1

Twitter mood predicts the stock market.
Johan Bollen1,? ,Huina Mao1,? ,Xiao-Jun Zeng2 .

arXiv:1010.3003v1 [cs.CE] 14 Oct 2010?: authors made equal contributions.

Abstract—Behavioral economics tells us that emotions canprofoundly affect individual behavior and decision-making. Does
this also apply to societies at large, i.e. can societies experiencemood states that affect their collective decision making? Byextension is the public mood correlated or even predictive of
economic indicators? Here we investigate whether measurementsof collective mood states derived from large-scale Twitter feeds
are correlated to the value of the Dow Jones Industrial Average(DJIA) over time. We analyze the text content of daily Twitter
feeds by two mood tracking tools, namely OpinionFinder thatmeasures positive vs. 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 cross-validate
the resulting mood time series by comparing their ability todetect the public’s response to the presidential election andThanksgiving day in 2008. A Granger causality analysis anda Self-Organizing Fuzzy Neural Network are then used toinvestigate the hypothesis that public mood states, as measured by
the OpinionFinder and GPOMS mood time series, are predictiveof changes in DJIA closing values. Our results indicate that theaccuracy of DJIA predictions can be significantly improved by
the inclusion of specific public mood dimensions but not others.
We find an accuracy of 87.6% in predicting the daily up anddown changes in the closing values of the DJIA and a reductionof the Mean Average Percentage Error by more than 6%.
Index Terms—stock market prediction — twitter — moodanalysis.

I. I NTRODUCTIONTOCK market prediction has attracted much attention
from academia as well as business. But can the stock
market really be predicted? Early research on stock market
prediction [1], [2], [3] was based on random walk theory
and the Efficient Market Hypothesis (EMH) [4]. According
to the EMH stock market prices are largely driven by new
information, i.e. news, rather than present and past prices.
Since news is unpredictable, stock market prices will follow arandom walk pattern and cannot be predicted with more than
50 percent accuracy [5].
There are two problems with EMH. First, numerous studies
show that stock market prices do not follow a random walkand can indeed to some degree be predicted [5], [6], [7], [8]thereby calling into question EMH’s basic assumptions. Second, recent research suggests that news may be unpredictable
but that very early indicators can be extracted from onlinesocial media (blogs, Twitter feeds, etc) to predict changes
in various economic and commercial indicators. This may
conceivably also be the case for the stock market. For example,[11] shows how online chat activity predicts book sales. [12]uses assessments of blog sentiment to predict movie sales.
[15] predict future product sales using a Probabilistic Latent
Semantic Analysis (PLSA) model to extract indicators ofSsentiment from blogs. In addition, Google search queries havebeen shown to provide early indicators of disease infection
rates and consumer spending [14]. [9] investigates the relations
between breaking financial news and stock price changes.
Most recently [13] provide a ground-breaking demonstrationof how public sentiment related to movies, as expressed on
Twitter, can actually predict box office receipts.
Although news most certainly influences stock market
prices, public mood states or sentiment may play an equallyimportant role. We know from psychological research thatemotions, in addition to information, play an significant rolein human decision-making [16], [18], [39]. Behavioral financehas provided further proof that financial decisions are significantly driven by emotion and mood [19]. It is therefore
reasonable to assume that the public mood and sentiment candrive stock market values as much as news. This is supported
by recent research by [10] who extract an indicator of publicanxiety from LiveJournal posts and investigate whether its
variations can predict S&P500 values.
However, if it is our goal to study how public mood
influences the stock markets, we need reliable, scalable andearly assessments of the public mood at a time-scale andresolution appropriate for practical stock market prediction.
Large surveys of public mood over representative samples ofthe population are generally expensive and time-consuming
to conduct, cf. Gallup’s opinion polls and various consumer
and well-being indices. Some have therefore proposed indirect
assessment of public mood or sentiment from the results ofsoccer games [20] and from weather conditions [21]. The
accuracy of these methods is however limited by the low
degree to which the chosen indicators are expected to becorrelated with public mood.
Over the past 5 years significant progress has been madein sentiment tracking techniques that extract indicators of
public mood directly from social media content such as blog
content [10], [12], [15], [17] and in particular large-scale
Twitter feeds [22]. Although each so-called tweet, i.e. an
individual user post, is limited to only 140 characters, theaggregate of millions of tweets submitted to Twitter at any
given time may provide an accurate representation of publicmood and sentiment. This has led to the development of realtime sentiment-tracking indicators such as [17] and “Pulse of
Nation”1 .
In this paper we investigate whether public sentiment, as
expressed in large-scale collections of daily Twitter posts, canbe used to predict the stock market. We use two tools tomeasure variations in the public mood from tweets submitted
1 http://www.ccs.neu.edu/home/amislove/twittermood/2

to the Twitter service from February 28, 2008 to December 19,
2008. The first tool, OpinionFinder, analyses the text content
of tweets submitted on a given day to provide a positive
vs. negative daily time series of public mood. The second
tool, GPOMS, similarly analyses the text content of tweets togenerate a six-dimensional daily time series of public moodto provide a more detailed view of changes in public along
a variety of different mood dimensions. The resulting publicmood time series are correlated to the Dow Jones Industrial
Average (DJIA) to assess their ability to predict changes inthe DJIA over time. Our results indicate that the prediction
accuracy of standard stock market prediction models is significantly improved when certain mood dimensions are included,
but not others. In particular variations along the public mooddimensions of Calm and Happiness as measured by GPOMS
seem to have a predictive effect, but not general happiness asmeasured by the OpinionFinder tool.

