Social Media Analysis is a very interesting topic. As an active
user, who spend(#or waste) at least one hour (#very conservative estimation) on SNSs, sometimes I would like to
review what I have done and learn about how I behavior at SNSs.
And I found some apps. The first one is “Mydata” on Weibo, which
could tell me male-to-female ratio and the astrological sign distribution of my
followers. (#OMG, so many virgos!)
The second is “FrienDoc” on Renren, which tells me the users who “care”
about me most, or interact with me most frequently.
The last one is developed by Natural Language Processing and
Computational Social Science Lab, Tsinghua University, named “Key Words”. You
can find this app in many different SNSs. It abstract the key words from the
content I posted, sort them by how frequent those words appear, and visualize
the result. The key word appears for the most of number would be in the middle
of the picture and would have bigger size than any other keywords.
All those three apps, however, are only focus on one user’s
content and behavior. Analyzing social media would more complex and would get
more valueable results.
This course also give me an opportunity as well as motivation to
learn Python, the powerful coding language that we will use for this course’s
projects. I have a good feeling of this language when I heard about it for the
first time. It’s not because I am a geek or a coding lover, but I really like
Monty Python’s comedy movies. (# XD )
I started to learn Python above one week ago. It’s seems that
Python is not as complex as the languages I am more familiar with, such as C
and Java. As far as I am concerned, by using Python, coders could spend more time and energy to solve the
really problem, instead of solving syntax or data structure problems. Python
would be a relatively mature language for social media analyzing. I hope I will
have many interesting discovers by mining data with Python.
Now it’s 21 September 23:40. As the DDL is approaching, this would
be the end of my first post. Next time I may do some more sericous work.
Thank you for reading. Thank you for your kindly comments. 多謝你哋！唔該晒！
You have written a rather interesting article. You are using lots of SNNs. Mydata, FrienDoc and Key Words definitely play a role on telling or reminding something we want to know. Obviously, you express your passion in term of Python, is is really because of the movies of Monty Python's? If I have trouble learning Python, I need your help.
回复删除Hi Johnny. Thanks for all your help in this course. I'm really enjoying studying with you. I supposed that you are really interested in the social media. Besides the weibo and wechat, you must be addicted in Zhihu. Let's talk about Zhihu and why it is more valuable. Grouping people with the same interests or topic is a effecient way of communication. We met professional knowledge and insights in the discentralized community and share efficiently. Maybe I talk too much. I have a report about how Zhihu operates. If you are interested in, I can share with you.
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