Automatic sentiment polarity analysis of financial news across languages and markets
The increasing amount of sentiments disseminated by traditional and social media and their impact on human societies including financial markets made the automatic detection and analysis of sentiments an important research area in academia and industry. In this thesis, techniques rooted in corpus linguistics and language for special purposes (LSP) have been used to develop a method for the automatic sentiment polarity analysis of financial news. Contrary to the existing practice in sentiment analysis where manual analysis is usually required to construct sentiment lexicons, the novelty introduced here is that the method can automatically extract a set of domain specific keywords and a set of polarity bearing words from training corpora comprising financial news across languages. The method requires only general news corpora for extracting keywords and historical market data time-series for extracting polar words. The method also automatically labels the extracted polar words as positive or negative and extracts the patterns in which these words are used. The language dependence of the use of polarity laden words was examined by looking at such patterns of usage in two typologically distinct languages: English
Almas, Yousif Yaqoob
Automatic sentiment polarity analysis of financial news across languages and markets. The increasing amount of sentiments disseminated by traditional and social media and their impact on human societies including financial markets made the automatic detection and analysis of sentiments an important research area in academia and industry. In this thesis, techniques rooted in corpus linguistics and language for special purposes (LSP) have been used to develop a method for the automatic sentiment polarity analysis of financial news. Contrary to the existing practice in sentiment analysis where manual analysis is usually required to construct sentiment lexicons, the novelty introduced here is that the method can automatically extract a set of domain specific keywords and a set of polarity bearing words from training corpora comprising financial news across languages. The method requires only general news corpora for extracting keywords and historical market data time-series for extracting polar words. The method also automatically labels the extracted polar words as positive or negative and extracts the patterns in which these words are used. The language dependence of the use of polarity laden words was examined by looking at such patterns of usage in two typologically distinct languages: English
automatic sentiment polarity analysis financial news across_language market increase_amount sentiment disseminate traditional social medium impact human_society include financial market make automatic detection analysis sentiment important area academia industry technique root corpus_linguistics language special purpose lsp use develop method automatic sentiment polarity analysis financial news contrary exist practice sentiment analysis manual analysis usually require construct sentiment lexicon novelty introduce method automatically extract set domain_specific keywords set polarity bearing word train corpus_comprise financial news across_language method require general news corpus extract keywords historical market data time_series extract polar word method also automatically label extract polar word positive_negative extract pattern word_use language dependence use polarity laden word examine look pattern usage typologically distinct language english