Social Media Analysis using Natural Language Processing Techniques
Social media is very popularly used every day with daily content viewing
and/or posting that in turn influences people around this world in a variety
of ways. Social media platforms, such as YouTube, have a lot of activity that
goes on every day in terms of video posting, watching and commenting. While
we can open the YouTube app on our phones and look at videos and what people
are commenting, it only gives us a limited view as to kind of things others
around us care about and what is trending amongst other consumers of our
favorite topics or videos. Crawling some of this raw data and performing
analysis on it using Natural Language Processing (NLP) can be tricky given
the different styles of language usage by people in today’s world. This effort
highlights the YouTube’s open Data API and how to use it in python to get the
raw data, data cleaning using NLP tricks and Machine Learning in python for social
media interactions, and extraction of trends and key influential factors from
this data in an automated fashion. All these steps towards trend analysis are
discussed and demonstrated with examples that use different open-source
python tools.
nlp, natural language processing, social media data, youtube, named entitity recognition, ner, keyphrase extraction
DOI10.25080/majora-1b6fd038-009