Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction

Sentiment analysis of textual content has become a popular approach for market prediction. However, lack of a process for word sense disambiguation makes it questionable whether the sentiment expressed by the context is correctly identified. Meanwhile, many studies in natural language processing have focused on word sense disambiguation. However, there has been a weak link between the two logically relevant fields of study. Therefore, with two motivations, we propose a system for FOREX market prediction that exploits word sense disambiguation in sentiment analysis of news headlines and predicts the directional movement of a currency pair. The first motivation is the implementation of a novel word sense disambiguation that can determine the proper senses of all significant words in a news headline. The main contributions of this work that make the first motivation possible, are the introduction of novel approaches termed Relevant Gloss Retrieval, Similarity Threshold, Verb Nominalization, and also optimization measures to decrease execution time. The second motivation is to prove that determination of proper senses of significant words in textual contents can improve the determination of sentiment, conveyed by the context, and consequently any application based on sentiment analysis. Inclusion of the word sense disambiguation into the proposed system proves the achievement of the second motivation. Carried out tests with the same dataset prove that the proposed system outperforms one of the best systems (to our best knowledge) proposed for market prediction and improves accuracy from 83.33% to 91.67%. The detail for reproduction of the system is amply provided.