The Boyfriend

Quantitative

Methodology

For our research we first wanted to use blogs from Japanese authors and use a sentiment analysis on their opinion to see if the Japanese public reacted in a positive or negative way to the first gay Japanese dating show. We realised that this method wasn’t going to work so to find our results, we looked for videos on youtube about the show “The Boyfriend”, which we found on the account of Netflix Japan. We asked chat gpt what do we need to scrape the comments on these videos? For this we needed to acquire an API key, python and PIP. We also asked chat gpt to write a python-script for us so we could scrape the youtube comments in python. This scraping didn’t work on macbook so we only used windows. Afterwards we put the scraped comments in an excel document in order to open it in openrefine so we could split them into multiple columns and clean. After putting it into openrefine we exported everything into an excel document again so it was easy to make a plain text out of it which we put into MeCab to parse the Japanese comments. MeCab didn’t work on windows so for this we used macbook. After parsing the comments we put this plain text back into an excel document in order to split and to clean with openrefine. Then we took the words that are 形容動詞語幹, which are the stems of adjectival nouns, and also adjectives out of openrefine to put it back into excel. We first wanted to use python for our sentimental analyses, but this didn’t work and openrefine also didn’t work, excel also wasn’t possible because it doesn’t support Japanese.

So instead of doing a sentiment analysis, we changed our original plan at the last minute of class and decided to do a frequency analysis on the words we would have used for the sentiment analysis. We used openrefine to give us the frequency of the words and picked out the 30 most used words that still had some purpose for the previous sentiment analysis. We then copied this from the text facet in openrefine and pasted it in excel. In excel we put it in order from highest to lowest number. then selected the cells and created a bar chart.

Conclusion

The bar chart shows us which adjectives are used the most in the comments. So although we were not able to do a full sentiment analysis, through this frequency analysis, we can still draw a conclusion on our research question, how the first gay dating show was received by the Japanese public. Most of the comments are positive, especially the 9 most used words are all very positive. Even though there are adjectives with negative connotations, they are a minority and not so frequently used. Purely from the results of the frequency analysis, we can conclude that the Japanese citizens commenting under these videos on youtube like the dating show named “The Boyfriend” on Netflix.