Link between Sentiment and Human Activity Represented by
Experiment Exploiting IoT Devices and Social Networks
Jaromir Salamon and Roman Moucek
Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia,
Universitni 8, Pilsen, Czech Republic
Keywords: Sentiment Extraction, Sentiment Analysis, Body Sensors, Machine Learning, Experiment.
Abstract: The Internet of Things world brings to our lives many opportunities to monitor our daily activities by
collecting data from various devices. Complementary to it, the data expressing opinions, suggestions,
interpretations, contradictions, and uncertainties are more accessible within variety of online resources. This
paper deals with collection and analysis of hard data representing the number of steps and soft data
representing the sentiment of participants who underwent a pilot experiment. The paper defines outlines of
the problem and presents possible sources of reliable data, sentiment evaluation, sentiment extraction using
machine learning methods, and links between the data collected from IoT devices and sentiment expressed
by the participant in a textual form. Then the results provided by using inferential statistics are presented.
The paper is concluded by discussion and summarization of results and future work proposals.
Analysis of human sentiment is currently a very
popular research task. Sentiment, identified in and
extracted from the text and/or speech, represents
subjective emotion of the writer/speaker associated
with some event, topic or situation. Sentiment
analysis is then usually considered as the use of
natural language/text processing methods and
computational linguistics to identify this kind of
information in the source text/speech.
However, emotions and feelings do not
necessarily have to be identified using only the
methods mentioned above. In addition to spoken and
textual expressions there exist physiological
symptoms and human activities that can be used as
data sources for sentiment analysis. Since the aim of
this article is not to provide a detailed analysis of
how to measure emotions, feelings and identify
sentiment, let us mention just a few examples. In
(Chanel, 2006) there was investigated arousal
dimension of human emotions from two different
physiological sources: peripheral signals and
electroencephalographic (EEG) signals from the
brain. Emotions were recognized from brain activity,
measured by the EEG signal, also in (Horlings,
Presence and quality of emotions, feelings, and
sentiment are often associated with overall human
life activities that include both physical activities
(e.g. total daily movement, physical exercises, and
sports training) and mental activities (e.g. learning,
mental load, and stress resistance). Then numeric
values obtained from various body sensors during
common human activities that can affect sentiment
could be considered as quantitative (depending on
the number of sensors and their capturing rate) and
qualitative (depending on the suitability of body
sensors to the task) descriptions of emotional
reactions of human body to a topic, event or
As a support of this abstract statement let us
imagine a situation when two people are arguing.
During this activity we can expect that both subjects
show up higher levels of blood pressure together
with higher levels of heart rate. Also the content of
their arguing written into a text form and translated
to the sentiment should correspond to elevated levels
of blood pressure and heart rate.
It is reasonable to expect that the quantity,
quality, and availability of various body sensors will
rapidly increase in near future. This will be
accompanied by overall availability of various
sensors within the IoT (Internet of Things) world.
Salamon, J. and Moucek, R.
Link between Sentiment and Human Activity Represented by Footsteps - Experiment Exploiting IoT Devices and Social Networks.
DOI: 10.5220/0005818204500457
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 450-457
ISBN: 978-989-758-170-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Then appropriate selection and use of suitable
sensors will play an important role in case of
context-aware applications.
Still, this article does want to present abstract
design principles for building such a complex
architecture, it focuses on design and
implementation of a pilot study that considers two
data sources for sentiment analysis: “classic” textual
source representing subjective emotions of people
participating in the experiment and raw data
collected from body sensors representing human
activities that could be associated with their
emotional state. We have also taken into account
that both data sources contain a lot of information
that is present, but irrelevant to our task.
Before we start looking for a link between the
sentiment expressed by text/speech and data
collected from body sensors, we need to find
appropriate source for both.
Thus we started to look for such data sources.
Both these types of data could be collected either
simultaneously or serially (in any order) but have to
be associated with a common emotional basis. So,
we defined several data source categories that should
be considered:
The first category includes un-supervised data
sources, typically data that are available on the
Internet with widely defined source and time
periods (e.g. a measurable activity implies
some textual discussion, more specifically: the
number of calories burned during sport
activities and provided with a sport tracker
implies discussion about the effectiveness of
various physical exercises).
