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Thanks. I worry that part of the problem with online social network analysis is that, as an example, tweets are often grammatically incorrect because of constraints on text length. I hope to learn if the latter is or is not an issue when I take the Caltech and IBM course.
I just read from [A] in the last hours. Linguistics is so fascinating. As Professor Price explains, corpus linguistic analysis removes a lot of the biases that, as an example, Likert scales will create. It seems to be very powerful.
Part of my desire is to mine online social networks to develop keywords that are more representative of the mind and attitude of the population. Questionnaires and Likert analysis seem to be less accurate when developing keywords. I don't know if AI can do this currently. I know corpus linguistics software can.
References:
[A] Price, H. (2022). The Language of Mental Illness: Corpus Linguistics and the Construction of Mental Illness in the Press. United Kingdom: Cambridge University Press. [edited by author]
Hi. I have found from my reading about ecolinguistics that they use certain 'positions' as indications of specific ecosophies, and in ecocriticism images are used. I am not sure how that is going to translate into keywords. I am looking more at phrases perhaps. I found Aron Stibbe wrote a lot about Ecolingistics and Greg Garrard wrote a book on Ecocriticism. Maybe their work can help you a bit. I would like to see how you manage to extract useful information from social media, because I couldn't figure out how to do it ):
I can't help you right now, but I will ask your question about collecting phrases when I attend the Caltech and IBM bootcamp.
Also, this is a book that I plan to read in the future[A]. I will remember you as I read. I just ask that you phrase your question here so that I can record it.
References:
[A] Data Mining Approaches for Big Data and Sentiment Analysis in Social Media. (2021). United States: IGI Global.