Financial markets sentiment analysis: developing a specialized Lexicon

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.