Nowcasting the Bitcoin Market with Twitter Signals

Size: px
Start display at page:

Download "Nowcasting the Bitcoin Market with Twitter Signals"

Transcription

1 1 Nowcasting the Bitcoin Market with Twitter Signals JERMAIN KAMINSKI, MIT Media Lab & Witten/Herdecke University 1 PETER A. GLOOR, MIT Center for Collective Intelligence 1. INTRODUCTION Bitcoin is a peer-to-peer electronic cash system [Nakamoto 2009] and a leading global open-source cryptocurrency [Kroll et al. 2013]. It uses cryptography to control the creation and transfer of money, while transactions are broadcasted as digitally signed messages to the shared public network, the block chain. Bitcoins can be obtained by mining 2 or in exchange for products, services, or other currencies. According to The Wall Street Journal [2013], the commercial use of bitcoin still seems comparable small, mainly as a result of high and risky price volatilities. However, there are signals of traction in retail business and elsewhere, where bitcoins are increasingly accepted in transactions. While the value of all bitcoin is currently totaling about $12.5 billion, 3 and steadily increasing, news coverage in the recent months pointed towards the legitimate use of the virtual currency, its taxation and the circumstance it might support the trade of illicit goods and services [The Economist 2013]. Noteworthy, a recent breakdown of the mayor exchange platform Mt.Gox on February 25 th 2013 marked a setback for Bitcoin traders and observers. While behavioral economics suggest that emotions can affect individual behavior and decision-making [Akerlof and Shiller 2009; Scott and Loewenstein 2010], the microblogging platform Twitter has drawn more and more attention from different disciplines as a laboratory to study large sets of social and economic data. Particularly interesting is the influence of Twitter users and information propagation [Ye and Wu 2010]. For example, Antweiler and Frank [2004] determine the correlation between activity in Internet message boards and stock volatility and trading volume, while similar methods on analyzing web communications are applied by Choudhury et al. [2008], Gloor et al. [2009] and Gilbert and Karahalios [2010]. Zhang et al. [2011], Oh and Sheng [2011], Bollen et al. [2011], Jaimes et al. [2012], Sprenger et al. [2013] and Si et al. [2013] conducted work on analyzing Twitter (or microblogging) data in correlation with financial time series. For instance, the authors Bollen et al. [2011] claim an accuracy of 87.6 % in predicting the daily up and down changes in the closing values of the Dow Jones Industrial Average (DJIA) by using a Google-Profile of Mood States (GPOMS), covering the emotional dimensions of Calm, Alert, Sure, Vital, Kind and Happy. 1 Corresponding author: 2 For an explanation of the term mining see New York Times [2013] 3 March 31st, 2014.

2 1:2 J. Kaminski & Peter A. Gloor This paper is an early approach to apply the presently known practice of Twitter sentiment analysis for the case of Bitcoin and may be among the first approaches in this field. Analyzing the Bitcoin market seems particularly interesting, as it is a global and decentralized 24-hour trading market, with tweets and other information signals that are simultaneously provided, in real-time. 2. METHODOLOGY By using an automated online collector with access to the Twitter API, 4 a total of 161,200 tweets has been collected within the timeframe from November 23 rd 2013 until March 7 th Queries have been updated every hour on a daily basis. According to the collector s database, the collected tweets created in sum about 300 million impressions, where impressions are the total number of times that the tweets have been delivered to Twitter streams of users. So far, it seems reasonable that even such a small proportion of captured tweets might entail multiplier effects. In order to fetch the relevant emotional tweets, following queries have been used: bitcoin AND feel OR happy OR great OR love OR awesome OR lucky OR good OR sad OR bad OR upset OR unhappy OR nervous -bot and bitcoin AND hope OR fear OR worry. By doing so, tweets that contain the word bitcoin and one of the other terms (or more) could be archived. The biggest challenge for analyzing Twitter data and preventing statistical bias is undistorded data. Therefore, some data cleaning has been applied in the next step and (for example) the combinations happy birthday and not bad have not been counted for happy or bad respectively, just as repetitive bot-created content was filtered out as far as possible. As a result, four blocks of information can be pooled: (1) sum positive tweets ( 131,117): Sum of tweets on Bitcoin containing positive signals (feel, happy, great, love, awesome, lucky, good). (2) sum negative tweets ( 19,179): Sum of tweets on Bitcoin containing negative signals (sad, bad, upset, unhappy, nervous). (3) sum emotions ( 150,296): Sum of positive and negative tweets. (4) sum hopefearworry ( 10,222): Sum of tweets on Bitcoin containing signals of uncertainty (hope, fear or worry), cf. Zhang et al. [2011]. Further, market data from the four leading Bitcoin indices was fetched, namely BitStamp, Bitfinex, BTC-e and BTC China. 6 For each index, following price data has been considered: Open, Close, High (intraday), Low (intraday), Volume (BTC), Volume (currency), Intraday Spread (High-Low), overall Intraday Return (Open-Close) and δ Price (Close Price day+2 - Close Price day0 ). In the following, Figure 1, 2 and 3 visualize the data collection results: 4 5 N=104 6 Together, BitStamp (34%), Bitfinex (26%), BTC-e (16%) and BTC China (10%) account for about 86% of the overall Bitcoin market volume. Cf.

3 Nowcasting the Bitcoin Market with Twitter Signals 1: Volume (Currency / $) BTC-e BitFinex BitStamp Fig. 1 Bitcoin markets and their Volume (by currency) Volume (BTC) BitStamp Price Volume (BTC) Intraday Spread Fig. 2 BitStamp trading volume and price indicators. Squares signal the Close price, while bars (green) reflect the Intraday Spread (High-Low).

4 1:4 J. Kaminski & Peter A. Gloor Volume (BTC) Tweets, BTC-e indicators sum_positive_tweets/4 sum_negative_tweets/2 sum_hopefearworry/2 bitstamp_closeprice bitstamp_intradayreturn Fig. 3 Tweets and BitStamp price indicators. Remark: sum positive tweets, sum negative tweets and sum hoepfearworry have been adjusted (/x) to fit the dimensions of the graph. Prominent peaks on day can be explained by emotions on Mt.Gox breakdown (February 25 th 2014). 3. RESULTS Table I. Correlation of Twitter sentiments and Bitcoin markets bitstamp closeprice bitfinex closeprice btce closeprice btcn closeprice sum positive tweets sum negative tweets -.262** -.261** -.278**.230** sum emotions sum hopefearworry -.275** -.249** -.271** -.259** sum ratio positive to negative.227*.228*.282**.170 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) We can see that the biggest bitcoin exchange platforms (by trading volume) seem to be most sensitive for negative tweets and signals of uncertainty (sum hopefearworry) on Bitcoin. A day with a low amount of negative tweets will most likely end with a higher close price.