The second category includes semi- or un-
supervised data sources that are available on
the Internet and/or as outputs from research
projects with strictly defined source and time
periods (e.g. blood pressure measurements
giving results for specific countries,
population, gender, or age accompanied by
discussions about blood pressure).
The third category includes supervised and
tightly defined data sources. It could be, for
example, an experiment on subjects that
provide data for sentiment analysis by writing
textual/spoken comments and collecting raw
data from body sensors in a specified time
period. The second example could be research
that provides blood pressure sensory values
and questionnaire results from patients having
blood pressure difficulties in a specific time
The presented categories differ in requirements
of what to be done during data collection. The third
category seems to be the easiest one for final data
interpretation. Since we were not successful in
finding appropriate and free data sources (we found
many sources satisfying required data source types,
but the sources did not have common emotional
basis), we decided to design and perform an
experiment that would provide data satisfying the
requirements for the third category of data sources.
One subject (the first author of the paper)
participated in a pilot experiment, which lasted 14
days. His task was to measure the number of
footsteps using a professional pedometer and write
short texts expressing his sentiment that were evenly
distributed throughout the day. The original goal
was to walk at least 10.000 steps a day and provide
at least 20 short text messages a day. The final
numbers of steps and text messages achieved during
the pilot experiment are shown in Table 1.
Table 1: Daily measures for the experiment.
Date Steps per day Texts per day
08/18/2014 1816 8
08/19/2014 8284 23
08/20/2014 8903 19
08/21/2014 3775 25
08/22/2014 11633 28
08/23/2014 4335 21
08/24/2014 5852 22
08/25/2014 10426 17
08/26/2014 5503 12
08/27/2014 5078 19
08/28/2014 11945 21
08/29/2014 3833 13
08/30/2014 3273 22
08/31/2014 3279 15
09/01/2014 6656 15
Average 6625 20
Total 94591 280
The experiment started 08/18/2014 at 7:40 PM
and ended 09/01/2014 at 12:40 PM. The average
numbers of steps and text messages were calculated
only for 13 days, because the experiment was carried
out only for a limited number of hours during the
first and last days. It is obvious from Table 1 that
Link between Sentiment and Human Activity Represented by Footsteps - Experiment Exploiting IoT Devices and Social Networks
originally defined goals were not met, at least not on
daily basis.
During the experiment the subject usually
walked only when he really needed to go
somewhere. When the subject wanted to reach the
daily goal, he had to walk without any purpose. The
distribution of steps is uneven during the day
(cumulative numbers of steps and their distribution
during the hours of the day are depicted in Figure 1).
It was much easier for the subject to meet the target
number of text messages. When he was behind with
the daily goal, he shortened the time period he wrote
3.1 Sentiment Expression
The sentiment derived from the subject’s data is
a subjective measure. This leads to the question how
to express and quantify it.
The recording phase of the experiment requires
expression of the sentiment from the subject during
a specific time frame. This is done through
description of activities and related mental and/or
physical processes that are accompanied by positive
and negative feelings.
More formally, if
is an entire set of
documents in a specific time frame 
and 
is a set of all activities in the
same specific time frame, then the document
representing sentiment is created based on the
specific activity
Then the sentiment quantification leads to the
classification task (if a text fragment is positively or
negatively polarized), see (Cambria, 2013) or
(Ikonomakis, 2005). This classification task is based
on the occurrence of positive and negative words
and their usage in a sentence or text fragment.
More formally, for an entire set of documents
in a specific time frame we have a classification
. Then one classification category
is assigned to each document.
3.2 Sentiment Collection
The requirement to describe subject’s activities and
mood during the whole day and throughout 14 days
implied also requirements for the recording system.
After considering available online systems as cloud
services for text document recording, online
task/planning systems, and discussion forums we
decided to use the social networking service Twitter
that has convenient characteristics. There is also
extensive experience with sentiment classification
on Twitter (Go, 2009).