II. R ESULTS
A. Data and methods overview
We obtained a collection of public tweets that was recorded
from February 28 to December 19th, 2008 (9,853,498 tweets
posted by approximately 2.7M users). For each tweet theserecords provide a tweet identifier, the date-time of the submission (GMT+0), its submission type, and the text content
of the Tweet which is by design limited to 140 characters.
After removal of stop-words and punctuation, we group all
tweets that were submitted on the same date. We only take
into account tweets that contain explicit statements of theirauthor’s mood states, i.e. those that match the expressions “ifeel”,“i am feeling”,“i’m feeling”,“i dont feel”, “I’m”, “Im”,“I am”, and “makes me”. In order to avoid spam messages andother information-oriented tweets, we also filter out tweets thatmatch the regular expressions ”http:” or ”www.”
As shown in Fig. 1 we then proceed in three phases. In thefirst phase, we subject the collections of daily tweets to 2 moodassessment tools: (1) OpinionFinder which measures positive
vs. negative mood from text content, and (2) GPOMS whichmeasures 6 different mood dimensions from text content.
This results in a total of 7 public mood time series, one
generated by OpinionFinder and six generated by GPOMS,
each representing a potentially different aspect of the public’s
mood on a given day. In addition, we extract a time seriesof daily DJIA closing-values from Yahoo! Finance. In thesecond phase, we investigate the hypothesis that public moodas measured by GPOMS and OpinionFinder is predictive of
future DJIA values. We use a Granger causality analysis inwhich we correlate DJIA values to GPOMs and OF valuesof the past n days. In the third phase, we deploy a SelfOrganizing Fuzzy Neural Network model to test the hypothesisthat the prediction accuracy of DJIA prediction models can be
improved by including measurements of public mood. We are
not interested in proposing an optimal DJIA prediction model,
but to assess the effects of including public mood informationon the accuracy of a “baseline” prediction model.

Methodology-n (lag)1
text
analysis
Twitterfeed

Mood indicators (daily)(1) OpinionFinder

Grangercausality~

F-statistic
p-value2(2) G-POMS (6 dim.)(3) DJIA

t-1
t-2
t-3

~

DJIA

normalization Stock market (daily)

SOFNN

predicted
value MAPE

3

Direction %

t=0
value

Data sets and timeline(1) OF ~GPOMS(2) Granger Causality analysis
test(3) SOFNN training
feb28
2008

apr

may

jun

jul

aug

sep

oct

nov

dec

dec20
2008

Fig. 1. Diagram outlining 3 phases of methodology and corresponding data
sets: (1) creation and validation of OpinionFinder and GPOMS public moodtime series from October 2008 to December 2008 (Presidential Election andThanksgiving), (2) use of Granger causality analysis to determine correlation
between DJIA, OpinionFinder and GPOMS public mood from August 2008 to
December 2008, and (3) training of a Self-Organizing Fuzzy Neural Network
to predict DJIA values on the basis of various combinations of past DJIA
values and OF and GPOMS public mood data from March 2008 to December
2008.

B. Generating public mood time series: OpinionFinder and
GPOMS
OpinionFinder (OF)2 is a publicly available software package for sentiment analysis that can be applied to determine
sentence-level subjectivity [25], i.e. to identify the emotional polarity (positive or negative) of sentences. It has
been successfully used to analyze the emotional content oflarge collections of tweets [26] by using the OF lexicon todetermine the ratio of positive versus negative tweets on agiven day. The resulting time series were shown to correlatewith the Consumer Confidence Index from Gallup3 and theReuters/University of Michigan Surveys of Consumers4 overa given period of time. We adopt OF’s subjective lexicon thathas been established upon previous work [37], [38], [24]. We
select positive and negative words that are marked as either“weak” and “strong” from the OF sentiment lexicon resulting
in a list of 2718 positive and 4912 negative words. For each
tweet we determine whether it contains any number of negativeand positive terms from the OF lexicon. For each occurrence
we increase the score of either negative or positive tweets by
1 and calculate the ratio of positive vs. negative messages forthe tweets posted on the same day t.
Like many sentiment analysis tools OF adheres to a unidimensional model of mood, making binary distinctions between
positive and negative sentiment [23]. This may however ignore
the rich, multi-dimensional structure of human mood. To
capture additional dimensions of public mood we created asecond mood analysis tools, labeled GPOMS, that can measure
human mood states in terms of 6 different mood dimensions,
namely Calm, Alert, Sure, Vital, Kind and Happy. GPOMS’
2 http://www.cs.pitt.edu/mpqa/opinionfinderrelease/3 http://www.gallup.com/poll/122840/Gallup-Daily-Economic-Indexes.aspx
4 http://www.sca.isr.umich.edu/

