| TF-IDF | Identify words of high frequency and importance. | Jung, 2010 |
| Centrality | Closeness centrality | Identify which words are located at the center of the network or are highly related to other words. | Kim and Song, 2016 |
| Harmony centrality | Similar to closeness centrality, but to analyze words by averaging the shortest distance reciprocals between them. | Kim, 2019 |
| Betweenness centrality | Identify which words act as links between words, meaning influential words on the network. | Marsden, 2002 |
| Degree centrality | Analyze the topic words associated with a particular word to determine which words are centered in the network. | Friedkin, 1993 |
| Network | Identify the words that appear at the same time as a particular word. | Kim and Lee, 2021 |
| Topic modeling | Identify the implications of the words in each cluster by grouping them into clusters. | Kim et al., 2016b |