5 Nowcasting the Bitcoin Market with Twitter Signals 1:5 It can complementary be noted that a higher ratio of positive to negative tweets (=more positive than negative tweets) is accompanied by a higher close price. 7 To the contrary, positive tweets and the sum of emotions do not seem to influence the close price of BitStamp significantly. The previously introduced sentiment signals will sequentially be tested with indicators for BitStamp, the currently biggest exchange market: Table II. Correlation of Twitter sentiments and BitStamp market indicators bitst closeprice bitst volumebtc bitst intraday spread bitst intraday return sum positive tweets ** sum negative tweets -.262**.566**.286** sum emotions ** sum hopefearworry -.257**.459**.194* ratio positive to negative.227* -.426** -.339**.124 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Following results can be taken from Table II: (1) Emotions, especially negative tweets, positively correlate with the BitStamp trading volume. Furthermore, signals of uncertainty (hope, fear, worry) seem to support a vivid market with high trading volumes. (2) Emotions and signals of uncertainty also fuel intraday price volatilities, such as the intraday spread, reflecting the difference between the highest and lowest intraday trading price of a Bit- Stamp bitcoin. (3) The lower the ratio of positive to negative tweets, the higher the trading volume. This implies a balanced ratio (i.e. equilibrium) of positive and negative tweets significantly fuels trading. (4) The more negative emotions and signals of uncertainty appear on a given trading day, the more likely is a lower close price. Mixed with signals of uncertainty, negative sentiments may be a sign of dissatisfaction or pessimism by traders (and their observers). The current data suggests that the term of Nowcasting (predicting the present) [Choi and Varian 2012] might be applicable to Bitcoin intraday market development. In order to find out more about the predictive value of Twitter sentiments for the development of the BitStamp market, another calculation will be conducted. For this purpose, we will project the BitStamp close price each -2 and +2 days into the past and future respectively, while applying a moving average for related sentiment data. 7 It may be noted that the number of positive tweets is much higher than that of negative ones, more than 10 times higher on average. So far, the assumption by [Zhang et al. 2011] that people prefer optimistic to pessimistic words can be confirmed.

6 1:6 J. Kaminski & Peter A. Gloor Table III. Correlation of Twitter sentiments and BitStamp close price (± 2 days) -2d bitst close -1d bitst close bitst close +1d bitst close +2d bitst close sum positive tweets * sum negative tweets -.208* -.269** -.318** -.341** -.338** sum emotions * -.248* sum hopefearworry -.262** -.298** -.274** -.256** -.269** ratio positive to negative.199*.229*.263**.281**.280** ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Table III shows that negative tweets are negatively correlated with the BitStamp close price within a 48-hour timespan. Given the increasing significance with the passage of time, a lagging negative effect for negative tweets on the BitStamp price might be observed. The sum of emotions and positive tweets both show a negative correlation with regard to the future close price. Further data suggests that signals of uncertainty (hope, fear, worry) do not only amplify trading volumes (cf. Table IV) but also the close price: The higher the amount of uncertainty signals, the lower the BitStamp close price within ± 2days. As far as other signals indicate, an equilibrium in the amount of positive and negative signals positively correlates with a higher close price within the next two days. Table IV. Correlation of Twitter sentiments and BitStamp trading volume, price δ (± 2 days) -2d volume -1d volume +1d volume +2d volume -2d δ price +2d δ price sum positive tweets ** sum negative tweets.237*.505** sum emotions ** sum hopefearworry.362**.546** ratio positive to negative -.366** -.447** -.238* ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Table IV demonstrates the correlation between the BitStamp (intraday) trading volume as well as the intraday price spread (High - Low) δ within a ±48-hour timespan. As far as data enables an interpretation, a high amount of emotions and signals of uncertainty (in the present) correlates with high trading volumes within the last 24 hours. Especially, a high amount of negative signals is a key influencer for trading volume within the past 24 hours. Again, a balanced ratio of positive and negative sentiments also contributes to a higher trading volume, while for intraday price spreads (δ), the current data does not support any significant influence by Twitter sentiments.

7 Nowcasting the Bitcoin Market with Twitter Signals 1:7 Now, following results can be drawn from this paper: (1) Intraday measurements of Twitter sentiments may contribute to determine the movement of Bitcoin close price and volume. While the predictive validity of this work is very limited, it may at least confirm the possibility of Nowcasting. (2) Static intraday data suggests that an equilibrium of positive and negative tweets correlates with a higher trading volume, while a higher amount of positive tweets leads to a higher close-price on average. (3) In terms of time series and lagging effects, especially negative sentiments positively correlate with the intraday trading volume within the last 48 hours. Also, signals of uncertainty positively correlate with the trading volume within the last 48 hours. This observation can be translated as follows: When the trading volume was (and is) high, emotions fly high on Twitter. The correlation of past trading volumes with current emotions may already suggest a yet unproven causality. (4) The positive correlation of a balanced ratio in emotional sentiments and the trading volume could designate Twitter as place that reflects the speculative momentum within the market. (5) Summarizing, the microblogging platform Twitter may be interpreted as a virtual trading floor that heats up with virtual emotions and reflects market movements. 4. OUTLOOK The underlying question of this early research is how far real world emotions influence real financial markets, like Bitcoin. What can be told from an analysis of the BitStamp market is that sentiments on Twitter could be a valuable source of information on a Bitcoin trading desk. While the current data of only 104 days already looks promising, a longitudinal analysis of about 6 months or more might provide a better quality of scientific expressiveness and point towards possible predictors, especially in view of the fact that we currently observe a very volatile market. Particularly, events such as the breakdown of Mt.Gox can be considered as (both internal and external) market shocks that essentially influence the considered data. Most important, the question of causality has to be considered, as it seems still a little unclear, if emotional tweets rather follow a vivid market (as suggested) or if they rather co-create it. Technically, correlations in this analysis follow the simple and assumption that the relation between variables is linear, which is hardly satisfied in the often appearing random walk of financial market movements. Here, a multivariate Granger (1969) causality test might contribute towards a model that may better identify the cause and effect relationship of Twitter sentiments and Bitcoin market indicators. Equal attention should be paid to data and sentiment quality, which is very limited in our current methodology. A better linguistic model (and equally broader queries) might contribute to more valuable information. Therefore, we cordially invite fellow researchers to keep a close eye on Twitter and the Bitcoin market and to improve the outlined approach.

