Analysis of perspectives in contemporary Japanese novels using computational stylistic methods

paper, specified "short paper"
  1. 1. Takafumi Suzuki

    Toyo University

  2. 2. Natsumi Yamashita

    Toyo University

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1. Introduction

Perspective in novels has been an important subject of research in literary studies. Ishimaru (1985) defined perspectives as the viewpoint of narrators; she roughly classified perspectives in novels as the first-person perspective, where the central character narrates the story from his/her perspective, and the third-person perspective, where the omniscient narrator recounts the story from a neutral perspective. This is a basic classification of perspective in literature. These perspectives represent the spirit of the age, typically shown in the positivism in 19th century French novels (Ishimaru, 1985), and also affect a readers’ impression of the characters and involvement in the work, and thus perspective is an important subject in literary studies.
Computational stylistics has been one of the important subfields of Digital Humanities. Using computational methods with digitized text materials, we can obtain systematic findings that can complement traditional qualitative analyses. Although computational methods can be powerful tools for investigating issues in literary studies, perspective in novels has rarely been analyzed with such method.
Against this background, we used computational stylistic methods, i.e., text classification and feature analyses by random forests machine learning methods, to tackle the perspective issue in literary studies. We selected Kotaro Isaka, who is a popular Japanese novelist, as the object of study; he explicitly switches perspective in his novels section by section, and this is an important reason for the popularity of his novels. Note that Haruki Murakami, another popular novelist, uses this perspective switching between two perspectives (Kudo et al., 2012). However, Isaka uses more varied perspective-switching patterns (Yamashita and Suzuki, 2013). First, we generated text files and applied morphological analysis. We then conducted random forests text classification and feature extraction experiments using text-feature matrices for two of Isaka’s novels. Then, we investigated (a) whether textual differences among perspectives can be detected or not, and (b) if detected, what types of textual characteristics contribute to the detection of perspective. By tackling these points, we will show the effectiveness of computational methods for analyzing the perspective issue in literary studies.
2. Data and methods

We selected the following novels by Kotaro Isaka, “Odyubon no Inori” (Audubon’s Prayer; ADP, original 2000, pocket edition 2003) and “Gurasuhoppa” (Grasshopper; GHP, original 2004, pocket edition 2007) as objects. ADP is a work representative of the earlier period of the author’s bibliography, and GHP is representative of the author’s middle period. We used the pocket editions of these two novels because Isaka is known to revise manuscripts when his work is published in pocket editions. We constructed the texts using a OCR document scanner and manually corrected OCR errors. We also removed the rubi, i.e., kanas printed alongside kanjis. We applied morphological analysis using MeCab,[1] Japanese morphological analyzer.
We divided the texts into sections and assigned perspective tags according to the perspective signs assigned by the author. Regarding ADP, we united all character perspectives except Ito, the central character, because the number of perspectives for each character is small. Without unification of perspective, it was difficult to perform meaningful classification and feature analysis experiments. Thus, we used two tags, Ito’s perspective and other characters’ perspectives. The numbers of sections was 56 for Ito and 22 for other characters. It should be noted that Ito’s section appeared after another of his section. Regarding GHP, we used three tags for the three main characters’ perspectives (Suzuki, Kujira, and Semi) according to the signs assigned by the author. The sequences of these three characters’ perspectives are essentially fixed, Suzuki first, Kujira second, and Semi third. In addition, the death of a character leads to the removal of that character’s perspective. The numbers of sections was 17 for Suzuki, 15 for Kujira, and 10 for Semi.
We calculated the frequencies of morphemes and basic textual statistics, and then we constructed the text-feature matrices using the relative frequencies of morphemes appearing in each text. We applied random forests machine learning methods proposed by Breiman (2001) with these matrices as data and perspectives as labels. We calculated the valuable importance provided by random forests and extracted important variables for classification, which are effective for differentiation among perspectives. We selected the random forests method because it has shown the best possible performance for authorship attribution in Japanese (Jin and Murakami, 2007) and is effective for extracting and analyzing the features that contribute to classification in related tasks such as computational sociolinguistics (Suzuki, 2009).
3. Results and discussion

3.1. Basic observation

Table1. Basic data (ADP)
Number of tokens
Number of texts sum mean s.d. c.v.
Ito 56 118042 2107.89 1712.37 0.81
Others 22 23290 1058.64 1315.31 1.24
Table 1 shows the basic data for ADP, the number of texts, and the sum, mean, standard deviation (s.d.), and coefficient of variations (c.v.) of the number of tokens for each perspective. It can be seen that Ito has more that 70% of all sections, and others have larger variances of the c.v. It is assumed that the larger variances were caused by the unification of characters.

