1,954 research outputs found
The Effects of Twitter Sentiment on Stock Price Returns
Social media are increasingly reflecting and influencing behavior of other
complex systems. In this paper we investigate the relations between a well-know
micro-blogging platform Twitter and financial markets. In particular, we
consider, in a period of 15 months, the Twitter volume and sentiment about the
30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We
find a relatively low Pearson correlation and Granger causality between the
corresponding time series over the entire time period. However, we find a
significant dependence between the Twitter sentiment and abnormal returns
during the peaks of Twitter volume. This is valid not only for the expected
Twitter volume peaks (e.g., quarterly announcements), but also for peaks
corresponding to less obvious events. We formalize the procedure by adapting
the well-known "event study" from economics and finance to the analysis of
Twitter data. The procedure allows to automatically identify events as Twitter
volume peaks, to compute the prevailing sentiment (positive or negative)
expressed in tweets at these peaks, and finally to apply the "event study"
methodology to relate them to stock returns. We show that sentiment polarity of
Twitter peaks implies the direction of cumulative abnormal returns. The amount
of cumulative abnormal returns is relatively low (about 1-2%), but the
dependence is statistically significant for several days after the events
Traffic event detection framework using social media
This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595
The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio
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