8 1:8 J. Kaminski & Peter A. Gloor REFERENCES George A. Akerlof and Robert J. Shiller Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism. Princeton University Press. Werner Antweiler and Murray Z. Frank Is all that talk just noise? The information content of Internet stock message boards. Journal of Finance 59, 3 (Juna 2004), Hohan Bollen, Huina Mao, and Xiao-Jun Zeng Twitter mood predicts the stock market. Journal of Computational Science 2, 1 (March 2011), 1 8. Hyunyoung Choi and Hal Varian Predicting the Present with Google Trends. Economic Record 88 (June 2012), 2 9. Munmun De Choudhury, Hari Sundaram, Ajita John, and Dorée D. Seligmann Can blog communication dynamics be correlated with stock market activity?. In In HT 08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia (2008) Eric Gilbert and Karrie Karahalios Widespread worry and the stock market. In Proceedings of the International Conference on Weblogs and Social. Peter A. Gloor, Jonas S. Krauss, Stefan Nann, Kai Fischbach, and Detlef Schoder Web Science 2.0: Identifying Trends Through Semantic Social Network Analysis. In International Conference on Computational Science and Engineering, Vol. 4. Alejandro Jaimes, Eduardo Ruiz, Vagelis Hristidis, Carlos Castillo, and Aristides Gionis Correlating Financial Time Series with Micro-Blogging Data. (2012). correlating-financial-time-series-with-micro-blogging-data/ Joshua A. Kroll, Ian C. Davey, and Edward W. Felten The Economics of Bitcoin Mining, The Economics of Bitcoin Mining, or Bitcoin in the Presence of Adversaries. (November 2013). Satoshi Nakamoto Bitcoin: A Peer-to-Peer Electronic Cash System. (2009). New York Times Into the Bitcoin Mines. (2013). Chong Oh and Olivia Sheng Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement. Proceedings of the International Conference on Information Systems, ICIS 2011 (January 2011). Rick Scott and George Loewenstein Handbook of Emotions. Guilford Press, Chapter The Role of Emotion in Economic Behaviour, Jianfeng Si, Arjun Mukherjee, Bing Liu, Qing Li, Huayi Li, and Xiaotie Deng Exploiting Topic based Twitter Sentiment for Stock Prediction. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL (2013), Timm O. Sprenger, Andranik Tumasjan, Philipp G. Sandner, and Isabell M. Welpe Tweets and trades: The information content of stock microblogs. European Financial Management (May 2013). The Economist Bitcoin under pressure. (November 2013) virtual-currency-it-mathematically-elegant-increasingly-popular-and-highly The Wall Street Journal Beware the Risks of the Bitcoin: Winklevii Outline the Downside. (July 2013). com/moneybeat/2013/07/02/beware-the-risks-of-the-bitcoin-winklevii-outline-the-downside/ Shaozhi Ye and Felix S. Wu Measuring Message Propagation and Social Influence on Twitter.com. In Proceedings of the Second international conference on Social informatics Xue Zhang, Hauke Fuehres, and Peter A. Gloor Predicting Stock Market Indicators Through Twitter I hope it is not as bad as I fear. Procedia - Social and Behavioral Sciences 26 (2011),

Nowcasting the Bitcoin Market with Twitter Signals 1

Nowcasting the Bitcoin Market with Twitter Signals 1 1 Nowcasting the Bitcoin Market with Twitter Signals 1 JERMAIN C. KAMINSKI, MIT Media Lab arxiv:1406.7577v2 [cs.si] 13 Aug 2015 1. ABSTRACT & KEYWORDS This paper analyzes correlations and causalities between

More information

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

Predicting Stock Market Indicators Through Twitter I hope it is not as bad as I fear Available online at www.sciencedirect.com Procedia Social and Behavioral Sciences Procedia - Social and Behavioral Sciences 00 (2009) 000 000 www.elsevier.com/locate/procedia COINs2010 Predicting Stock

More information

Twitter Mood Predicts the Stock Market. Xiao-Jun Zeng School of Computer Science University of Manchester x.zeng@manchester.ac.uk

Twitter Mood Predicts the Stock Market. Xiao-Jun Zeng School of Computer Science University of Manchester x.zeng@manchester.ac.uk Twitter Mood Predicts the Stock Market Xiao-Jun Zeng School of Computer Science University of Manchester x.zeng@manchester.ac.uk Outline Introduction and Motivation Approach Framework Twitter mood model

More information

IMPACT OF SOCIAL MEDIA ON THE STOCK MARKET: EVIDENCE FROM TWEETS

IMPACT OF SOCIAL MEDIA ON THE STOCK MARKET: EVIDENCE FROM TWEETS IMPACT OF SOCIAL MEDIA ON THE STOCK MARKET: EVIDENCE FROM TWEETS Vojtěch Fiala 1, Svatopluk Kapounek 1, Ondřej Veselý 1 1 Mendel University in Brno Volume 1 Issue 1 ISSN 2336-6494 www.ejobsat.com ABSTRACT

More information

QUANTIFYING THE EFFECTS OF ONLINE BULLISHNESS ON INTERNATIONAL FINANCIAL MARKETS

QUANTIFYING THE EFFECTS OF ONLINE BULLISHNESS ON INTERNATIONAL FINANCIAL MARKETS QUANTIFYING THE EFFECTS OF ONLINE BULLISHNESS ON INTERNATIONAL FINANCIAL MARKETS Huina Mao School of Informatics and Computing Indiana University, Bloomington, USA ECB Workshop on Using Big Data for Forecasting

More information

Predicting Asset Value Through Twitter Buzz

Predicting Asset Value Through Twitter Buzz Predicting Asset Value Through Twitter Buzz Xue Zhang a,b, Hauke Fuehres b, Peter A. Gloor b a Department of Mathematic and Systems Science, National University of Defense Technology, Changsha, Hunan,

More information

Sentiment Analysis. D. Skrepetos 1. University of Waterloo. NLP Presenation, 06/17/2015

Sentiment Analysis. D. Skrepetos 1. University of Waterloo. NLP Presenation, 06/17/2015 Sentiment Analysis D. Skrepetos 1 1 Department of Computer Science University of Waterloo NLP Presenation, 06/17/2015 D. Skrepetos (University of Waterloo) Sentiment Analysis NLP Presenation, 06/17/2015

More information

CSE 598 Project Report: Comparison of Sentiment Aggregation Techniques

CSE 598 Project Report: Comparison of Sentiment Aggregation Techniques CSE 598 Project Report: Comparison of Sentiment Aggregation Techniques Chris MacLellan cjmaclel@asu.edu May 3, 2012 Abstract Different methods for aggregating twitter sentiment data are proposed and three

More information

Can Twitter provide enough information for predicting the stock market?