Figure 1: Cumulative numbers of steps and their
distribution during the hours of the day.
We were afraid of one possible negative feature
of Twitter when the text is limited to 140 characters.
However, having experience from the pilot
experiment we finally started to consider it as
a practical feature.
As the result, descriptions of subject’s activities
and mood were recorded as up to 140 characters
long texts with timestamps providing information
about the order of each text in timeline. The limit of
140 characters finally seems to be advantageous
from two points of view. At first, participants do not
need to think about message content too much, they
just express the current situation and their mood.
Secondly, short messages support their willingness
to express themselves often or at all.
3.3 Sentiment Extraction
The crucial part of this work was sentiment
extraction and evaluation. Overall results and their
precision are dependent on the sentiment polarity
which was evaluated by human and also extracted
from the experimental corpus.
3.3.1 Human Evaluation
After collecting a complete corpus of 280 documents
we let a small group of 7 people to evaluate all
documents and assign one of three classification
values (1 for positive, -1 for negative and 0 for
neutral) expressing sentiment to each document.
Using equations (3) and (4) we get an evaluated
sentiment class
by summing up all numeric
representations of classification per person for
a specific document
and normalizing it according
HEALTHINF 2016 - 9th International Conference on Health Informatics
to classes represented by original numeric values:
Table 2 shows the results of human sentiment
evaluation of the document corpus.
Table 2: Summary of human evaluated sentiment classes.
Class name Classes count
negative (-1) 52
neutral (0) 21
positive (1) 207
Total 280
For every individual participant of human sentiment
evaluation there was a calculated percentage
Table 3: Percentage consensus for human evaluated
A 49 7 46 40 49 51
B 49 6 50 47 52 52
C 7 6 6 6 6 7
D 46 50 6 47 49 53
E 40 47 6 47 45 48
F 49 52 6 49 45 83
G 51 52 7 53 48 83
3.3.2 Machine Learning Sentiment
For the subsequent comparison to the human
evaluation described above and for the future work
over larger data we decided to use supervised
machine learning methods. From a set of possibly
usable methods we have chosen the most suitable
one and compared the results of human evaluated
sentiment to the sentiment extracted by machine
learning during testing of the final hypothesis.
Corpus selection
Because our corpus containing only 280 documents
(see Table 1) is really small we considered another
approach than usual. We used the whole
experimental corpus and its human evaluation for
testing (see Table 1 and Table 2) and following
external corpora for training:
Movie review corpus (Cornell University)
with 10662 documents annotated as
positive / negative (Pang, 2004) and (Pang,
2008). It contains movie reviews that were
classified with machine learning methods.
Table 4: Summary of Movie review corpus classes.
Class name Classes count
negative (-1) 5331
positive (1) 5331
Total 10662
Twitter sentiment corpus (Technische
Universität Berlin) with 10786 documents annotated
as positive, neutral, negative, and n/a (Narr, 2012). It
contains tweets that were human-annotated with
sentiment labels by three Mechanical Turk workers.
Table 5: Summary of Twitter sentiment corpus classes.
Class name Classes count
negative (-1) 1758
neutral (0) 5647
positive (1) 2873
n/a 508
Total 10786
Because the Movie Review corpus contains only
positive and negative classes, we needed to
eliminate all documents with the neutral class from
our test (human evaluated) corpus. For full
comparison we needed to keep consistency about
classes in all corpora, thus we eliminated neutral and
n/a classes from the Twitter sentiment corpus.
Machine Learning Methods
Considering the fact that sentiment was included
into text messages through the Twitter social
network for which Naïve Bayes and Support Vector
Machine learning methods (Annett, 2008) or (Yang,
1999) are mostly used, we decided to use them and
compare with more methods for both training
corpora and choose the most suitable of them from
the following list:
Decision Trees (DT)
Random Forrest (RF)
Naïve Bayes (NB)
Maximum Entropy (ME)
Support Vector Machines (SVM)
R language RTextTools (text classification via
supervised learning) and text mining packages were
used as tools. We used a supervised method and
calculated the following measures: precision, recall,
F-measure, and accuracy. The results are shown in
Table 6 and Table 7.