3mood dimensions and lexicon are derived from an existing
and well-vetted psychometric instrument, namely the Profile
of Mood States (POMS-bi)[32], [33]. To make it applicableto Twitter mood analysis we expanded the original 72 termsof the POMS questionnaire to a lexicon of 964 associatedterms by analyzing word co-occurrences in a collection of
2.5 billion 4- and 5-grams5 computed by Google in 2006
from approximately 1 trillion word tokens observed in publicly
accessible Webpages [35], [36]. The enlarged lexicon of 964
terms thus allows GPOMS to capture a much wider variety ofnaturally occurring mood terms in Tweets and map them totheir respective POMS mood dimensions. We match the terms
used in each tweet against this lexicon. Each tweet term thatmatches an n-gram term is mapped back to its original POMS
terms (in accordance with its co-occurence weight) and via the
POMS scoring table to its respective POMS dimension. The
score of each POMS mood dimension is thus determined as theweighted sum of the co-occurence weights of each tweet termthat matched the GPOMS lexicon. All data sets and methods
are available on our project web site6 .
To enable the comparison of OF and GPOMS time series wenormalize them to z-scores on the basis of a local mean andstandard deviation within a sliding window of k days before
and after the particular date. For example, the z-score of timeseries Xt , denoted ZXt , is defined as:

characterized by a significant drop in Calm indicating highly
elevated levels of public anxiety. Election Day itself is characterized by a reversal of Calm scores indicating a significant
reduction in public anxiety, in conjunction with a significant
increases of Vital, Happy as well as Kind scores. The latter
indicates a public that is energized, happy and friendly onelection day. On November 5, these GPOMS dimensions continue to indicate positive mood levels, in particular high levelsof Calm, Sure, Vital and Happy. After November 5, all mooddimensions gradually return to the baseline. The public mood
response to Thanksgiving on November 27, 2008 provides acounterpart to the differentiated response to the Presidential
election. On Thanksgiving day we find a spike in Happyvalues, indicating high levels of public happiness. However,no other mood dimensions are elevated on November 27.
Furthermore, the spike in Happy values is limited to the oneday, i.e. we find no significant mood response the day before
or after Thanksgiving.
OpinionFinder
1.75

day afterelectionThanksgiving

1.25
CALM
1
-1
pre- election
anxiety
ALERT

Xt − x̄(Xt±k )(1)
σ(Xt±k )where x̄(Xt±k ) and σ(Dt±k ) represent the mean and standard deviation of the time series within the period [t−k, t+k].
This normalization causes all time series to fluctuate around azero mean and be expressed on a scale of 1 standard deviation.

1

ZXt =

C. Cross-validating OF and GPOMS time series against largesocio-cultural events
We first validate the ability of OF and GPOMS to capture
various aspects of public mood. To do so we apply themto tweets posted in a 3-month period from October 5, 2008
to December 5, 2008. This period was chosen specificallybecause it includes several socio-cultural events that may have
had a unique, significant and complex effect on public moodnamely the U.S presidential election (November 4, 2008) andThanksgiving (November 27, 2008). The OF and GPOMS
measurements can therefore be cross-validated against the
expected emotional responses to these events. The resulting
mood time series are shown in Fig. 2 and are expressed in
z-scores as given by in Eq. 1.
Fig. 2 shows that the OF successfully identifies the public’s
emotional response to the Presidential election on November
4th and Thanksgiving on November 27th. In both cases OF
marks a significant, but short-lived uptick in positive sentiment
specific to those days.
The GPOMS results reveal a more differentiated publicmood response to the events in the three-day period surrounding the election day (November 4, 2008). November 3, 2008 is
5 n-grams are frequently occurring sequences of terms in text of length n,for example “we are the robots” could be a frequent 4-gram.
6 http://terramood.informatics.indiana.edu/data

-1
SURE

electionresultsz-scores

1
-1
VITAL

pre! electionenergy

1
1
-1

KIND

-1

HAPPY

Thanksgiving
happiness1-1
Oct 22

Oct 29No v 05

No v 12No v 19

No v 26

Fig. 2.
Tracking public mood states from tweets posted between October
2008 to December 2008 shows public responses to presidential election andthanksgiving.