Can Twitter provide enough information for predicting the stock market? Can Twitter provide enough information for predicting the stock market? Maria Dolores Priego Porcuna Introduction Nowadays a huge percentage of financial companies are investing a lot of money on Social

More information

Sentiment analysis on tweets in a financial domain

Sentiment analysis on tweets in a financial domain Sentiment analysis on tweets in a financial domain Jasmina Smailović 1,2, Miha Grčar 1, Martin Žnidaršič 1 1 Dept of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia 2 Jožef Stefan International

More information

Do Tweets Matter for Shareholders? An Empirical Analysis

Do Tweets Matter for Shareholders? An Empirical Analysis Do Tweets Matter for Shareholders? An Empirical Analysis Brittany Cole University of Mississippi Jonathan Daigle University of Mississippi Bonnie F. Van Ness University of Mississippi We identify the 215

More information

The Influence of Sentimental Analysis on Corporate Event Study

The Influence of Sentimental Analysis on Corporate Event Study Volume-4, Issue-4, August-2014, ISSN No.: 2250-0758 International Journal of Engineering and Management Research Available at: www.ijemr.net Page Number: 10-16 The Influence of Sentimental Analysis on

More information

A Description of Consumer Activity in Twitter

A Description of Consumer Activity in Twitter Justin Stewart A Description of Consumer Activity in Twitter At least for the astute economist, the introduction of techniques from computational science into economics has and is continuing to change

More information

The Viability of StockTwits and Google Trends to Predict the Stock Market. By Chris Loughlin and Erik Harnisch

The Viability of StockTwits and Google Trends to Predict the Stock Market. By Chris Loughlin and Erik Harnisch The Viability of StockTwits and Google Trends to Predict the Stock Market By Chris Loughlin and Erik Harnisch Spring 2013 Introduction Investors are always looking to gain an edge on the rest of the market.

More information

The process of gathering and analyzing Twitter data to predict stock returns EC115. Economics

The process of gathering and analyzing Twitter data to predict stock returns EC115. Economics The process of gathering and analyzing Twitter data to predict stock returns EC115 Economics Purpose Many Americans save for retirement through plans such as 401k s and IRA s and these retirement plans

More information

Please note trading advice and risk statement on pages three and four

Please note trading advice and risk statement on pages three and four FTSE Buzz FTSE Sentiment 6,75 7,25 1 7, 5 6,5 1 % 6,75 6,5-5 6,25 6,25-1 12 12 Euro/US Dollar Buzz Euro/US Dollar Sentiment 1.36 1.36 5 1.34 1 % 1.34 1.32 1.32-5 26 26 Realtime data and updates on s tockpuls

More information

It has often been said that stock

It has often been said that stock Twitter Mood as a Stock Market Predictor Johan Bollen and Huina Mao Indiana University Bloomington Behavioral finance researchers can apply computational methods to large-scale social media data to better

More information

Big Data and High Quality Sentiment Analysis for Stock Trading and Business Intelligence. Dr. Sulkhan Metreveli Leo Keller

Big Data and High Quality Sentiment Analysis for Stock Trading and Business Intelligence. Dr. Sulkhan Metreveli Leo Keller Big Data and High Quality Sentiment Analysis for Stock Trading and Business Intelligence Dr. Sulkhan Metreveli Leo Keller The greed https://www.youtube.com/watch?v=r8y6djaeolo The money https://www.youtube.com/watch?v=x_6oogojnaw

More information

with news articles and Internet search data

with news articles and Internet search data lakyara Nowcasting the real estate market with news articles and Internet search data Tomohiko Taniyama 10. June. 2013 Executive Summary Tomohiko Taniyama Senior Researcher Infrastructure & Asset Finance

More information

Using Twitter to Predict Investor Decisions. Honors Thesis

Using Twitter to Predict Investor Decisions. Honors Thesis Using Twitter to Predict Investor Decisions Honors Thesis Presented in Partial Fulfillment of the Requirements for the Bachelor of Science in Business Administration Degree with Honors Research Distinction

More information

Twitter Volume Spikes: Analysis and Application in Stock Trading

Twitter Volume Spikes: Analysis and Application in Stock Trading Twitter Volume Spikes: Analysis and Application in Stock Trading Yuexin Mao University of Connecticut yuexin.mao@uconn.edu Wei Wei FinStats.com weiwei@finstats.com Bing Wang University of Connecticut bing@engr.uconn.edu

More information

Differentiation of AmalgaMood through Longer Holding Periods

Differentiation of AmalgaMood through Longer Holding Periods Differentiation of AmalgaMood through Longer Holding Periods Analysis: Contact: Comparison of AmalgaMood with other methods info@amalgamood.com AmalgaMood s Mood Index has performed very well highlighting

More information

CS 229, Autumn 2011 Modeling the Stock Market Using Twitter Sentiment Analysis

CS 229, Autumn 2011 Modeling the Stock Market Using Twitter Sentiment Analysis CS 229, Autumn 2011 Modeling the Stock Market Using Twitter Sentiment Analysis Team members: Daniel Debbini, Philippe Estin, Maxime Goutagny Supervisor: Mihai Surdeanu (with John Bauer) 1 Introduction

More information

Italian Journal of Accounting and Economia Aziendale. International Area. Year CXIV - 2014 - n. 1, 2 e 3

Italian Journal of Accounting and Economia Aziendale. International Area. Year CXIV - 2014 - n. 1, 2 e 3 Italian Journal of Accounting and Economia Aziendale International Area Year CXIV - 2014 - n. 1, 2 e 3 Could we make better prediction of stock market indicators through Twitter sentiment analysis? ALEXANDER

