Analyzing tweets nvivo 1012/28/2022 Parse_date_time(orders = '%a %b %d %H%M%S %Y') Note that in the raw file it has type character. We parse the Created_At column into a date format. # $ Text "#HappySunday #FelizDomingo #DomingoDeGanarSeguidores #Si… The analysis is done in R and it is mainly motivated by the techniques presented in the book Text Mining with R.įilter(!str_detect(string = Text, pattern = %>% ![]() In a previous post I described how to get (scraping) the referendum results data per town. You can find more information about it here. The topic I chose to run the analysis is the Colombian peace agreement referendum (Plebiscito), celebrated on. In particular, I describe how networks (graphs) can be used as data structures to describe text relations (some measure of pairwise count occurrences). The emphasis of this post is in the data manipulation and data visualization. The aim is not to give a complete analysis (as it would require many interations), but rather to describe how to to start. ![]() In this post I want to present a small case study where I analyze Twitter text data. In addition, as a fundamental component of the analysis, it is important to find ways of communicating the results, i.e. data visualization. It is a very interesting challenge to discover techniques to get insights on the content and development of social media data. Nowadays social media generates a vast amount of raw data (text, images, videos, etc).
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