A visual comparison of Fig. 2 suggests that GPOMS’
Happy dimension best approximates the mood trend provided
by OpinionFinder. To quantitatively determine the relations
between GPOMS’s mood dimensions and the OF mood trends,
we test the correlation between the trend obtained from OF
lexicon and the six dimensions of GPOMS using multiple
regression. The regression model is shown in Eq. 2.
YOF = α +nX

βi Xi + t(2)iwhere X1 , X2 , X3 , X4 , X5 and X6 represent the mood timeseries obtained from the 6 GPOMS dimensions, respectively

4

TABLE IM ULTIPLE R EGRESSION R ESULTS FOR O PINION F INDER VS . 6 GPOMS
MOOD DIMENSIONS .
Coeff.
Std.Err.
t
p
1.731
1.348
1.284
0.20460
0.199
2.319
0.086
0.932
3.897
0.613
6.356
4.25e-08 ? ? ?
1.763
0.595
2.965
0.004??
1.687
1.377
1.226
0.226
2.770
0.578
4.790
1.30e-05 ??
Residual Std.Err
Adj.R2
F6,55
p
0.078
0.683
22.93
2.382e-13(p-value < 0.001: ? ? ?, p-value < 0.05: ??, p-value < 0.1: ?)Parameters
Calm (X1 )Alert (X2 )Sure (X3 )Vital (X4 )Kind (X5 )Happy (X6 )Summary

We perform the Granger causality analysis according tomodel L1 and L2 shown in Eq. 3 and 4 for the period oftime between February 28 to November 3, 2008 to exclude
the exceptional public mood response to the Presidential
Election and Thanksgiving from the comparison. GPOMS andOpinionFinder time series were produced for 342,255 tweets
in that period, and the daily Dow Jones Industrial Average(DJIA) was retrieved from Yahoo! Finance for each day8 .
L1 : Dt = α +nXβi Dt−i + t

(3)

i=1

7 [10] uses only one mood index, namely Anxiety, but we investigate therelation between DJIA values and all Twitter mood dimensions measured by
GPOMS and OpinionFinder

βi Dt−i +nX

γi Xt−i + t

(4)

i=1

Based on the results of our Granger causality (shown in
Table II), we can reject the null hypothesis that the mood timeseries do not predict DJIA values, i.e. β{1,2,··· ,n} 6= 0 with ahigh level of confidence. However, this result only applies to
1 GPOMS mood dimension. We observe that X1 (i.e. Calm)
has the highest Granger causality relation with DJIA for lags
ranging from 2 to 6 days (p-values < 0.05). The other fourmood dimensions of GPOMS do not have significant causal
relations with changes in the stock market, and neither does
the OpinionFinder time series.
To visualize the correlation between X1 and the DJIA in
more detail, we plot both time series in Fig. 3. To maintainthe same scale, we convert the DJIA delta values Dt and moodindex value Xt to z-scores as shown in Eq. 1.
bankbail-out

2
1

1
0

0

-1

-1

-2

-2

Calm z-score

2
DJIA z-score

After establishing that our mood time series responds tosignificant socio-cultural events such as the Presidential election and Thanksgiving, we are concerned with the question
whether other variations of the public’s mood state correlatewith changes in the stock market, in particular DJIA closing
values. To answer this question, we apply the econometrictechnique of Granger causality analysis to the daily timeseries produced by GPOMS and OpinionFinder vs. the DJIA.
Granger causality analysis rests on the assumption that if avariable X causes Y then changes in X will systematicallyoccur before changes in Y . We will thus find that the lagged
values of X will exhibit a statistically significant correlation
with Y . Correlation however does not prove causation. We
therefore use Granger causality analysis in a similar fashion
to [10]; we are not testing actual causation but whether one
time series has predictive information about the other or not7 .
Our DJIA time series, denoted Dt , is defined to reflect dailychanges in stock market value, i.e. its values are the delta
between day t and day t − 1: Dt = DJIAt − DJIAt−1 . To
test whether our mood time series predicts changes in stock
market values we compare the variance explained by two linear
models as shown in Eq. 3 and Eq. 4. The first model (L1 )uses only n lagged values of Dt , i.e. (Dt−1 , · · · , Dt−n ) for
prediction, while the second model L2 uses the n lagged valuesof both Dt and the GPOMS plus the OpinionFinder mood timeseries denoted Xt−1 , · · · , Xt−n .

n
X
i=1

2
DJIA z-score

D. Bivariate Granger Causality Analysis of Mood vs. DJIA
prices

L2 : Dt = α +

1
0
-1
-2

2
Calm z-score

Calm, Alert, Sure, Vital, Kind and Happy.
The multiple linear regression results are provided inTable I (coefficient and p-values), and indicate that YOF
is significantly correlated with X3 (Sure), X4 (Vital) andX6 (Happy), but not with X1 (Calm), X2 (Alert) and X5(Kind). We therefore conclude that certain GPOMS mooddimension partially overlap with the mood values provided by
OpinionFinder, but not necessarily all mood dimensions that
may be important in describing the various components ofpublic mood e.g. the varied mood response to the Presidential
election. The GPOMS thus provides a unique perspective on
public mood states not captured by uni-dimensional tools suchas OpinionFinder.

