Analyzing Public Sentiments on Gun Control through Twitter: A Machine Learning Approach

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Introduction
Violence related to gun has become a complex matter and it is accountable for a
massive populace of the violent incidents. For instance, in December 2012 a profoundly
armed young man having mental illness found right of entry to his mother’s lawfully
possessed guns and gunshot all the way into the protected apartment of the Sandy Hook
Elementary School (SHES) in Connecticut, USA (Benton et al., 2016). Certainly within a
short span of less than fifteen minutes the young man had killed approximately 26 people
consisting of six adults and twenty kids (Wang, Varghese, & Donnelly, 2016). Furthermore,
before the incident of SHES the young man had murdering his mother in the bed and lastly he
killed himself. As a result of this mass shooting that happened at Sandy Hook Elementary
School it led to a heated debate as well as legislation regarding gun regulation across the
entire United States. Accordingly, social media played a lively public debate with individuals
expressing different views both for and against gun legislation. Certainly, much focus
regarding this incident of mass shooting was primarily based on gun ownership and gun
control with people giving different views advocating for tougher gun control. Indeed, the
tougher gun control views has been in clash with the America Constitutional second
amendment advocating for the right to hold firearms.
On the same note while the public opinion has been on the front in fighting for
tougher firearm control rules for more than twenty years, the centralized gun control laws has
been a heatedly debated topic facing small legislative progress in which even the local
restraints have been met with obstruction. Basically, this debate is ongoing and in its totality
it is past the scope of this report restrict itself to looking forward to comprehend and interpret
pro-gun as well as anti-gun sentiments conveyed through social media stand particularly
Twitter. Indeed, since the birth of social media, microblogging has become a famous
communication component among users of the internet. The research make use of Twitter

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because it is a free and easily accessible microblogging service that tend to take over from the
traditional communication tools like mailing lists and blogs. Therefore, the daily upsurge in
the number of users keep on sending out millions of messages across the website which are
precisely created for the purpose Twitter, Facebook and Tumblr only to mention a few. The
content of these messages discusses a range of topics. Thus, social media has allowed users to
write their opinion about society, share views and divulge ideas. With the rise in users
creating content of different varieties on social media platforms such as marketing views,
political or religion has made microblogging an interesting source of opinion mining as well
as sentimental analysis. For instance in the case of marketing sentiment analysis it is capable
to determine if consumers like or dislike a given product. In the case of politics sentiment
analysis is used to collect information regarding political party activities such as whether the
party support or does not support a party’s political agenda.
So as to realise this the research paper make use of machine learning which
illustration more than 300,000 tweets made by people in the U.S. which comprise one or
more pre-set significant key words. Ideally, the target of this research is to attain the pro-gun
and anti-gun emotion and the manner in which it transformed over time. Consequently, the
researcher explore the viability of taking machine learning approach to sentimental analysis
as well as stance detection for political tweets. Accordingly, a machine learning system is
trained to deduct sentiment information from tweets that is a tweet which is formulated
neutrally, negatively or positively. Moreover, the machine learning is trained to analyse the
stance recognition that is when an individual is against, in favour of or neutral towards a
given subject matter and in this case gun control in the U.S (Vizzard, 2014). certainly, for
further description the researcher will expound more about Twitter, sentimental analysis and
lastly but not least stance discovery.
Twitter

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In this case the researcher make use of Twitter because it has various advantages
which makes it a significant tool for sentimental analysis. Indeed, twitter is a free online
social media platform where users are capable of sharing messages freely which are
commonly referred to as tweets (Stefanone et al., 2015). Every tweet is restricted to a specific
number of characters and before November 2017 twitter only allowed users to tweet a
maximum of 140 characters for each tweet. Nevertheless, after the update in November 2017
one tweet can vary in length from between one character and 280 characters. Although tweets
vary based on the discussion topic these tweets differ in various ways such as in terms of
content and writing depending the user’s background. The advantage is that microblogging
service Twitter is capable of collecting tweets from individuals with dissimilar cultural, social
and economic settings. On the same note, twitter users use different words with different
meanings thus the data has to be gathered in various languages (O’Brien, Forrest, Lynott, &
Daly, 2013). As a result, research has shown that microblogging services are not limited to
only one language but many languages because of the numerous people with different
nationalities in the U.S.
Similarly, the database of twitter keep on growing each day thus Twitter is an infinite
data resource. Accordingly, with regard to the number of characters of each tweet, the
gigantic audience as well as the ever-expanding database it leaves Twitter as the most ideal
microblogging service for gathering and analysing data for the purpose of gun control
sentimental analysis and stance recognition. In the context of gun control twitter sentimental
analysis the research will develop a structure to gather, pre-processes and categorise tweets
on gun control (Benton et al., 2016). Therefore, the researcher will use an indicator curated
gold normal dataset in categorising the whole research sample. The research will use different
machine learning strategies and then evaluate these approaches to offer maximum
accurateness over a trivial section to be used for categorising the whole tweet model. The