Title :
Recognition of NASDAQ stock symbols in Tweets
Author :
Kanungsukkasem, Nont ; Netisopakul, Ponrudee ; Leelanupab, Teerapong
Author_Institution :
Knowledge Manage. & Knowledge Eng. Lab. (KMAKE), King Mongkut´s Inst. of Technol. Ladkrabang (KMITL), Bangkok, Thailand
Abstract :
The massive volume of Twitter data has attracted much attention of researchers to study their correlation with stock market. Tweets with stock symbols can be identified by the prefix with dollar sign or by using some complex techniques. In this paper, we focus on discovering NASDAQ stock symbols in a stream of tweets. We propose a simple but effective methodology to recognize the stock symbols. Stock symbols from NASDAQ company list, WordNet, Wikipedia and sample tweets, as well as a classic method of collocation discovery are employed to filter stock-related tweets. Experimental evaluations show that our methodology outperforms the baseline approach for recognizing NASDAQ stock symbols.
Keywords :
social networking (online); NASDAQ company list; Twitter; Wikipedia; WordNet; baseline approach; classic method; collocation discovery; dollar sign; sample tweets; stock market; stock symbols; Channel hot electron injection; Electronic publishing; Encyclopedias; Internet; Organizations; Portfolios; NASDAQ; Named Entity Recognition; Stock Symbol; Tweet; Twitter;
Conference_Titel :
Knowledge and Smart Technology (KST), 2014 6th International Conference on
Conference_Location :
Chonburi
Print_ISBN :
978-1-4799-1423-4
DOI :
10.1109/KST.2014.6775386