Table 2. Basic data (GHP)
    Number of tokens
  Number of texts sum mean s.d. c.v.
Suzuki 17 51229 3013.47 1930.56 0.65
Kujira 15 33453 2230.2 1251.70 0.56
Semi 10 27153 2715.3 946.63 0.35
Table 2 lists the basic data for GHP, the number of texts, and the sum, mean, s.d., and c.v. of the number of tokens for each perspective. The table shows that Suzuki has the largest section numbers and has the largest c.v. It is assumed that Suzuki’s perspective includes both small and long sections.
3.2. Classification by random forests

Table 3. Classification results (ADP)
  Ito others error rates
Ito 55 1 0.02
Others 17 5 0.77
Table 3 shows the classification results obtained by random forests for ADP. Each column represents the original tags, and each row represents the results. It can be seen that 55 of 56 Ito texts were classified as Ito’s. It is assumed that Ito’s perspectives have special characteristics. In comparison, only 5 of 22 texts by others were classified as others and 17 of 22 texts by others were classified as Ito. It is assumed that these results were partly caused by the limits of our experiments; the number of Ito texts was much larger than others, and the text from several characters was merged.

Table 4. Classification results (GHP)
  Suzuki Kujira Semi error rates
Suzuki 17 0 0 0
Kujira 0 15 0 0
Semi 0 5 5 0.45
Table 4 shows the classification results obtained by random forests for GHP. Each column represents the original tags, and each row shows the results given by random forests. It can be seen that all Suzuki texts were 17 were classified as Suzuki’s, and all Kujira texts were classified as Kujira’s. Only 5 of 10 texts were classified as Semi, and 5 other texts were classified as Kujira. It is assumed that there were special characteristics for Suzuki and Kujira’s perspective; however, in comparison, Semi’s perspectives were rather characterless and closer in nature to Kujira’s texts. It is worth noting that both Semi and Kujira are assassins, and Suzuki is an employee; therefore, it is assumed that the fact that Semi and Kujira are similar characteristics indicates the author’s intent to differentiate these two characters and Suzuki.
3.3. Feature analysis

Table 5. Top 20 important features (ADP)
  feature readings translation pos variable importance
1 僕 Boku I noun (pronoun) 0.01911
2 だ da be auxiliary verb 0.00661
3 日比野 Hibino Hibino noun (proper) 0.00404
4 ん n - noun, auxiliary verb, particles 0.00293
5 。 - - symbol 0.00267
6 を wo - particle 0.00262
7 静香 Shizuka Shizuka noun (proper) 0.00253
8 」 - - sign 0.00246
9 声 Koe Voice noun 0.00214
10 しれ shire - verb 0.00177
11 よ yo - particle 0.00172
12 伊藤 Ito Ito noun (proper) 0.00165
13 かも kamo May particle 0.00142
14 歯 Ha Dent noun 0.00126
15 に ni - particle 0.00113
16 いや Iya - exclamations 0.00106
17 島 Shima Island noun 0.00095
18 返事 Henji Reply noun 0.00094
19 目 Me Eye noun 0.00093
20 ? - - symbol 0.00092
Table 5 shows the top 20 variables that contributed to classification of ADP with English translations, indicates parts of speech, and shows the variable importance obtained by random forests. The variables include many proper nouns and content words such as “島” (Shima; Island) which simply represent contextual difference in the narrative. Table 5 also includes stylistic characteristics such as pronouns that represent the differences between the perspectives of Ito and others.
Table 6. Top 20 important features (GHP)