More information

Exploiting Topic based Twitter Sentiment for Stock Prediction

Exploiting Topic based Twitter Sentiment for Stock Prediction Exploiting Topic based Twitter Sentiment for Stock Prediction Jianfeng Si * Arjun Mukherjee Bing Liu Qing Li * Huayi Li Xiaotie Deng * Department of Computer Science, City University of Hong Kong, Hong

More information

Predicting Stock Market Fluctuations. from Twitter

Predicting Stock Market Fluctuations. from Twitter Predicting Stock Market Fluctuations from Twitter An analysis of the predictive powers of real-time social media Sang Chung & Sandy Liu Stat 157 Professor ALdous Dec 12, 2011 Chung & Liu 2 1. Introduction

More information

** Please note trading advices and risk statement on pages three and four!

** Please note trading advices and risk statement on pages three and four! Good morning Today's Trend Signals** Signal Type Stock Signal Prediction EON bearish 0,69% ISIN: DE000ENAG999 Index: DAX Hit Ratio: 64% More signals and updates on stockpulse.de Today's Buzz Warnings**

More information

Tweets Miner for Stock Market Analysis

Tweets Miner for Stock Market Analysis Tweets Miner for Stock Market Analysis Bohdan Pavlyshenko Electronics department, Ivan Franko Lviv National University,Ukraine, Drahomanov Str. 50, Lviv, 79005, Ukraine, e-mail: b.pavlyshenko@gmail.com

More information

On the Predictability of Stock Market Behavior using StockTwits Sentiment and Posting Volume

On the Predictability of Stock Market Behavior using StockTwits Sentiment and Posting Volume On the Predictability of Stock Market Behavior using StockTwits Sentiment and Posting Volume Abstract. In this study, we explored data from StockTwits, a microblogging platform exclusively dedicated to

More information

The impact of social media is pervasive. It has

The impact of social media is pervasive. It has Infosys Labs Briefings VOL 12 NO 1 2014 Social Enablement of Online Trading Platforms By Sivaram V. Thangam, Swaminathan Natarajan and Venugopal Subbarao Socially connected retail stock traders make better

More information

A Survey on Predictive Analytics Integrated with and Social Media

A Survey on Predictive Analytics Integrated with and Social Media A Survey on Predictive Analytics Integrated with and Social Media Madhusudhan V 1, Shilpa N R 2 1M.tech, Computer Science and Engineering, REVA ITM, Bangalore, India 2 Asst.professor, REVA UNIVERSITY,

More information

Analysis of Tweets for Prediction of Indian Stock Markets

Analysis of Tweets for Prediction of Indian Stock Markets Analysis of Tweets for Prediction of Indian Stock Markets Phillip Tichaona Sumbureru Department of Computer Science and Engineering, JNTU College of Engineering Hyderabad, Kukatpally, Hyderabad-500 085,

More information

Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement

Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement Ray Chen, Marius Lazer Abstract In this paper, we investigate the relationship between Twitter feed content and stock market

More information

Predicting Elections with Twitter What 140 Characters Reveal about Political Sentiment

Predicting Elections with Twitter What 140 Characters Reveal about Political Sentiment Predicting Elections with Twitter What 140 Characters Reveal about Political Sentiment Andranik Tumasjan, Timm O. Sprenger, Philipp G. Sandner, Isabell M. Welpe Workshop Election Forecasting 15 July 2013

More information

A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements

A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements Review of Economic Analysis 6 (2014) 132-140 1973-3909/2014132 A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements SERDA SELIN OZTURK Istanbul Bilgi University KURSAD CIFTCI

More information

Fuzzy logic decision support for long-term investing in the financial market

Fuzzy logic decision support for long-term investing in the financial market Fuzzy logic decision support for long-term investing in the financial market Abstract This paper discusses the use of fuzzy logic and modeling as a decision making support for long-term investment decisions

More information

European Parliament elections on Twitter

European Parliament elections on Twitter Analysis of Twitter feeds 6 June 2014 Outline The goal of our project is to investigate any possible relations between public support towards two Polish major political parties - Platforma Obywatelska

More information

The Information of Spam

The Information of Spam University of Arkansas, Fayetteville ScholarWorks@UARK Computer Science and Computer Engineering Undergraduate Honors Theses Computer Science and Computer Engineering 12-2015 The Information of Spam Sawyer

More information

Improve Human Settlement Mapping with Big Data

Improve Human Settlement Mapping with Big Data Improve Human Settlement Mapping with Big Data Huina Mao 1,2 1 School of Informatics and Computing, Indiana University Bloomington 2 Oak Ridge National Laboratory 1 Introduction Every facet of our daily

More information

Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams

Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams 2012 International Conference on Computer Technology and Science (ICCTS 2012) IPCSIT vol. XX (2012) (2012) IACSIT Press, Singapore Using Text and Data Mining Techniques to extract Stock Market Sentiment

More information

A GENERAL TAXONOMY FOR VISUALIZATION OF PREDICTIVE SOCIAL MEDIA ANALYTICS

A GENERAL TAXONOMY FOR VISUALIZATION OF PREDICTIVE SOCIAL MEDIA ANALYTICS A GENERAL TAXONOMY FOR VISUALIZATION OF PREDICTIVE SOCIAL MEDIA ANALYTICS Stacey Franklin Jones, D.Sc. ProTech Global Solutions Annapolis, MD Abstract The use of Social Media as a resource to characterize

More information

EXPLOITING TWITTER IN MARKET RESEARCH FOR UNIVERSITY DEGREE COURSES

EXPLOITING TWITTER IN MARKET RESEARCH FOR UNIVERSITY DEGREE COURSES EXPLOITING TWITTER IN MARKET RESEARCH FOR UNIVERSITY DEGREE COURSES Zhenar Shaho Faeq 1,Kayhan Ghafoor 2, Bawar Abdalla 3 and Omar Al-rassam 4 1 Department of Software Engineering, Koya University, Koya,

More information

traders jlbollen #twitter mood predicts the #DJIA FTW!

traders jlbollen #twitter mood predicts the #DJIA FTW! Introduction Methods Results Conclusions traders jlbollen #twitter mood predicts the #DJIA FTW! jbollen@indiana.edu, huinmao@indiana.edu School of Informatics and Computing Center for Complex Networks

More information

Using Tweets to Predict the Stock Market

Using Tweets to Predict the Stock Market 1. Abstract Using Tweets to Predict the Stock Market Zhiang Hu, Jian Jiao, Jialu Zhu In this project we would like to find the relationship between tweets of one important Twitter user and the corresponding

More information

Twitter mood predicts the stock market.

