New visualization tool makes climate texts easier to analyze at scale
Researchers have improved on the word cloud—a decades-old text analysis tool—by adding semantic positioning and multiple visual signals to reveal patterns in climate change documents. The upgrade could help policymakers, researchers, and companies quickly identify emerging themes and language shifts in climate communications, compliance documents, or media coverage.
Originaltitel: From Word Clouds to Word Rain: Revisiting the Classic Word Cloud to Visualize Climate Change Texts
<p>Word Rain is a development of the classic word cloud. It addresses some of the limitations of word clouds, in particular the lack of a semantically motivated positioning of the words, and the use of font size as a sole indicator of word prominence. Word Rain uses the semantic information encoded in a distributional semantics-based language model – reduced into one dimension – to position the words along the x-axis. Thereby, the horizontal positioning of the words reflects semantic similarity. Font size is still used to signal word prominence, but this signal is supplemented with a bar chart, as well as with the position of the words on the y-axis. We exemplify the use of Word Rain by three concrete visualization tasks, applied on different real-world texts and document collections on climate change. In these case studies, word2vec models, reduced to one dimension with t-SNE, are used to encode semantic similarity, and TF-IDF is used for measuring word prominence. We evaluate the technique further by carrying out domain expert reviews.</p>