The most exact methods for Movie review
corpus are Random Forest and Support Vector
Machine method based on precision, recall, and
accuracy values.
Link between Sentiment and Human Activity Represented by Footsteps - Experiment Exploiting IoT Devices and Social Networks
Table 6: Metrics for Movie reviews corpus.
Precision 10.0 55.5 24.2 54.5 56.5
Recall 50.0 59.0 55.8 56.5 59.5
F-measure 16.5 52.5 33.7 49.0 49.0
Accuracy 20.1 58.7 56.0 52.9 51.7
Order 5 1 4 3 2
Table 7: Metrics for Twitter sentiment corpus.
Precision 40.0 62.0 22.9 55.0 58.0
Recall 50.0 62.5 57.7 56.0 59.5
F-measure 44.5 62.0 32.8 54.5 58.0
Accuracy 20.1 74.9 52.5 65.6 70.3
Order 5 1 4 3 2
The most exact methods for Twitter sentiment
corpus are also Random Forest and Support Vector
Machine method based on precision, recall, and
accuracy values.
From previous comparison are chosen Random
Forest and Support Vector Machine prediction used
with Twitter sentiment corpus. And for better
decision about the final method and its prediction
results used in further analysis we have compared
cross tables in Table 8 and Table 9.
Table 8: SVM cross table.
22 30 52
47 160 207
69 190
Table 9: Random Forest cross table.
22 30 52
35 172 207
57 202
Thanks to its much better measures and slightly
better cross table results is chosen predicted
sentiment with Random Forest method.
In the final analysis we were interested in trends in
link between the number of steps walked by the
participant and his sentiment expressed by tweets.
We did not need to care about measurement
uncertainty and errors, because it should not
influence trends during the time period in which the
experiment was done.
The previous steps were necessary to get basis
for the future analysis and research.
Data used in the further analysis are coming
from human evaluated sentiment and from machine
learning extracted sentiment, both in combination
with measured steps.
4.1 Hypothesis Definition
According to (Sibold, 2009) pilot study and other
research (Anderson, 2010) we defined the following
null and alternative hypothesis:
: Movement does not affect mood.
: Movement results in a positive mood.
Then we were looking for a pattern that could be
described as: there are more steps with positive
sentiment than with negative sentiment.
4.2 Data Overview
The variables of interest for the analysis are:
Sentiment: categorical ordinal and
explanatory variable
Steps: continuous numeric and response
4.2.1 Data Pre-processing
Steps are obtained with higher granularity with
approximately one minute. But the sentiment
expressed via tweets is recorded only several times a
day. This needs to be aligned together through
aggregation of steps.
According to the alternative hypothesis
“Movement results in a positive mood” which could
be also interpreted as: “Expression of sentiment is
a result of movement”, all preceding steps were
aggregated to the nearest sentiment (see Figure 2).
HEALTHINF 2016 - 9th International Conference on Health Informatics
Figure 2: The whole preceding time window of steps is
aggregated to following sentiment.
4.3 Hypothesis Testing
4.3.1 Exploratory Data Analysis
An interesting output of exploratory data analysis is
the mean value for both sentiment classes and
density plots (Table 10, Figure 3, Table 11, and
Figure 4).
Table 10: Statistical results for human sentiment
Sentiment classes
negative (-1) positive (1)
N 52 207
mean (µ) 304.13 380.56
sd 727.43 630.99
Figure 3: Statistical results for human sentiment
Table 11: Statistical results for machine learning sentiment
Sentiment classes
negative (-1) positive (1)
N 57 202
mean (µ) 292.79 385.65
sd 364.01 710.42
Figure 4: Statistical results for machine learning sentiment
The results from exploratory data analysis shown
that alternative hypothesis (6) is valid for both
human sentiment evaluation and machine learning
sentiment extraction. This observed assumption
needs to be confirmed through the following
statistical inference. We will look if the significance
of difference between means of steps assigned to
positive and negative sentiment is big enough to
confirm alternative hypothesis (6).