Twitter mood predicts the stock market. Twitter mood predicts the stock market. Johan Bollen,,Huina Mao,,Xiao-Jun Zeng. : authors made equal contributions. arxiv:00.3003v [cs.ce] 4 Oct 00 Abstract Behavioral economics tells us that emotions

More information

WILL TWITTER MAKE YOU A BETTER INVESTOR? A LOOK AT SENTIMENT, USER REPUTATION AND THEIR EFFECT ON THE STOCK MARKET

WILL TWITTER MAKE YOU A BETTER INVESTOR? A LOOK AT SENTIMENT, USER REPUTATION AND THEIR EFFECT ON THE STOCK MARKET WILL TWITTER MAKE YOU A BETTER INVESTOR? A LOOK AT SENTIMENT, USER REPUTATION AND THEIR EFFECT ON THE STOCK MARKET ABSTRACT Eric D. Brown Dakota State University edbrown@dsu.edu The use of social networks

More information

Twitter Volume and First Day IPO Performance. Tammy Kwan

Twitter Volume and First Day IPO Performance. Tammy Kwan Twitter Volume and First Day IPO Performance Tammy Kwan The Leonard N. Stern School of Business Glucksman Institute for Research in Securities Faculty Advisor: Jeffrey Wurgler April 1, 2015 I. Introduction

More information

Quantifying the Effects of Online Bullishness on International Financial Markets

Quantifying the Effects of Online Bullishness on International Financial Markets Quantifying the Effects of Online Bullishness on International Financial Markets Huina Mao 1, Scott Counts 2, and Johan Bollen 1 1 School of Informatics and Computing, Indiana University, Bloomington,

More information

ICC 103-7. 17 September 2009 Original: French. Study. International Coffee Council 103 rd Session 23 25 September 2009 London, England

ICC 103-7. 17 September 2009 Original: French. Study. International Coffee Council 103 rd Session 23 25 September 2009 London, England ICC 103-7 17 September 2009 Original: French Study E International Coffee Council 103 rd Session 23 25 September 2009 London, England Coffee price volatility Background In the context of its programme

More information

Big Data, Socio- Psychological Theory, Algorithmic Text Analysis, and Predicting the Michigan Consumer Sentiment Index

Big Data, Socio- Psychological Theory, Algorithmic Text Analysis, and Predicting the Michigan Consumer Sentiment Index Big Data, Socio- Psychological Theory, Algorithmic Text Analysis, and Predicting the Michigan Consumer Sentiment Index Rickard Nyman *, Paul Ormerod Centre for the Study of Decision Making Under Uncertainty,

More information

TWITTER AND FINANCIAL MARKETS

TWITTER AND FINANCIAL MARKETS The 215 WEI International Academic Conference Proceedings TWITTER AND FINANCIAL MARKETS Muktamala Chakrabarti, Asim Kumar Pal, Ashok Banerjee Indian Institute of Management Calcutta D.H. Road, Joka, Kolkata,

More information

SOCIAL MEDIA AND MARKETS: The New Frontier

SOCIAL MEDIA AND MARKETS: The New Frontier WHITEPAPER Gnip, Inc. www.gnip.com 888.777.7405 trading@gnip.com @gnip SOCIAL MEDIA AND MARKETS: The New Frontier Source: Gnip, Inc. For the first time in history, access to the observations, wisdom and

More information

GUIDE CAMPAIGN MANAGEMENT BOARDS. How to Use Boards for. How to Set up a Board

GUIDE CAMPAIGN MANAGEMENT BOARDS. How to Use Boards for. How to Set up a Board How to Use Boards for CAMPAIGN MANAGEMENT BOARDS GUIDE How to Set up a Board BOARDS are highly customized, interactive dashboards that ubervu via Hootsuite users can personalize to fit a specific task,

More information

Twitter mood predicts the stock market

Twitter mood predicts the stock market Introduction Methods Results Conclusions jbollen@indiana.edu, huinmao@indiana.edu School of Informatics and Computing Center for Complex Networks and Systems Research Indiana University April 9, 2011 Introduction

More information

Prediction of changes in the stock market using twitter and sentiment analysis

Prediction of changes in the stock market using twitter and sentiment analysis Prediction of changes in the stock market using twitter and sentiment analysis Iulian Vlad Serban, David Sierra González, and Xuyang Wu University College London Abstract Twitter is an online social networking

More information

BitIodine: extracting intelligence from the Bitcoin network

BitIodine: extracting intelligence from the Bitcoin network BitIodine: extracting intelligence from the Bitcoin network Michele Spagnuolo http://miki.it michele@spagnuolo.me @mikispag Bitcoin BitIodine About Bitcoin Decentralized, global digital currency A global

More information

Twitter sentiment vs. Stock price!

Twitter sentiment vs. Stock price! Twitter sentiment vs. Stock price! Background! On April 24 th 2013, the Twitter account belonging to Associated Press was hacked. Fake posts about the Whitehouse being bombed and the President being injured

More information

Quantitative Trading on News and Social Media Content

Quantitative Trading on News and Social Media Content 1 Princeton - UChicago Quant Trading Conference November 8, 2013 - Chicago Quantitative Trading on News and Social Media Content Presentation by: Armando Gonzalez President & CEO - RavenPack and thousands

More information

Automated news based

Automated news based Automated news based ULIP fund switching model ULIP funds switching model recommends the fund switching Parikh Satyen Professor & Head A.M. Patel Institute of Computer Sciences Ganpat University satyen.parikh@ganpatuniver

More information

the beginner s guide to SOCIAL MEDIA METRICS

the beginner s guide to SOCIAL MEDIA METRICS the beginner s guide to SOCIAL MEDIA METRICS INTRO Social media can be an incredibly important business tool. Tracking the right social metrics around your industry, company, products, competition and

More information

Can Twitter Predict Royal Baby's Name?

