Rule-based sentiment analysis for financial news

Tan, Liim and Phang, Waisan and Chin, Kim On and Patricia Anthony, (2015) Rule-based sentiment analysis for financial news. In: Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 9-12 October 2015, City University of Hong KongKowloon Tong; Hong Kong.


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This paper describes a rule-based sentiment analysis algorithm for polarity classification of financial news articles. The system utilizes a prior polarity lexicon to classify the financial news articles into positive or negative. Sentiment composition rules are used to determine the polarity of each sentence in the news article, while the Positivity/Negativity ratio (P/N ratio) is used to calculate the sentiment values of the overall content of each news article. The performance of the Sentiment Analyser was evaluated using a dataset of manually annotated financial news articles collected from various online financial newspapers. The result was encouraging as our Sentiment Analyser obtained an overall F-Score of 75.6% for both positive and negative classifications

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Polarity Classification, Sentiment Analysis, Sentiment Composition
Subjects: H Social Sciences > HG Finance
Divisions: FACULTY > Faculty of Computing and Informatics
Depositing User: Unnamed user with email
Date Deposited: 16 Aug 2016 01:27
Last Modified: 10 Nov 2017 03:26

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