1
0
-1
-2

A ug 09

A ug 29

Sep 18

Oct 08

Oct 28Fig. 3.
A panel of three graphs. The top graph shows the overlap of the
day-to-day difference of DJIA values (blue: ZDt ) with the GPOMS’ Calmtime series (red: ZXt ) that has been lagged by 3 days. Where the two graphs
overlap the Calm time series predict changes in the DJIA closing values that
occur 3 days later. Areas of significant congruence are marked by gray areas.
The middle and bottom graphs show the separate DJIA and GPOMS’ Calmtime series.

As can be seen in Fig. 3 both time series frequently overlap
or point in the same direction. Changes in past values of Calm(t − 3 ) predicts a similar rise or fall in DJIA values (t =8 Our DJIA time series has no values for weekends and holidays because
trading is suspended during those days. We do not linearly extropolate to fill
the gaps. This results in a time series of 64 days.

5

TABLE IIS TATISTICAL SIGNIFICANCE ( P - VALUES ) OF BIVARIATE G RANGER - CAUSALITY CORRELATION BETWEEN MOODS AND DJIA IN PERIOD F EBRUARY 28,
2008 TO N OVEMBER 3, 2008.
Lag
OF
Calm
Alert
1 day
0.085?
0.272
0.952
??
2 days
0.268
0.013
0.973
3 days0.436
0.022??
0.981
0.218
0.030??
0.998
4 days
5 days0.300
0.036??
0.989?
6 days
0.446
0.065
0.996
0.620
0.157
0.999
7 days(p-value < 0.05: ??, p-value < 0.1: ?)0. The Calm mood dimension thus has predictive value withregards to the DJIA. In fact the p-value for this shorter period,
i.e. August 1, 2008 to October 30 2008, is significantly lower(lag n = 3, p = 0.009) than that listed in Table II for theperiod February 28, 2008 to November 3, 2008.
The cases in which the t − 3 mood time series fails to
track changes in the DJIA are nearly equally informativeas where it doesn’t. In particular we point to a significant
deviation between the two graphs on October 13th where the
DJIA surges by more than 3 standard deviations trough-topeak. The Calm curve however remains relatively flat at thattime after which it starts to again track changes in the DJIA
again. This discrepancy may be the result of the the Federal
Reserve’s announcement on October 13th of a major bankbailout initiative which unexpectedly increase DJIA values thatday. The deviation between Calm values and the DJIA on thatday illustrates that unexpected news is not anticipated by the
public mood yet remains a significant factor in modeling thestock market.
E. Non-linear models for emotion-based stock prediction
Our Granger causality analysis suggests a predictive relation between certain mood dimensions and DJIA. However, Granger causality analysis is based on linear regression
whereas the relation between public mood and stock market
values is almost certainly non-linear. To better address these
non-linear effects and assess the contribution that public moodassessments can make in predictive models of DJIA values, we
compare the performance of a Self-organizing Fuzzy Neural
Network (SOFNN) model [30] that predicts DJIA values on
the basis of two sets of inputs: (1) the past 3 days of DJIA
values, and (2) the same combined with various permutationsof our mood time series (explained below). Statistically significant performance differences will allow us to either confirm
or reject the null hypothesis that public mood measurement
do not improve predictive models of DJIA values.
We use a SOFNN as our prediction model since they havepreviously been used to decode nonlinear time series data
which describe the characteristics of the stock market [28]and predict its values [29]. Our SOFNN in particular is a fivelayer hybrid neural network with the ability to self-organize
its own neurons in the learning process. A similar organization
has been successfully used for electricial load forecasting in
our previous work [31].
To predict the DJIA value on day t, the input attributesof our SOFNN include combinations of DJIA values and

Sure
0.648
0.811
0.349
0.415
0.544
0.691
0.381

Vital
0.120
0.369
0.418
0.475
0.553
0.682
0.713

Kind
0.848
0.991
0.991
0.989
0.996
0.994
0.999

Happy
0.388
0.7061
0.723
0.750
0.173
0.081?
0.150

mood values of the past n days. We choose n = 3 since
the results shown in Table II indicate that past n = 4 theGranger causal relation between Calm and DJIA decreases
significantly. All historical load values are linearly scaled to[0,1]. This procedure causes every input variable be treatedwith similar importance since they are processed within auniform range.
SOFNN models require the tuning of a number of parameters that can influence the performance of the model.
We maintain the same parameter values across our various input combinations to allow an unbiased comparison ofmodel performance, namely δ = 0.04, σ = 0.01, krmse =0.05, kd (i), (i = 1, ..., r) = 0.1 where r is the dimension of
input variables and krmse is the expected training root meansquared error which is a predefined value.
To properly evaluate the SOFNN model’s ability to predict
daily DJIA prices, we extend the period under consideration
to February 28, 2008 to December 19, 2008 for training
and testing. February 28, 2008 to November 28, 2008 is
chosen as the longest possible training period while Dec
1 to Dec 19, 2008 was chosen as the test period because
it was characterized by stabilization of DJIA values after
considerable volatility in previous months and the absence of
any unusual or significant socio-cultural events. Fig. 4 showsthat the Fall of 2008 is an unusual period for the DJIA due
to a sudden dramatic decline of stock prices. This variability
may in fact render stock market prediction more difficult thanin other periods.
DJIA daily closing value (March 2008−December 2008

13000
12000
11000
10000
9000
8000
Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec 2008

Fig. 4.
Daily Dow Jones Industrial Average values between February 28,
2008 and December 19, 2008.

