Several tests were necessary to calibrate the queries, which seem to perform poorly when a # is used. A watch of the trends posted on Twitter also led to the addition of the keywords ARN and mRNA, since the corpus analysis will be carried out in French and English.
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| corpus_vac <- search_tweets( | |
| q = "vaccination OR vaccine OR ARN OR mRNA OR bootser", retryonratelimit = TRUE, geocode = "52.897449,4.753000,850mi", type="mixed", include_rts = FALSE | |
| ) | |
| corpus_pass2 <- search_tweets( | |
| q = "covid OR sanitary AND pass OR \"safe ticket\"", retryonratelimit = TRUE, geocode = "52.897449,4.753000,850mi", type="mixed", include_rts = FALSE | |
| ) | |
| corpus_protest <- search_tweets( | |
| q = "anti-pass OR coronaprotest OR covid AND manifestation OR sanitary AND pass OR covid AND manifestation", retryonratelimit = TRUE, geocode = "52.897449,4.753000,850mi", type="mixed", include_rts = FALSE | |
| ) | |
| corpus_gen <- search_tweets( | |
| q = "vaccination OR vaccine OR anti-pass OR coronaprotest OR MRNa OR ARN OR Pfizer OR Moderna", retryonratelimit = TRUE, type="mixed", include_rts = FALSE | |
| ) |
Each retrieved dataset is first cleaned with Open Refine: column mergers are sometimes necessary because the “text” column is sometimes split into several columns (recording with comma separator). The three corpora with a defined geographical area present fewer quality problems than the general corpus, which targets all directions: big data does not necessarily mean good data.
Academic readings:
- Deng, S., Sinha, A. P., & Zhao, H. (2017). Adapting sentiment lexicons to domain-specific social media texts. Decision Support Systems, 94, 65-76.
- Mowlaei, M. E., Abadeh, M. S., & Keshavarz, H. (2020). Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Systems with Applications, 148, 113234.