Twitter Sentiment Analysis: Machine Learning & Gun Control Debate

This report examines public sentiment surrounding gun control using machine learning techniques applied to Twitter data. It begins by introducing the complexities of gun violence and the ensuing debates, particularly in the wake of the Sandy Hook Elementary School shooting. The study leverages machine learning to analyze over 300,000 tweets, classifying them as positive, negative, or neutral to gauge public sentiment. The methodology involves data collection from Twitter via GNIP, data pre-processing, classification of tweets, summarization using Pro-Gun Public Sentiment Scores (PGSS), and data visualization through geographic maps and line graphs. Predictive models such as Random Forest, Support Vector Machine, and Maximum Entropy are employed. The results highlight key sentiments, distinguishing between neutral, pro-gun, and anti-gun stances. The report concludes by emphasizing the potential of social media sentiment analysis and the necessity for stricter gun control measures to prevent future tragedies.

Gun Control

Introduction
 Violence related to gun has become a complex matter and it is accountable for a massive
populace of the violent incidents.
 In December 2012 a heavily armed young man having mental illness found right of entry to his
mother’s lawfully possessed guns and gunshot his way into the protected apartment of the Sandy
Hook Elementary School (SHES) in Connecticut, USA.
 The young man had killed approximately 26 people consisting of six adults and twenty kids and
lastly shot himself.
 The mass shooting at Sandy Hook Elementary School led to a heated debate and legislation
regarding gun control across the entire United States.

Cont’
 The incident of mass shooting was primarily based on gun ownership and gun
control with people giving different views advocating for tougher gun control.
 However, the tougher gun control views has been in clash with the America
Constitutional second amendment advocating for the right to hold firearms.

Machine Learning for Sentimental
Analysis
 To realise this, the research paper make use of machine learning sentiment analysis.
 Machine learning illustrate more than 300, 000 tweets made by people in the
United Sates.
 A machine learning system is trained to deduct sentiment information from tweets
that is a tweet which is formulated neutrally, negatively or positively.
 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

Why Twitter
 The researcher use Twitter in data collection because it has various advantages
which makes it a significant tool for sentimental analysis.
 Twitter is a free online social media platform that allow users to share messages
freely what is commonly referred to as tweets
 Every tweet is restricted to a specific number of characters.
 Microblogging service Twitter is capable of collecting tweets from individuals
with dissimilar cultural, social and economic settings.

Cont’
 The database of twitter keep on growing each day thus Twitter is an infinite data
resource.
 Twitter is the most ideal microblogging service for gathering and analysing data.
 For the purpose of gun control sentimental analysis and stance recognition Twitter
is the most appropriate for sentiment analysis.

Research questions
1. In the case of this research the researcher will use the following
questions:
2. What is the yearly trend on tweets about gun control?
3. At what frequency does the word “gun control” appear in the tweet over
time?
4. What is the comparison of tweets for pro-gun and anti-gun with and
without emoticons and polarity?

Hypothesis
 H0: The tweet sentiment analysis packages such as TextBlog, nltk, and Scikit
Learn are effective approaches for polarity and subjective analysis.

Methodology
 The procedure for analysing public sentiments about gun control involves various processes by
incorporating machine learning through the following approach:
 data collection
 pre-processes
 Classification
 summaries and
 data visualization.

Data collection
 The raw tweet data is in JSON format is bought from GNIP which is an authorized
third party that resells historical tweets determined by predetermined standards.
 GNIP filter data according to the period, key words as well as the user’s
geographical location.
 In comparison with other alternatives like tradition built web flatterers and
programming the Twitter Streaming API.
 This method is suitable in the sense that it saves time and also offer data in a
consistent structure despite the fact that the tweets have to be purchased.

Pre-processing
 The researcher utilize a file comprising a list of JSON files as well as their local
settings which was found from the GNIP reseller.
 Every JSON file is arranged for a period of ten minutes which covers a huge
amount of data about the gun control tweets.
 The JSON files are analysed to mine the most significant data for the study which
is later on converted into the comma-separated values (CSV) format.

Classification
 At the machine learning phase, the feelings of every tweet is extracted.
 The preparation set of data consists of 5000 tweets which are designated from the
clipped data whereas the outstanding tweets which are remaining for grouping.
 The views of every tweet is physically labelled using three categories that is
neutral, pro-gun, and anti-gun to gain a gold standard set of data.