Sentiment and structure in word co-occurrence networks on Twitter

TL;DR

This paper investigates how word co-occurrence networks on Twitter relate to sentiment and thematic structures, revealing that community detection can uncover meaningful sentiment themes despite no direct correlation between individual word scores and co-occurrence patterns.

Contribution

It introduces a community-based analysis of sentiment themes in Twitter word networks, demonstrating the effectiveness of network backboning and community detection in thematic sentiment analysis.

Findings

Neutral words dominate the networks.

No homophily observed between positive and negative words.

Community detection reveals meaningful sentiment themes.

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.