Sentiment and structure in word co-occurrence networks on Twitter
- URL: http://arxiv.org/abs/2110.00587v1
- Date: Fri, 1 Oct 2021 18:00:02 GMT
- Title: Sentiment and structure in word co-occurrence networks on Twitter
- Authors: Mikaela Irene Fudolig, Thayer Alshaabi, Michael V. Arnold, Christopher
M. Danforth, Peter Sheridan Dodds
- Abstract summary: We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks.
neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words.
Although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus.
- Score: 1.181206257787103
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We explore the relationship between context and happiness scores in political
tweets using word co-occurrence networks, where nodes in the network are the
words, and the weight of an edge is the number of tweets in the corpus for
which the two connected words co-occur. In particular, we consider tweets with
hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton's
presidential bid in 2016. We then analyze the network properties in conjunction
with the word scores by comparing with null models to separate the effects of
the network structure and the score distribution. Neutral words are found to be
dominant and most words, regardless of polarity, tend to co-occur with neutral
words. We do not observe any score homophily among positive and negative words.
However, when we perform network backboning, community detection results in
word groupings with meaningful narratives, and the happiness scores of the
words in each group correspond to its respective theme. Thus, although we
observe no clear relationship between happiness scores and co-occurrence at the
node or edge level, a community-centric approach can isolate themes of
competing sentiments in a corpus.
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