Prepared for presentation at the MPSA Annual Meeting, April 13th , 2023.
Karl Ho |
University of Texas at Dallas |
Hyperpartisan_DPP | Hyperpartisan_KMT | |
---|---|---|
Chi2 (df=3) | 14.738 | 11.206 |
p | 0.002 | 0.011 |
RMSEA | 0.019 | 0.016 |
CFI | 0.999 | 0.999 |
Michael Kang (Boston College Law Review 2020):
two cohesive and hostile camps
voter animosity
ideologically polarized
[the other party is] “so misguided that it threatens the nation’s well-being”
the out-party surpasses racial prejudice
[one camp is] fearful of the other side
antagonistic
internally unified teams with clear, contrary positions
rig the rules of the game in their favor and gouge the other party in [an] outrageous fashion.
Strong or superlatively partisan attachment
Hyperpartisans are usually strong political party supporters who identify as close or very close to one political party.
Exclusivity
Hyperpartisans are highly exclusive of the identified rival, opposite party or camp.
Hostility
Hyperpartisans treat oppositive party or camp with high level of hostility with the often use of the verbs of “hate”, “despise”.
Apprehension
Hyperpartisans consider other rival party or camp as threatening and associate it with a negative or highly negative labels
The subjectivity score is a measure of how subjective or objective a piece of text is.
It ranges from 0 to 1, with 0 being completely objective and 1 being completely subjective. The score is calculated by counting the number of words in the text that are found in a pre-defined list of subjective and objective words (Bing 2012, Poria et al 2017).
The more subjective words in the text, the higher the subjectivity score will be.
The polarity score is a measure of the emotional sentiment expressed in a piece of text.
It ranges from -1 to 1, with -1 being very negative, 0 being neutral, and 1 being very positive.
The score is calculated by summing the polarity scores of individual words in the text, which are determined based on their presence in a pre-defined list of words with positive and negative sentiment.
The more positive words in the text, the higher the polarity score will be, and vice versa for negative words.
Author(s) | count |
---|---|
Bill O'Reilly and Martin Dugard | 62 |
Laura Hillenbrand | 46 |
Malala Yousafzai with Christina Lamb | 27 |
Chris Kyle with Scott McEwen and Jim DeFelice | 21 |
Ta-Nehisi Coates | 17 |
Michelle Alexander | 16 |
Bryan Stevenson | 13 |
Mark Owen with Kevin Maurer | 11 |
Katherine Boo | 10 |
Marcus Luttrell with Patrick Robinson | 10 |
Liu, B. 2012. "Sentiment Analysis and Opinion Mining." Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Poria, S., Cambria, E., Hazarika, D., Mazumder, N., & Zadeh, A. 2017. "A review of affective computing: From unimodal analysis to multimodal fusion". Information Fusion, 37, 98-125. https://doi.org/10.1016/j.inffus.2017.02.003
Bird, S., Klein, E., & Loper, E. 2009. Natural Language Processing with Python. O'Reilly Media, Inc.