  Regressions trained to predict the future activity of social media users need
rich features for accurate predictions. Many advanced models exist to generate
such features; however, the time complexities of their computations are often
prohibitive when they run on enormous data-sets. Some studies have shown that
simple semantic network features can be rich enough to use for regressions
without requiring complex computations. We propose a method for using semantic
networks as user-level features for machine learning tasks. We conducted an
experiment using a semantic network of 1037 Twitter hashtags from a corpus of
3.7 million tweets related to the 2022 French presidential election. A
bipartite graph is formed where hashtags are nodes and weighted edges connect
the hashtags reflecting the number of Twitter users that interacted with both
hashtags. The graph is then transformed into a maximum-spanning tree with the
most popular hashtag as its root node to construct a hierarchy amongst the
hashtags. We then provide a vector feature for each user based on this tree. To
validate the usefulness of our semantic feature we performed a regression
experiment to predict the response rate of each user with six emotions like
anger, enjoyment, or disgust. Our semantic feature performs well with the
regression with most emotions having $R^2$ above 0.5. These results suggest
that our semantic feature could be considered for use in further experiments
predicting social media response on big data-sets.
