1. Introduction

Submit the respective source{code and outputs per question.

1.1     Modeling Twitter Sentiment (15 points)

Hedonometer¹ is a resource that quantifies twitter sentiment as a numeric value capable of being classified as positive, negative, or neutral. Formed from a collaboration between MITRE Corporation and University of Vermont, Hedonometer allows you to ultimately get a daily \pulse” of the twitter universe { a snapshot. With such a resource, you can investigate the transition of sentiment from one day to the next.

Hedonometer is free to download from http://hedonometer.org/data/word-vectors/vacc/sumhapps.csv. Given this data, investigate Hedonometer values for year 2018 and come{up with numeric thresholds that map a value to a positive, negative, or neutral classification. Next, build a Markov Chain that models the label transition from one day to the next. Show the produced transition probability matrix, X and initial probabilities, .

1.2     Markov Chains and Steady – State (5 points)

Given X and below, show that this Markov Chain does not converge to a steady{state. Show modifications you would make to ensure this Markov Chain shall reach steady{state:

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