What is Omikuji?
If you visit shrines and temples in Asia, you may often see people praying for good wishes and taking Omikuji (fortunes written on paper strips) from boxes or even coin-slot machines. The Omikuji predicts your fortunes in health, work, study, marriage, etc. There are many kinds of words written on Omikuji to describe fortunes, and I am interested in the method of using classical Japanese poetry (waka) as divination.
I decided to run some fortune-telling poems with Voyant to see the results. The Omikuji strips are usually rolled up and folded; you will need to unroll them to see the result. Before you read the fortune-telling poems, you will see a general indicator to tell you if you are lucky today. Among many categorization methods, the examples I am using are divided as follows,
- Dai-kichi大吉 (excellent luck)
- Kichi吉 (good luck)
- Chu-kichi中吉 (moderate luck)
- Sho-kichi小吉 (a little bit of luck)
- Sue-kichi末吉 (bad luck; good luck yet to come)
I retrieved the data from the Omikuji-joshidosya website. It is said that 70% of current Omikuji strips in Japan are made by the Nishoyamada Shrine, where the Organization Joshidosha (Women’s Road Company) locates.
My first attempt was a total disappointment. See the Figure 4,
The high-frequency words that appeared in Cirrus, TermsBerry, and Trends are single hiragana characters instead of objects’ names and verbs. These words are similar to determiners and prepositions (stopwords in Voyant) in English (the, a, in, to, form, etc.). I then also realized that stopwords are not the only problem in analyzing Japanese text. Text segmentation is also different in Japanese: this issue is already super complicated in modern Japanese, not to mention that the poems in my mini-project are written in classical Japanese. So I tried to refer to the article “Basic Python for Japanese Studies: Using fugashi for Text Segmentation” and see if I could reframe the textual structure of my text for Voyant. For example, I could clean my text before uploading it to Voyant by removing auxiliary verbs, particles, suffixes, prefixes, etc. I also learned about a more manageable solution about Japanese version of stopwords from Japanese DH scholar Nagasaki Kiyonori in his post.
Inspired by Nagasaki Kiyonori, I started to create a stopword list by myself. (Figure 5) The default setting of the stopword list in Voyant Japanese mode is based on modern Japanese. See some examples here,
あそこ あの あのかた あの人 あります あれ います え おります から
何 彼 彼女 我々 私 私達 貴方 貴方方
Unlike modern Japanese or Japanese in the Meiji period (1868–1912), auxiliary verbs and particles are almost used in a completely different system in classical Japanese. See some examples in my stopword list here,
が て して で つつ つ ぬ たり り き けり む
But I am glad I chose poetry to do the Voyant experiment because the waka poetry has a relatively easier text segmentation method: one poem always breaks into phrases of 5/7/5/7/7.
Example: 朝日かげ たゞさす庭の 松が枝に 千代よぶ鶴の こえののどけさ
Asahikage (5) tadasasuniwano (7) matsugaeni (5) chiyoyobutsuruno (7) koenonodokesa (7)
Research Questions and Result
Okay, now we have a feasible approach! The next question is about the purpose of this analysis. Should I do a full-text analysis, or should I do several studies with questions that could be asked about those poems? For example, what seasons and figurative language are chosen for good luck and bad luck respectively?
I decided to do a comparison of imagery/actions used in the excellent luck group and the bad luck group. See the number of poems in each group:
- Dai-kichi大吉 (excellent luck) 17
- Kichi吉 (good luck) 6
- Chu-kichi中吉 (moderate luck) 7
- Sho-kichi小吉 (a little bit of luck) 9
- Sue-kichi末吉 (bad luck; good luck yet to come) 11
The result of Dai-kichi大吉 (excellent luck)
The result of Sue-kichi 末吉 (bad luck)
The keywords mentioned above have already shown us a sharp comparison between what the creators believe as good luck and bad luck. I am very satisfied with the result, even though I know there are a lot to be improved. I also went to try TermsBerry and Trends in Voyant and realized that I can do further studies using these features. For example although the keyword “flower” and “shadow” both appear in two groups, what associations they have that make them different in good and back luck groups? The example in Figure 8 shows a clear association between flower, sakura (both in hiragana and kanji characters), and peach flower,
The Getting Started with Text Mining is very helpful. I started my mini-project without big data but with the idea that I need to prepare my data (cleaning and removing). If I want to use Voyant to do deeper and larger scale analysis of poems and classical Japanese texts, it definitely requires a huge preparation work. For example, I believe if I do more stopwords considering conjugations, the result probably will be more accurate. I think this tool is great for learning intertextuality and imagery in poetry writing.
There are also Sinitic poetry (kanshi 漢詩) fortune-telling Omikuji! Oh, that would be in a totally different linguist structure, but worth a try next time.
This is so beautiful Miaoling! I really appreciate how you explain how to clean your “data” regarding poems and your conclusion about intertextuality. I wonder how would it look like to compare text from different languages that have common themes. Like what’s the word choice or horizon to described or narrate bad/good luck as expressed in other languages and then compared to the Omikuji.