The Granger causality analysis indicates that only Calm(and to some degree Happy) is Granger-causative of DJIA
values. However, the other mood dimensions could still contain predictive information of DJIA values when combined

6with other mood dimensions. For example, Happy may not
be independently linearly related with DJIA, but it may
nevertheless improve the SOFNN prediction accuracy whencombined with Calm. To clarify these questions, we investigate
seven permutations of input variables to the SOFNN model,
the first of which, denoted I0 , represents a naive, baselinemodel that has been trained to predict DJIA values at time
t from the historical values at time {t − 1, t − 2, t − 3} :I0

= {DJIAt−3,2,1 }I1= {DJIAt−3,2,1 , X1,t−3,2,1 }I1,2= {DJIAt−3,2,1 , X1,t−3,2,1 , X2,t−3,2,1 }I1,3= {DJIAt−3,2,1 , X1,t−3,2,1 , X3,t−3,2,1 }···DJIAt−3,2,1 represents the DJIA values and X1,t−3,2,1
represents the values of the GPOMS mood dimension 1, attime t − 3, t − 2, and t − 1. According to the same notation
I1,3 , I1,4 , I1,5 , I1,6 represent a combination of historical DJIA
with mood dimensions 3, 4, 5 and 6 at time t − 3, t − 2, andt − 1. For example, I1,6 represents a set of inputs that includes
the DJIA values t − 3, t − 2, and t − 1, and mood dimensions
1 and 6 at the same times.
In order to compare the performance of the GPOMS mooddata with the positive/negative sentiment values given by
OpinionFinder, we additionally define the input combination:
IOF= {DJIAt−3,2,1 , XOF,t−3,2,1 }Forecasting accuracy is measured in terms of the average
Mean Absolute Percentage Error (MAPE) and the direction
accuracy (up or down) during the test period (December 1
to December 19, 2008). The prediction results are shown in
Table III.
We can draw several conclusions from these results. First,
adding positive/negative sentiment obtained from OF (IOF )has no effect on prediction accuracy compared to using only
historical DJIA values(I0 ). This confirms the results of ourGranger causuality analysis.
Second, adding Calm, i.e. input I1 , we find the highest
prediction accuracy. Compared to I0 and all other input combinations, adding input I1 leads to significant improvements
in MAPE values (1.83% vs. the maximum of 2.13% and1.95% for IOF ) and direction accuracy (86.7% compared to73.3% for IOF and 46.7% for I1,3 ). Thirdly, I1,3 and I1,4
actually reduce prediction accuracy significantly both in termsof MAPE and direction %, most likely because X3 (Sure)and X4 (Vital) do not contain information that is usefulfor prediction of DJIA values causing prediction accuracy
to regress to chance levels. It is notable that I1,6 , i.e. a
combination of X6 and X1 does significantly reduce average
MAPE, and provides good direction accuracy (80%). This is
surprising since X6 (i.e. Happy) does not have a good Granger
causality relation with DJIA at a lag of n = 3 days (seeTable II, p = 0.723). However in combination with Calm, it
produces a more accurate SOFNN prediction (MAPE=1.79%)
and direction accuracy (80%).

To assess the statistical significance of the SOFNN achieving the above mentioned accuracy of 87.6% in predicting theup and down movement of the DJIA we calculate the odds ofthis result occurring by chance. The binomial distribution indicates that the probability of achieving exactly 87.6% correct
guesses over 15 trials (20 days minus weekends) with a 50%
chance of success on each single trial equals 0.32%. Taken
over the entire length of our data set (February 28 to December
20, excluding weekends) we find approximately 10.9 of such
20 day periods. The odds that the mentioned probability would
hold by chance for a random period of 20 days within that
period is then estimated to be 1−(1−0.0032)10.9 = 0.0343 or
3.4%. The SOFNN direction accuracy is thus most likely not
the result of chance nor our selecting a specifically favorable
test period.
In addition, we test the linear effect of both Calm(X1 ) andHappy (X6 ) on DJIA, with a nested F − test between the fullmodel F and reduced model R shown as follows:

F : Dt = α +nPβi Dt−i +

i=1

R:

