Mayor, E., Bietti, L.M. & Canales-Rodríguez, E.J. (2022) Text as signal. A tutorial with case studies focusing on social media (Twitter). Behavior Research Methods. https://doi.org/10.3758/s13428-022-01917-1
Abstract. Sentiment
analysis is the automated coding of emotions expressed in text. Sentiment
analysis and other types of analyses focusing on the automatic coding of
textual documents are increasingly popular in psychology and computer science.
However, the potential of treating automatically coded text collected with
regular sampling intervals as a signal is currently overlooked. We use the
phrase "text as signal" to refer to the application of signal
processing techniques to coded textual documents sampled with regularity. In
order to illustrate the potential of treating text as signal, we introduce the
reader to a variety of such techniques in a tutorial with two case studies in
the realm of social media analysis. First, we apply finite response impulse
filtering to emotion-coded tweets posted during the US Election Week of 2020
and discuss the visualization of the resulting variation in the filtered
signal. We use changepoint detection to highlight the important changes in the
emotional signals. Then we examine data interpolation, analysis of periodicity
via the fast Fourier transform (FFT), and FFT filtering to personal value-coded
tweets from November 2019 to October 2020 and link the variation in the
filtered signal to some of the epoch-defining events occurring during this
period. Finally, we use block bootstrapping to estimate the
variability/uncertainty in the resulting filtered signals. After working
through the tutorial, the readers will understand the basics of signal
processing to analyze regularly sampled coded text.
