other sentiment lexicons in the two sentiment classification tasks. In the unsupervised sentiment classification task, it has, on average, a balanced accuracy of 69.4%, and in the supervised setting, a balanced accuracy of 75.1%. Moreover, the various sentiment classification tasks confirm that the sentiment lexicons could be improved by taking into account negation while building the sentiment lexicons. The improvement could be made by using one of the proposed methods to incorporate negation in the sentiment lexicon construction process.

金融投资者根据可用信息进行交易。先前的研究证明，微博是支持股票市场决策的有用资源。但是，金融领域缺少可用于从这些微博中提取情感的特定情感词典。在这项研究中，我们调查了可用于构建财务情绪词典的自动方法。我们介绍了点向互信息方法的加权版本，以自动构建情感词典。此外，现有的情感词典通常在构建情感词典时会忽略否定。在这项研究中，我们还提出了两种方法（否定单词和翻转情感）来扩展情感构建方法，以在构建情感词典时考虑到否定。我们利用来自StockTwits的200,000条消息来构建财务情感词典。我们在两种不同的情感分类任务（无监督和有监督）中评估构建的金融情感词典。另外，将所创建的金融情感词典与彼此以及与其他现有情感词典进行比较。通过将我们的加权归一化点向互信息方法与否定词方法相结合，可以构建性能最佳的金融情感词典。在这两个情感分类任务中，它的表现优于所有其他情感词典。在无监督的情感分类任务中，平均精度为69.4％，在无监督的环境中，平衡精度为75.1％。而且，各种情感分类任务证实，在构建情感词典时可以通过考虑否定来改进情感词典。可以通过使用一种提议的方法将否定纳入情感词典构建过程中来进行改进。