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首页> 外文期刊>Journal of Intelligent Information Systems >Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction
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Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction

机译：词义消歧在新闻标题情绪分析中的应用：外汇市场预测的一种应用方法

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摘要

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.
机译：文本内容的情感分析已成为市场预测的流行方法。但是，缺少词义歧义处理的过程使人们怀疑是否正确识别了上下文表达的情感。同时，许多关于自然语言处理的研究都集中在词义消歧上。但是，这两个逻辑相关的研究领域之间的联系薄弱。因此，出于两个动机，我们提出了一种外汇市场预测系统，该系统利用新闻头条的情绪分析中的词义歧义消除并预测货币对的定向运动。第一个动机是实施新颖的词义消歧，它可以确定新闻标题中所有重要词的正确含义。这项工作的主要贡献是，它使第一个动机成为可能，是引入了称为相关光泽度检索，相似性阈值，动词名词化的新颖方法，以及减少执行时间的优化措施。第二个动机是证明确定文本内容中重要单词的正确含义可以改善上下文确定所传达的情感确定，因此可以改善任何基于情感分析的应用。将单词歧义消除包含在所提议的系统中证明了第二动机的实现。使用相同的数据集进行的测试证明，所提出的系统优于为市场预测所提出的最佳系统之一（就我们所知），其准确性从83.33％提高到91.67％。充分提供了系统重现的细节。

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