Natural language processing in specific domains such as financial markets requires the knowledge of domain ontology. Therefore, developing a domain-specific lexicon to improve financial context sentiment analysis is noteworthy. In this paper, by exploring a wide related corpus along with using lexical resources, a hybrid approach is proposed to build a lexicon specialized for financial markets sentiment analysis. The lexicon is applied on a large dataset gathered from Twitter during nine months. Experimental results demonstrate a significant correlation between extracted sentiments from the corpus and market trends which indicates lexicon’s superior efficiency in measuring market sentiment compared with general-purpose dictionaries.

中文翻译：

---

金融市场情绪分析：开发专门的词典

金融市场等特定领域的自然语言处理需要领域本体的知识。因此，开发特定领域的词典来改进金融上下文情感分析是值得注意的。在本文中，通过探索广泛的相关语料库以及使用词汇资源，提出了一种混合方法来构建专门用于金融市场情绪分析的词典。该词典应用于九个月内从 Twitter 收集的大型数据集。实验结果表明，从语料库中提取的情绪与市场趋势之间存在显着相关性，这表明与通用词典相比，词典在衡量市场情绪方面具有更高的效率。