![]() He has been with the University of Manchester since 2002 and is currently a Senior Lecturer in the School of Computer Science and the School of Informatics. degree in Computation from UMIST (currently the University of Manchester), Manchester, U.K., in 1996. ![]() degree in Control Theory and Operation Research from Xiamen University, Xiamen, China, and the Ph.D. Mellon Foundation and NSF funded MESUR project which aims to expand the quantitative tools available for the assessment of scholarly impact. He is presently the Principal Investigator of the Andrew W. He has extensively published on these subjects as well as matters relating to adaptive information systems architecture. His present research interests are sentiment tracking, computational social science, usage data mining, informetrics, and digital libraries. Mellon Foundation, National Science Foundation, Library of Congress, National Aeronautics and Space Administration and the Los Alamos National Laboratory. His research has been funded by the Andrew W. He has taught courses on Data Mining, Information Retrieval and Digital Libraries. He obtained his PhD in Experimental Psychology from the University of Brussels in 2001 on the subject of cognitive models of human hypertext navigation. He was formerly a staff scientist at the Los Alamos National Laboratory from 2005-2009, and an Assistant Professor at the Department of Computer Science of Old Dominion University from 2002 to 2005. Johan Bollen is associate professor at the Indiana University School of Informatics and Computing. We find an accuracy of 86.7% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error (MAPE) by more than 6%. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). ![]() We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. Does this also apply to societies at large, i.e. Stops should be kept tight, with a move below $53.91 on a closing basis likely to send the Twitter stock price lower still.Ī deeper decline could see the market retreat to the August 2018 highs at $46.25, 17% below the current level.ĭon’t miss a beat! Follow us on Telegram and Twitter.Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. ![]() Catching a falling knife is a high-risk strategy. Bulls could look to add on weakness as support approaches. ![]() Should the projected move play out, a buying opportunity may arise. Lending further support is the 29th of October high at $53.91. An ascending trend line in place from the $20.01 lows of March 2020 is currently seen at $54.15 This would see the share price test what I consider to be a major support level. The Twitter stock price looks set to continue on its path lower, with a potential price target of around $54.00 per share, 6.35% below its current level. This news triggered a violent sell-off, sending the Twitter stock price $6.49 lower to $57.60, erasing close to $6 Billion from the social media giants’ valuation. In the accompanying trading statement, Twitter revealed its user growth in the first three months of 2020 had slowed to its weakest in two years. The forward guidance and a slowdown in user growth left the market disappointed, sending the Twitter price sharply lower in after-hours trading. On the face of it, the headline data came in pretty much as expected. ![]()
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