3
P

γi X1,t−i +i=1

Dt = α +nP
i=1βi Dt−i +3P

γi X6,t−i + t

i=1
3
P

γi X1,t−i + t

i=1

We find a p-value of 0.66 and an F-statistic of 0.53
indicating that a linear combination of X1 and X6 produces
worse results than X1 alone. Since the SOFNN prediction ismore accurate when using a combination of X1 and X6 , weconclude that this confirms a nonlinear relation among thedifferent dimensions of moods.
III. D ISCUSSION
In this paper, we investigate whether public mood asmeasured from large-scale collection of tweets posted on
twitter.com is correlated or even predictive of DJIA values.
Our results show that changes in the public mood state canindeed be tracked from the content of large-scale Twitter feeds
by means of rather simple text processing techniques and thatsuch changes respond to a variety of socio-cultural drivers ina highly differentiated manner. Among the 7 observed mooddimensions only some are Granger causative of the DJIA;
changes of the public mood along these mood dimensions
match shifts in the DJIA values that occur 3 to 4 days later.
Surprisingly we do not observe this effect for OpinionFinder’sassessment of public mood states in terms of positive vs. negative mood but rather for the GPOMS dimension labeled“Calm”. The calmness of the public (measured by GPOMS)
is thus predictive of the DJIA rather than general levels of
positive sentiment as measured by OpinionFinder. A SelfOrganizing Fuzzy Neural Network trained on the basis ofpast DJIA values and our public mood time series furthermore
demonstrated the ability of the latter to significantly improve
the accuracy of even the most basic models to predict DJIA
closing values. Given the performance increase for a relatively
basic model such as the SOFNN we are hopeful to find equal
or better improvements for more sophisticated market models
that may in fact include other information derived from news

7

TABLE III
DJIA DAILY P REDICTION U SING SOFNN
Evaluation
MAPE (%)
Direction (%)

IOF
1.95
73.3

I0
1.94
73.3

I1
1.83
86.7?

sources, and a variety of relevant economic indicators. These
results have implications for existing sentiment tracking tools
as well as surveys of “self-reported subjective well-being” inwhich individuals evaluate the extent to which they experience
positive and negative affect, happiness, or satisfaction withlife [40]. Such surveys are relatively expensive and timeconsuming, and may nevertheless not allow the measurementof public mood along mood dimensions that are relevant
to assess particular socio-economic indicators. Public moodanalysis from Twitter feeds on the other hand offers anautomatic, fast, free and large-scale addition to this toolkit
that may in addition be optimized to measure a variety ofdimensions of the public mood state.
Our analysis does not acknowledge a number of importantfactors that will form the basis of future research. First, wenote that our analysis is not designed to be limited to any
particular geographical location nor subset of the world’s
population. This approach may be appropriate since the US
stock markets are affected by individuals worldwide, but forthe particular period under observation Twitter.com users werede facto predominantly English speaking and located in the
US. As Twitter.com’s user base becomes increasingly international and the use of smartphones equipped with geo-location
increases, future analysis will have to factor in location andlanguage to avoid geographical and cultural sampling errors.
Second, although we have cross-validated the results of 2
different tools to assess public mood states, we have noknowledge of the “ground truth” for public mood states nor infact for the particular subsample of the population represented
by the community of Twitter.com users. This problem can only
be addressed by increased research into direct assessments ofpublic mood states vs. those derived from online communities
such as Twitter. Third, these results are strongly indicative ofa predictive correlation between measurements of the publicmood states from Twitter feeds, but offer no information on
the causative mechanisms that may connect public mood stateswith DJIA values in this manner. One could speculate that thegeneral public is presently as strongly invested in the DJIA
as financial experts, and that therefore their mood states will
directly affect their investment decisions and thus stock market
values, but this too remains an area of future research.
ACKNOWLEDGMENT
This research was supported by NSF Grant BCS #1032101.
We thank David Crandell and Michael Nelson for their helpfulcomments on earlier versions of this manuscript. We are also
grateful to Daniel Kahneman for his early comments on ourwork.
R EFERENCES[1] Fama, Eugene F, e. a. (1969) International Economic Review 10, 1–21.