  feature reading translation pos variable importance
1 鈴木 Suzuki Suzuki noun (proper) 0.00947
2 妻 Tsuma wife noun 0.00938
3 比 Hi - noun (proper) 0.00812
4 亡き Naki dead adnominal 0.00781
5 鯨 Kujira Kujira noun (proper) 0.00764
6 亡霊 Borei ghost noun 0.00699
7 僕 Boku I noun (pronoun) 0.00664
8 子 Ko Ko noun (proper) 0.00560
9 槿 Asagao Asagao noun (proper) 0.00524
10 西 Nishi noun (proper) 0.00477
11 岩 Iwa - noun (proper) 0.00475
12 与 Yo noun (proper) 0.00452
13 彼女 Kanojo she noun (pronoun) 0.00393
14 ねえ nee - noun 0.00367
15 おまえ Omae you noun (pronoun) 0.00354
16 長男 Chonan eldest son noun 0.00322
17 君 Kimi you noun (pronoun) 0.00297
18 つう Tsuu - auxiliary verb 0.00268
19 なかっ nakatt - auxiliary verb 0.00254
20 だろ daro - auxiliary verb 0.00224
Table 6 shows the top 20 variables that contributed to the classification of GHP with translations in English, indicates part of speech and presents the variable importance obtained by random forests. Table 6 includes many [part of] proper nouns, indicating that they are the most important characteristics for discriminating the perspectives of the three main characters. In addition, Table 6 includes “つう” (Tsuu) and “ねえ” (Nee), which are style markers specific to several characters (e.g., Kujira) This indicates that these special style markers are also important characteristics for discriminating the perspectives among the three main characters.
4. Conclusion

This study analyzed the textual difference among perspectives in two contemporary Japanese novels. The results indicate that (a) respective perspectives have their specific textual characteristics, (b1) textual characteristics such as proper nouns that represent respective scenes are important for discriminating perspectives, and (b2) stylistic characteristics such as pronouns and nouns that represent styles of speech are also important. We conclude that computational stylistic methods can differentiate among perspectives in contemporary novels.
This study is a preliminary analysis of the study of perspectives using computational stylistic methods and is also part of an ongoing study of Kotaro Isaka’s work. In future, we would like to further investigate the effectiveness of computational methods for perspective issues and continue to analyze other work by Kotaro Isaka.

This study was supported by Grant-in-Aid for Scientific Research 23700288 for Young Scientists (B), from the Ministry of Education, Culture, Sports, Science and Technology, Japan. An earlier version of this study was presented at the 19th Annual Meeting of Japanese Natural Language Processing (NLP2008) at Nagoya University. This research includes revised and expanded content based on the gradation thesis presented by Natsumi Yamashita to the Faculty of Sociology, Toyo University.

Breiman L. (2001) Random forests, Machine Learning, Vol.45, pp.5-23.
Isaka, K. (2003) Odyubon no Inori,Sincho Bunko, Tokyo.
Isaka, K. (2007) Gurasuhoppa, Kadokawa Bunko, Tokyo.
Ishimaru, A. (1985) Bunsyo ni okeru shiten, Nihongogaku, 4(12), 22-31.
Jin, M. and M. Murakami (2007) Authorship identification using random forests, Proceedings of the Institute of Statistical Mathematics, 55(2), 255-268.
Kudo, A., Murai, H. and A. Tokosumi (2012) Kyotsu go no fuchi to henka ni motoduku heiko keisiki syosetsu no monogatari kouzo, Journal of Japan Society of Information and Knowledge, 22(3) 187-202.
Suzuki, T. (2009) Extracting speaker-specific functional expressions from political speeches using random forests in order to investigate speakers' political styles, Journal of the American Society for Information Science and Technology, 60(8), 1596-1606.
Yamashita, N. and T. Suzuki (2013) Keiryo tekisuto bunseki wo mochiita syosetsu no shiten kenkyu: Isaka Kotaro wo rei to shite, Proceedings of 19th Annual Meeting of the Association of Natural Language Processing (NLP2013), P1-3 (

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Conference Info


ADHO - 2014
"Digital Cultural Empowerment"

Hosted at École Polytechnique Fédérale de Lausanne (EPFL), Université de Lausanne

Lausanne, Switzerland

July 7, 2014 - July 12, 2014

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Attendance: 750 delegates according to Nyhan 2016

Series: ADHO (9)

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