Can Twitter Predict Royal Baby's Name? Summary Can Twitter Predict Royal Baby's Name? Bohdan Pavlyshenko Ivan Franko Lviv National University,Ukraine, b.pavlyshenko@gmail.com In this paper, we analyze the existence of possible correlation between

More information

The Hollywood Stock Exchange: Efficiency and The Power of Twitter

The Hollywood Stock Exchange: Efficiency and The Power of Twitter The Hollywood Stock Exchange: Efficiency and The Power of Twitter by Nathaniel Harley A special thanks to Professor Richard Walker for advising on this thesis. Also, thanks to Professor Joseph Ferrie,

More information

Statistics Paper Series

Statistics Paper Series Statistics Paper Series Huina Mao, Scott Counts and Johan ollen Quantifying the effects of online bullishness on international financial markets No 9 / July 2015 Note: This Statistics Paper should not

More information

Life Cycle Asset Allocation A Suitable Approach for Defined Contribution Pension Plans

Life Cycle Asset Allocation A Suitable Approach for Defined Contribution Pension Plans Life Cycle Asset Allocation A Suitable Approach for Defined Contribution Pension Plans Challenges for defined contribution plans While Eastern Europe is a prominent example of the importance of defined

More information

Bitcoin: Assessing the Tax Implications Associated with the IRS s Notice Deeming Virtual Currencies Property

Bitcoin: Assessing the Tax Implications Associated with the IRS s Notice Deeming Virtual Currencies Property 2014-2015 DEVELOPMENTS IN BANKING LAW 451 VI. Bitcoin: Assessing the Tax Implications Associated with the IRS s Notice Deeming Virtual Currencies Property A. Introduction Recently, there has been considerable

More information

Using Twitter as a source of information for stock market prediction

Using Twitter as a source of information for stock market prediction Using Twitter as a source of information for stock market prediction Ramon Xuriguera (rxuriguera@lsi.upc.edu) Joint work with Marta Arias and Argimiro Arratia ERCIM 2011, 17-19 Dec. 2011, University of

More information

Social Media analysis: A very useful tool for trading and investing. Gerhard Lampen Head Sanlam itrade Online

Social Media analysis: A very useful tool for trading and investing. Gerhard Lampen Head Sanlam itrade Online Social Media analysis: A very useful tool for trading and investing Gerhard Lampen Head Sanlam itrade Online Agenda 1. A Growing Industry 2. Breaking News Discovery 3. Copy/Mirror Trading 4. Sentiment

More information

Forecasting stock markets with Twitter

Forecasting stock markets with Twitter Forecasting stock markets with Twitter Argimiro Arratia argimiro@lsi.upc.edu Joint work with Marta Arias and Ramón Xuriguera To appear in: ACM Transactions on Intelligent Systems and Technology, 2013,

More information

USING TWITTER TO PREDICT SALES: A CASE STUDY

USING TWITTER TO PREDICT SALES: A CASE STUDY USING TWITTER TO PREDICT SALES: A CASE STUDY Remco Dijkman 1, Panagiotis Ipeirotis 2, Freek Aertsen 3, Roy van Helden 4 1 Eindhoven University of Technology, Eindhoven, The Netherlands r.m.dijkman@tue.nl

More information

Stock Prediction Using Twitter Sentiment Analysis

Stock Prediction Using Twitter Sentiment Analysis Stock Prediction Using Twitter Sentiment Analysis Anshul Mittal Stanford University anmittal@stanford.edu Arpit Goel Stanford University argoel@stanford.edu ABSTRACT In this paper, we apply sentiment analysis

More information

Machine Learning and Algorithmic Trading

Machine Learning and Algorithmic Trading Machine Learning and Algorithmic Trading In Fixed Income Markets Algorithmic Trading, computerized trading controlled by algorithms, is natural evolution of security markets. This area has evolved both

More information

Dow Theory. The Key To Understanding Stock Market Movement

Dow Theory. The Key To Understanding Stock Market Movement Dow Theory The Key To Understanding Stock Market Movement Christopher M. Quigley B.Sc.(Maj. Accounting), M.I.I. Grad., M.A. The Dow theory has been around for almost 100 years. Developed by Charles Dow

More information

Initial Report. Predicting association football match outcomes using social media and existing knowledge.

Initial Report. Predicting association football match outcomes using social media and existing knowledge. Initial Report Predicting association football match outcomes using social media and existing knowledge. Student Number: C1148334 Author: Kiran Smith Supervisor: Dr. Steven Schockaert Module Title: One

More information

The Implication of Tweet s Distribution by Quantizing Stock Values for Inference in the Indian Financial Market: A Sentiment Analysis Approach

The Implication of Tweet s Distribution by Quantizing Stock Values for Inference in the Indian Financial Market: A Sentiment Analysis Approach International Journal of Computer Applications (975 8887) Volume 19 No. 8, January 215 The Implication of Tweet s Distribution by Quantizing Stock Values for Inference in the Indian Financial Market: A

More information

Whether you re new to trading or an experienced investor, listed stock

Whether you re new to trading or an experienced investor, listed stock Chapter 1 Options Trading and Investing In This Chapter Developing an appreciation for options Using option analysis with any market approach Focusing on limiting risk Capitalizing on advanced techniques

More information

Some Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter

Some Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter Some Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter Nuno Oliveira Centro Algoritmi Dep. Sistemas de Informação Universidade do Minho 4800-058

More information

Constructing Social Intentional Corpora to Predict Click-Through Rate for Search Advertising

Constructing Social Intentional Corpora to Predict Click-Through Rate for Search Advertising Constructing Social Intentional Corpora to Predict Click-Through Rate for Search Advertising Yi-Ting Chen, Hung-Yu Kao Department of Computer Science and Information Engineering National Cheng Kung University

More information

Section A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I

Section A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA - Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting

More information

Predicting the Stock Market with News Articles

Predicting the Stock Market with News Articles Predicting the Stock Market with News Articles Kari Lee and Ryan Timmons CS224N Final Project Introduction Stock market prediction is an area of extreme importance to an entire industry. Stock price is

More information

Stock Market Forecasting Using Machine Learning Algorithms

Stock Market Forecasting Using Machine Learning Algorithms Stock Market Forecasting Using Machine Learning Algorithms Shunrong Shen, Haomiao Jiang Department of Electrical Engineering Stanford University {conank,hjiang36}@stanford.edu Tongda Zhang Department of

More information

Exploiting Social Relations and Sentiment for Stock Prediction

Exploiting Social Relations and Sentiment for Stock Prediction Exploiting Social Relations and Sentiment for Stock Prediction Jianfeng Si * Arjun Mukherjee Bing Liu Sinno Jialin Pan * Qing Li Huayi Li * Institute for Infocomm Research, Singapore { thankjeff@gmail.com,

More information

Understanding pricing anomalies in prediction and betting markets with informed traders

