Sentiment and structure in word co-occurrence networks on Twitter

Mikaela Irene Fudolig,Thayer Alshaabi,Christopher M Danforth,Peter Sheridan Dodds,Michael V Arnold

doi:10.1007/s41109-022-00446-2

Mikaela Irene Fudolig, Thayer Alshaabi + Show 3 more

Open Access

https://doi.org/10.1007/s41109-022-00446-2

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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.

Highlights

Large-scale analysis of user-generated text has been instrumental in understanding recent political campaigns and movements, from the Arab Spring (Howard et al 2011; Wolfsfeld et al 2013) to the Black Lives Matter protests (Wu et al 2021)
This paradigm of studying collective sentiment has been used to monitor general sentiment on Twitter, and has been validated by cross-referencing changes in measured sentiment with high-profile events, such as election results, terrorist activities, holidays and even birthdays of popular artists (Dodds et al 2011). Holidays such as Christmas, New Year, Fourth of July and Thanksgiving correspond to spikes in happiness, while mass shootings in the United States, the fire at Notre Dame cathedral, the murder of George Floyd and the storming of the US Capitol coincide with a marked decrease in happiness on Twitter. In contrast to this approach, which looks at social media posts as a bag of words, we look at it as a collection of tweets that are in turn related by their use of specific words
From the word co-occurrence network, we examined how the co-occurrence structure of words in political tweets is related to the word happiness scores

Summary

Introduction

Large-scale analysis of user-generated text has been instrumental in understanding recent political campaigns and movements, from the Arab Spring (Howard et al 2011; Wolfsfeld et al 2013) to the Black Lives Matter protests (Wu et al 2021). Unlike surveys where questions are carefully constructed so that the answers can be directly interpreted, extracting sentiment from large-scale unstructured text requires automated interpretation. The field of sentiment analysis in natural language processing (NLP) developed in response to this need, not just in politics but in other areas as well. Retailers have used sentiment analysis techniques to analyze product reviews and provide a better experience for its users (Fang and Zhan 2015; Shivaprasad and Shetty 2017). Sentiment analysis has been applied to financial markets, where the opinion of market participants plays an important role in future prices (Smailovic et al 2013; Pagolu et al 2016; Mishev et al 2020)

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