Hi, I am quite a newbie with python, and I need to run some text mining analysis on 100+ literary texts in German, which I have stored as individual txt files in a folder. They are with the scheme author_title_date (for example "schnitzler_else_1924.txt").
I was thinking of using the python package nltk and/or spaCy, and maybe the Stanford NER, as I need to analyse sentiments in the different texts and to identify specific locations as well as the sentiments in relation to such locations.
I am stuck on a very preliminary passage though: how do I import the all the text files from the folder to a single corpus/vector corpus that retains the metadata in the title? I could relatively easily produce that in R with TM, but I can't find a way to do it in python. Thanks!
I suggest you follow https://github.com/umer7/Applied-Text-Mining-in-Python
https://github.com/agniiyer/Applied-Text-Mining-in-Python
IN "R" : https://onlinelibrary.wiley.com/doi/book/10.1002/9781119282105
Via MATLAB : https://au.mathworks.com/help/textanalytics/getting-started-with-text-analytics-toolbox.html
You can use python OS library to read each single text file ( make sure you use proper encoding) and store it into a single csv or suitable format. Then you can apply your algorithm on that single file.
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