Understanding pricing anomalies in prediction and betting markets with informed traders Understanding pricing anomalies in prediction and betting markets with informed traders Peter Norman Sørensen Økonomi, KU GetFIT, February 2012 Peter Norman Sørensen (Økonomi, KU) Prediction Markets GetFIT,

More information

Algorithmic Trading Session 1 Introduction. Oliver Steinki, CFA, FRM

Algorithmic Trading Session 1 Introduction. Oliver Steinki, CFA, FRM Algorithmic Trading Session 1 Introduction Oliver Steinki, CFA, FRM Outline An Introduction to Algorithmic Trading Definition, Research Areas, Relevance and Applications General Trading Overview Goals

More information

Finding profitable trading strategies in the bitcoin currency economy with time series spike detection

Finding profitable trading strategies in the bitcoin currency economy with time series spike detection Finding profitable trading strategies in the bitcoin currency economy with time series spike detection Hakim Khalafi Complex Systems Sciences École Polytechnique Palaiseau, 9112 France hakim.khalafi@polytechnique.edu

More information

Marketing Mix Modelling and Big Data P. M Cain

Marketing Mix Modelling and Big Data P. M Cain 1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored

More information

Text and data analytics for social network mining

Text and data analytics for social network mining Text and data analytics for social network mining Goran Nenadic John Keane, Xiao-Jun Zeng School of Computer Science G.Nenadic@manchester.ac.uk Manchester Institute of Biotechnology Topics Aim and tasks

More information

DELTA Dashboards Visualise, Analyse and Monitor kdb+ Datasets with Delta Dashboards

DELTA Dashboards Visualise, Analyse and Monitor kdb+ Datasets with Delta Dashboards Delta Dashboards is a powerful, real-time presentation layer for the market-leading kdb+ database technology. They provide rich visualisation of both real-time streaming data and highly optimised polled

More information

Forecasting and big data analysis

Forecasting and big data analysis Forecasting and big data analysis July 2014 Ron Levkovitz, Ogentech Ltd. Supply chain forecasting Accurate forecasting and demand planning is the basis of efficient supply chain management and execution.

More information

Financial Text Mining

Financial Text Mining Enabling Sophisticated Financial Text Mining Calum Robertson Research Analyst, Sirca Background Data Research Strategies Obstacles Conclusions Overview Background Efficient Market Hypothesis Asset Price

More information

How to Use Boards for Competitive Intelligence

How to Use Boards for Competitive Intelligence How to Use Boards for Competitive Intelligence Boards are highly customized, interactive dashboards that ubervu via Hootsuite users can personalize to fit a specific task, job function or use case like

More information

PREDICTIVE ANALYTICS ON PUBLIC DATA THE CASE OF STOCK MARKETS

PREDICTIVE ANALYTICS ON PUBLIC DATA THE CASE OF STOCK MARKETS PREDICTIVE ANALYTICS ON PUBLIC DATA THE CASE OF STOCK MARKETS Nann, Stefan, University of Cologne, Pohligstr. 1, 50969 Köln, Germany, nann@wim.uni-koeln.de Krauss, Jonas, University of Cologne, Pohligstr.

More information

The predictive characteristic of the social sentiment on the stock market: Twitter and the stock trend

The predictive characteristic of the social sentiment on the stock market: Twitter and the stock trend The predictive characteristic of the social sentiment on the stock market: Twitter and the stock trend Moreno Lo Giudice University of Twente P.O. Box 217, 7500AE Enschede The Netherlands ABSTRACT The

More information

Proceedings of the 9th WSEAS International Conference on APPLIED COMPUTER SCIENCE

Proceedings of the 9th WSEAS International Conference on APPLIED COMPUTER SCIENCE Automated Futures Trading Environment Effect on the Decision Making PETR TUCNIK Department of Information Technologies University of Hradec Kralove Rokitanskeho 62, 500 02 Hradec Kralove CZECH REPUBLIC

More information

Microblog Sentiment Analysis with Emoticon Space Model

Microblog Sentiment Analysis with Emoticon Space Model Microblog Sentiment Analysis with Emoticon Space Model Fei Jiang, Yiqun Liu, Huanbo Luan, Min Zhang, and Shaoping Ma State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory

More information

Linked Data-based Social Media Analysis for Stock Market Tracking

Linked Data-based Social Media Analysis for Stock Market Tracking Linked Data-based Social Media Analysis for Stock Market Tracking Priyanka Dank University of Bonn 53117 Bonn NRW, Germany priyankadank@gmail.com Simon Scerri Fraunhofer IAIS Schloss Birlinghoven Sankt

More information

MARKETS, INFORMATION AND THEIR FRACTAL ANALYSIS. Mária Bohdalová and Michal Greguš Comenius University, Faculty of Management Slovak republic

MARKETS, INFORMATION AND THEIR FRACTAL ANALYSIS. Mária Bohdalová and Michal Greguš Comenius University, Faculty of Management Slovak republic MARKETS, INFORMATION AND THEIR FRACTAL ANALYSIS Mária Bohdalová and Michal Greguš Comenius University, Faculty of Management Slovak republic Abstract: We will summarize the impact of the conflict between

More information

Journal of Chemical and Pharmaceutical Research, 2015, 7(3):1388-1392. Research Article. E-commerce recommendation system on cloud computing

Journal of Chemical and Pharmaceutical Research, 2015, 7(3):1388-1392. Research Article. E-commerce recommendation system on cloud computing Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2015, 7(3):1388-1392 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 E-commerce recommendation system on cloud computing

More information

Statistical Challenges with Big Data in Management Science

Statistical Challenges with Big Data in Management Science Statistical Challenges with Big Data in Management Science Arnab Kumar Laha Indian Institute of Management Ahmedabad Analytics vs Reporting Competitive Advantage Reporting Prescriptive Analytics (Decision

More information

Twitter and Fashion Forecasting: An Exploration of Tweets regarding Trend Identification for Fashion Forecasting

Twitter and Fashion Forecasting: An Exploration of Tweets regarding Trend Identification for Fashion Forecasting Twitter and Fashion Forecasting: An Exploration of Tweets regarding Trend Identification for Fashion Forecasting Samaneh Beheshti-Kashi University of Bremen, Germany Abstract The fashion industry faces

More information

CIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet

CIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet CIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet Muhammad Atif Qureshi 1,2, Arjumand Younus 1,2, Colm O Riordan 1,

More information