I1,2
2.03
60.0

I1,3
2.13
46.7

I1,4
2.05
60.0

I1,5
1.85
73.3

I1,6
1.79?
80.0

[2] Fama, E. F. (1991) Journal of Finance 46, 1575–617.
[3] H.Cootner, P. (1964) The random character of stock market prices.
(MIT).
[4] Fama, E. F. (1965) The Journal of Business 38, 34–105.
[5] Qian, Bo, Rasheed, & Khaled. (2007) Applied Intelligence 26, 25–33.
[6] Gallagher, L. A & Taylor, M. P. (2002) Southern Economic Journal 69,
345–362.
[7] Kavussanos, M & Dockery, E. (2001) Applied Financial Economics 11,
573–79.
[8] Butler, K. C & Malaikah, S. J. (1992) Journal of Banking and Finance
16, 197–210.
[9] Schumaker, R. P & Chen, H. (2009) ACM Trans. Inf. Syst. 27, 12:1–
12:19.
[10] Gilbert, E & Karahalios, K. (2010) Widespread worry and the stock
market.
[11] Gruhl, D, Guha, R, Kumar, R, Novak, J, & Tomkins, A. (2005) The
predictive power of online chatter. (ACM, New York, NY, USA), pp.
78–87.
[12] Mishne, G & Glance, N. (2006) Predicting Movie Sales from Blogger
Sentiment. AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs[13] S. Asur and B. A. Huberman 2010 Predicting the Future with Social
Media arXiv:1003.5699v1[14] Choi, H & Varian, H. (2009) Predicting the present with google trends.,(Google), Technical report.
[15] Liu, Y, Huang, X, An, A, & Yu, X. (2007) ARSA: a sentiment-awaremodel for predicting sales performance using blogs. (ACM, New York,NY, USA), pp. 607–614.
[16] Dolan, R. J. (2002) Science 298, 1191–1194.
[17] Dodds, Peter. (2009) Journal of Happiness July, doi: 10.1007/s10902009-9150-9[18] Damasio, A. R. (1994) Descartes’ error : emotion, reason, and thehuman brain. (Putnam), pp. xix, 312 p.+.
[19] Nofsinger, J. (2005) Journal of Behaviour Finance. 6, 144–160.
[20] Edmans, A, Garca, D, & Norli, . (2007) Journal of Finance 62, 1967–1998.
[21] Hirshleifer, D & Shumway, T. (2003) Journal of Finance 58, 1009–1032.
[22] Pak, A & Paroubek, P. (2010) Twitter as a Corpus for Sentiment Analysis
and Opinion Mining. (European Language Resources Association(ELRA), Valletta, Malta).
[23] Pang, B & Lee, L. (2008) Foundations and Trends in Information
Retrieval 2, 1–135.
[24] Wilson, T, Wiebe, J, & Hoffmann, P. (2005) Recognizing Contextual
Polarity in Phrase-Level Sentiment Analysis. (Vancouver, CA).
[25] Wilson, T, Hoffmann, P, Somasundaran, S, Kessler, J, Wiebe, J, Choi, Y,
Cardie, C, Riloff, E, & Patwardhan, S. (2005) OpinionFinder: A system
for subjectivity analysis. pp. 34–35.
[26] O’Connor, B, Balasubramanyan, R, Routledge, B. R, & Smith, N. A.
(2010) From Tweets to Polls: Linking Text Sentiment to Public Opinion
Time Series.
[27] Diener E, Diener M & Diener D (2009) Factors Predicting the Subjective
Well-Being of Nations. Social Indicators Research Series 38:43-70[28] Lapedes, A & Farber, R. (1987) Nonlinear signal processing using neuralnetwork: Prediction and system modeling, (Los Alamos National Lab
Technical Report), Technical report.
[29] Zhu, X, Wang, H, Xu, L, & Li, H. (2008) Expert Syst. Appl. 34, 3043–
3054.
[30] Leng, G, Prasad, G, & McGinnity, T. M. (2004) Neural Netw. 17, 1477–
1493.
[31] Mao, H, Zeng, X.-J, Leng, G, Zhai, Y, & Keane, A. J. (2009) IEEE
Transaction on Power System. 24, 1080–1090.
[32] Norcross, J. C, Guadagnoli, E, & Prochaska, J. O. (2006) Journal of
Clinical Psychology 40, 1270 – 1277.
[33] McNair, D, Heuchert, J. P, & Shilony, E. (2003) Profile of mood states.
Bibliography 1964–2002. (Multi-Health Systems).
[34] Pepe, A & Bollen, J. (2008) Between conjecture and memento: shaping
a collective emotional perception of the future.

8[35] Brants, T & Franz, A. (2006) Web 1t 5-gram version 1, (Linguistic Data
Consortium, Philadelphia), Technical report.
[36] Bergsma, S, Lin, D & Goebel, R (2009) IJCAI’09: Proceedings ofthe 21st international jont conference on Artifical intelligence, San
Francisco, CA., 1507–1512.
[37] Riloff, E, Wiebe, J, & Wilson, T. (2003) Learning subjective nouns
using extraction pattern bootstrapping. (Association for Computational
Linguistics, Morristown, NJ, USA), 25–32.
[38] Riloff, E & Wiebe, J. (2003) Learning extraction patterns for subjective
expressions. (Association for Computational Linguistics, Morristown,
NJ, USA), pp. 105–112.
[39] Kahneman, D & Tversky, Amos (1979) Prospect Theory: An Analysis
of Decision under Risk. (Econometrica), pp. 263–291.
[40] Frey, B. S. (2008) Happiness: A Revolution in Economics. (The MIT
Press).

Johan Bollen School of Informatics and Computing, Indiana UnviersityBloomington, United States Email:jbollen@indiana.edu

Huina Mao School of Informatics and Computing, Indiana UnviersityBloomington, United States Email: huinmao@indiana.edu

Xiao-Jun Zeng School of Computer Science, The University of Manchester,
United Kingdom Email: x.zeng@manchester.ac.uk