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Twitter mood predicts the stock market.

Creativity in Context: Update to "The Social Psychology of Creativity"

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.

Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

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Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu

Group Genius: The Creative Power of Collaboration; Keith Sawyer; Basic Books, NY, 2007; 274 pp., $26.95. If you're involved in a business that depends on sustained innovation (and which doesn't?)-especially if you're a manager who can influence decisions and resources-then add this to your reading list. Keith Sawyer's writing is direct, clear, even friendly, and the text is unencumbered by ponderous footnotes and thick quotations. An associate professor of psychology at Washington University, St. Louis, Sawyer's personal hobbies of jazz and improv theater appear again and again to support his thesis that innovation is a product of groups, and that the solitary genius is a myth. But the scope of the book is more than that: it covers the important and current thinking about fostering innovation in the Internet-enabled age. By my own reading and experience in a research-driven Fortune 500 corporation, the innovation literature is converging on a number of "best practices." Sawyer captures them well, and with his well-chosen supporting anecdotes these are reason enough to read the book and get to work applying these lessons in your organization: * Poorly structured, lazy brainstorming is practiced so frequently and is so wasteful, we can't have enough books that reveal its flaws and how to improve upon it. Sawyer's treatment is excellent. * Sustained innovation depends on having many irons in the fire, with the corollary that failure will be frequent and must be supported. * Myths about lone geniuses need bursting. Even those who work alone stand on the shoulders of giants, and implementation always needs teamwork and has its own continuing need for creative problem solving. The risk of propagating the solitary genius concept (to the exclusion of other possibilities) is that organizations will fail to support "messy" teams. * Innovation happens at the "edge of chaos"; either too much or too little structure is destructive. * Clusters are important. It may seem risky to have many firms in one location competing for a common pool of talent, but history shows that a crossfertilizing, dynamic environment is more innovative and sustainable. * The Internet and related communications standards are flattening the world at an unprecedented pace; the Web empowers enormous networks of individual innovators. Standing still is not an option. Group Genius also benefits from: * Annotated Notes: Sawyer has made a wise choice to keep the prose simple and fast-moving, but as a result, major ideas sometimes jump off the page as bald assertions (for example, ". . . the most effective . . . groups are self-managing. . . . without being directed by a leader."). The annotated notes at the end of the book provide good counterweight and credibility to the breezy style. * Frequent Checklists: The book is sprinkled with bulleted lists of do's and don'ts, which stitch the storytelling narrative together into practical advice. …

Group Genius: The Creative Power of Collaboration

http://www.ickn.org/documents/COINs2010_Twitter4.pdf

Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

This paper describes early work trying to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts. We collected Twitter feeds for 5 months obtaining a large set of emotional retweets originating from within the US, from which six public opinion time series containing the keywords “dollar% t ”, “$% t ”, “gold% t ”, “oil% t , “job% t ” and “economy% t ” were extracted. Our results show that these variables are correlated to and even predictive of the financial market movement. Except “$% t ”, all other five public opinion time series are identified by a Granger-causal relationship with certain market movements. It is demonstrated that daily changes in the volume of economic topic retweeting seem to match the value shift occurring in the corresponding market next day.

/pdf/predicting-asset-value-through-twitter-buzz-2cj1afmrz1.pdf

Predicting Asset Value through Twitter Buzz

In this project we compare communication structure and content exchanged by members of creative, interdisciplinary teams of medical researchers, physicians, patients and caretakers with their creative output. We find that longitudinal social networking patterns and word usage predict creative performance. We collected the e-mail archives of 60 members of a community of researchers working on 12 projects improving various aspects of the daily lives of patients with Crohn’s disease. Our results indicate that more creative projects show a decrease in group density, while more actors are involved, and more e-mails are exchanged, suggesting that a more successful project attracts more attention from many different people. We also found that members of more creative projects use more outspoken language, which gets more focused over time.

/pdf/measuring-creative-performance-of-teams-through-dynamic-2n9x572f7r.pdf

Measuring creative performance of teams through dynamic semantic social network analysis

Detecting early-warning signals for social emergencies by temporal network sociomarkers

Science and technology capability refers to the comprehensive capability of all factors that affect the development of science and technology, mainly referring to human and material factors related to science and technology, among which human resources are the foundation and driving force. Therefore, researchers become a unique research perspective for the evaluation of national science and technology capabilities. Taking the integrated circuit field as the analysis case, this article proposed a researchers' transfer network model based on the online open source literature database. From the published literature information, the model obtains the researchers' transfer network that has a core-periphery structure. The core nodes are the European Union, the United States, China, etc., and these nodes are the most closely connected. A country/region role evolution model is also proposed, which reveals the characteristics of the role evolution of the European Union, the United States, China, and other countries from the perspective of researchers' transfer, especially their transfer between countries.

Xue Zhang

Papers

Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

Predicting Asset Value through Twitter Buzz

Measuring creative performance of teams through dynamic semantic social network analysis

Detecting early-warning signals for social emergencies by temporal network sociomarkers

Researchers' transfer network reveals the evolution of national science and technology capabilities.