1. Overview

Many people around me are fans of Game of Thrones, and last summer I heard a lot of complaints about the show’s ending. I think it would be very interesting to analyze the words of the characters.

Since this analysis is a bit long, I figured it would be better to break it down to two posts. This is the second half of the analysis, focusing on sentiment analysis and word cloud analysis, as well as some conclusions.

The analysis is made through R and the code is published in my github.

2. Sentiment analysis

It is also insightful to perform setiment analysis to check emotions within the lines. Two plots were made for each character. The first plot shows the top words contributing the most to positive and negative emotions, and the second plot shows the top 15 emotional words that contribute to sentiment. It is interesting to see that the word “love” is the top positive word appearred in the analysis results for all 4 characters. Additionally, it is worth noticing that in the second plot (Contribution to sentiment) for all four main characters, there are more negtive words contributing to sentiment than positive words, indicating a negative attitude in lines for the four characters. img4 img5 img6 img7

3. Word cloud

Last but not least, I created a word cloud for each character to visualize the emotional words in lines. We can see that on the negative side, “kill” and “die” are really eyes-catching, while “love” and “grace” pop up in the positive side.

-Tyrion Lannister img8 -Jon Snow img9 -Daenerys Targaryen img10 -Cersei Lannister img11

4. Conclusions

In this project, I used 5 ways to analyze the lines from Game of Thrones with NLP techniques. As a summary， the LDA and topic model do a good job in terms of representing the stories of each character. The sentiment analyses provide insights about the emotions of these characters. It is interesting to see that most of the results correspond to the background of the characters. This show mostly provides the audience with negative emotions, but I am happy to see that love is the most used positive word.

Reference

Julia Silge and David Robinson,”Text Mining with R”