When I look back to the whole semester learning, feeling like getting into a brand new world. The overall learning process could be roughly concluded in Fig.1. When we start this course, we firstly should have some senses, including the basic concepts and definitions. At the meantime, blogging and commenting to other classmates could not only enhance our understanding to the concepts and open our horizon, but also bond us tightly and made the data we need later. After grabbing enough basic knowledge, we came into the two major parts of this journey: sentiment analysis and social networks analysis. With the help of programming tasks, we get more familiar with theses analysis ways in real life.
After visiting and commenting each other’s blogs, the programme assignment 1 was built on the sentiment analysis of received comments. Based on the provide positive and negative dictionaries, dealing with the cleaned data, a sentiment score turned out.
According to the above function, my final sentiment score is 0.03328290468986384, which is a little bit larger than 0, suggesting the sentiment of received comments is prone to be neutral. With the respect to the collected negative and positive words in dictionaries, however, the final score may have some bias. The negative words were nearly not about their attitudes towards to the blog itself, but their reviews of the social reality in blogs. In the comments, sometimes we not only talk about the blog content or structure, but also share other different aspects or truth.
In the assignment 2, according to the social matrix that TA provided, undirect graph was made and we could get four important indexes: Degree Centrality (IN & OUT), Betweenness Centrality, Closeness Centrality. After running the programme, my results are showed in the following:
The high In-degree means there are several different classmates coming to visit my blogs and leave their comments, suggesting the content of my blogs could be, to some extent, interesting and attractive. Out-degree as 13 means I’ve commented 13 different classmates, implying I could probably be an outgoing or social girl. The closeness centrality as 0.5 could a nice result, because it could slightly prove the distance of me and other nodes are not quite long, in other words, I’m close to the other classmates. Besides, the betweenness centrality measures how important a node is to the shortest paths through the network  and I’m not sure my result is the best.
During this one-semester learning experience, I’ve met and made some great friends, even though we might could only talk through the Internet. I will always treasure this precious time and love u all. Hope u could remember me.❤
 IEMS5720 Social Networking Lecture 7,CUHK, p55,2020.
 Betweenness centrality. Retrieved from https://www.sciencedirect.com/topics/computer-science/betweenness-centrality
 IEMS5720 Social Networking Lecture 12,CUHK, p48,2020.