4.3.2 Statistical Inference
When testing a hypothesis with a categorical
explanatory variable with two levels and
a quantitative response variable, the paired t-test is
used for statistical inference. As a value of statistical
significance we have taken α = 0.05. The results are
presented in Table 12.
Table 12: t-test for equality of means.
Sentiment eval. methods
t -0.70 -1.34
df 71.47 182.37
mean difference -76.43 -92.86
se difference 96.44 -346.41
95% C.I.
lower -295.73 -229.89
upper 142.88 44.16
p 0.4894 0.1828
Looking at the results it can be seen that true
difference in means is not equal to 0, but probability
p is higher than statistical significance. Thus we
need to accept the null hypothesis H
and reject the
alternative hypothesis H
Link between Sentiment and Human Activity Represented by Footsteps - Experiment Exploiting IoT Devices and Social Networks
5.1 Bias
We are fully aware that the experiment has several
biases. However, we considered them (and also the
options which we had at the time) when we had
designed the pilot experiment.
At first, walking is related to need to move
somewhere. When you do not plan to have an active
day, then it is hard to produce steps. Distribution of
activity during the day is not even.
Secondly, expression of sentiment is subjective
as well as its evaluation. The questions how to
express sentiment and extract it are discussed in
many publications cited in this paper. The subject
mostly described his activity and current feelings as
hate or pleasure.
Lastly, but not less importantly, we need to
consider conditions for tweet writing. If the aim was
to write 20 tweets a day, we had effectively 16 hours
of active day (assuming 8 hours of sleep). Thus the
subject was required to produce a tweet expressing
sentiment every 48 minutes. This was little bit pushy
and also affected content and even willingness to
express mood.
5.2 Corpora
Selection of a right corpus plays a significant role in
future analysis. As we can see in Section 3.3.2, there
was an observational difference in measures using
machine learning methods for two selected corpora.
Better results for the Twitter sentiment corpus
might be given by the fact that we also used tweets
in our experiment. The limit of 140 characters can
lead to short expressions of what subject wants to
say. As the result, the words in the Twitter corpus
could be more similar than the words in the Movie
review corpus.
Another fact is that whilst the Movie review
corpus contains both positive and negative values
equally, the Twitter sentiment corpus has 38%
tweets with negative sentiment and 62% tweets with
positive sentiment. This proportion is closer to the
distribution of sentiment classes in our experimental
corpus which is 20% tweets for negative sentiment
and 80% tweets for positive sentiment.
5.3 Hypothesis Testing Results
Summarizing all the results from Table 12 we can
see that in both cases we can accept the null
hypothesis H
: “Movement does not affect mood”
and reject the alternative hypothesis H
: “Movement
results in a positive mood”. We did not found a link
between the quantified value (the number of steps)
and the sentiment evaluated or extracted from the
text in this pilot experiment.
5.4 Result and Improvements
5.4.1 Result Summary
It is obvious that the result we got it is not
corresponding to what we expected, namely to
confirm the alternative hypothesis. There could be
many reasons for that as it is listed in previous
conclusion subchapters including the typical issue
with a relative small population for statistical
Nevertheless, we still see the potential to explore
the issues like data source suitability, machine
learning method evaluation, and exploratory data
analysis. Also with improved experiment design and
larger population sample we are more likely to reach
the goal.
5.4.2 Research Proposal Follow-ups
During the past 15 months when the pilot
experiment has been conducted, IoT devices suitable
for this kind of experiment increased their
capabilities and offer not only steps as a quantified
measure but also a continuously measured heart rate
with nearly ECG precision and more precise
algorithms for steps measurement.
We believe that the heart rate measured
independently of current human activity, but
together with steps and in the same timeline with
sentiment can give us better answers.
This work was supported by the UWB grant SGS-
2013-039 Methods and Applications of Bio- and
Medical Informatics and by the project LO1506 of
the Czech Ministry of Education, Youth and Sports.
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