hey guys
today we are going to present our research paper
11 years with wearables
quantitative analysis of social media academia
news agencies and lead user community from 2019 to 2020
on wearable technologies
my name is Tianyi Wang, I'm the second author of this study
this is the research study
we have done during our
master degrees at Boston University
before we start
Yanglei and I want to thank Reza
our professor at Boston University and all
other authors for their contributions and guidance
in this work
here's how we are going to approach today's presentation
first we will talk about the reason
why we conduct such a research
then we will go over our data
including its originality
and the way we collect and process the data
next we talk about our experience
followed by our findings
first, why we want to do such a research
wearable technology industry goes fast
this is a new type of
human computer interaction paradigms
which use computing power
to drastically improve the user's perception
of the environment
and add an acquisition of their status
and thus play an irreplaceable role
in improving user quality of life
these technologies
cover a wide range of electronics categories
including devices intended to be worn as clothing
accessories or even implanted into the user's body
here's a screenshot from our paper
it shows an operating train
for wearable technology mentioned in the sector of academia
the same train applies to the rest three sectors
next we will give a brief description of the data
that we used
for our research
we gather data from
diverse fields in a web technology market
in order to understand
different opportunities in the industry
such as academia
we get our data from PubMed and DBLP
which represents the academic community
for social media
Twitter data is used to represent the voice of end users
Google news and Bing news for news media
last but not the least
we collect data from Indiegogo and Kickstarter
to represent the entrepreneurship community
How we collect our data?
in total a set of 42 keywords was
identified from different categories of wearable technology
such as Augmented Reality
Body Gadget and Brain-Computer Interface
next unrelated keywords were removed
the identified keywords were used
a crawling API to construct the data set
with data from Google news
Bing news Twitter and Kickstarter
furthermore Indiegogo data provided by
Web Robots and academic publications
extracted from DBLP and PubMed
were cleaned using python
each entry in the cleaned dataset featured
the content of the
post in the text format
its creation time stamp and the platform which
from it was collected
here is the glimpse of our data
on the left is the distribution of
keywords in both DBLP and PubMed
on the right is the keywords counts for all
four communities
in the wearable technology industry
as you can see that
academia is pretty
focused on several domains in wearable technology
such as xr and exoskeleton
next
we will talk about the methods we use in our research
why we choose to use LDA
Unlike clustering algorithms
LDA did not incorporate any distance
matrix to measure distances
between topics
instead it assigned each document a mixture of topics
and each topic
was assigned a specific weight
the topic with the greatest weight
was set as the featured topic for each text
Here is a screenshot of our results
Here we go over the model briefly
our input is texture data mentioned before
we pre-process all the textual data
into a form which is suitable for BERT
all these texture data are sent
into a pre-trianed BERT model provided by Huggingface
followed by a two-layer
bidirectional Gated Recurrent Unit
to get a sentiment score in the range of zero to one
the higher sentiment score
the more positive the attitude toward
the product mentioned in the text
we grouped those sentiment scores by keywords and
by topic
here's how the sentiment analysis result looks
we present
sentiment analysis results based on LDA topic
modeling and
the 36 listed
keywords across all six platforms
to help our readers to better
understand how each sector reacts to
different weather topics
we make one heatmap for each sector by ear
the dark the color
the highest intimate score for that topic
What do we find?
XR was mentioned the most
in three sectors of the four
the negative attitudes of Academia
towards extra demonstrate that this demand has
not yet fully matured
and that its application
still have room for improvement specifically
regards to battery challenges
Brain Computer Interrface has been continuously researched
in academia and appears to be mentioned in recent years
by end-users
and news media
so this might be some opportunities there
according to our LDA model results considerably
portion of the social media and news media data
mentioned the topic fashion
we believe that the consumer market not
only wants a product with practically
reliability and functionality
they also demand a product which
represent personality and style
as we can see from the headman
a "hyped" wearable technology received user favor
for about three years
news media were overwhelmed positive
about XR in a three-year period
from 2014-2016
even with the positive attitude of the entrepreneurship
community and some sectors of news media
social media did not overly sought
after wearable technologies, at least in the last two years
Wearable technologies can have
advanced protection for health care workers
by enhanced operational
performance and efficiency
by continuously monitor user body temperature
in spite
of the active promotion of the entrepreneurship
community and the news media
social media users appeared to be conservative
about exoskeleton products
with this topic
receiving the least interest among all
wearable topics mentioned on Twitter
according to academic data from
2019-2020 in the fields of
computer science and biomedical science
the overall number of publications
in biomedical science
which include our
keywords was greater than in the computer science
we found that these two fields of academia
both focused on three major topics of wearable technology industry
including xr
wrist-worn and exoskeleton
thank you for your time and listen to our presentation
we hope our findings are helpful for your construction
your next research idea