People living in the digital age usually has difficulties in reading classical novels, in terms of obscure words, contextual difference and reading habits. This paper proposes a framework of digital models integrating spatial narrative theories to represent the narrative and narrative of experience of a Chinese classic novel, The Tale of Li Wa, which has been diversely interpreted by literature and historians in the past approximately 900 years. To help contemporary readers understand this classic narrative and its context in an integrated and in-depth approach, based on its knowledge graph about “narratives, experiences and geographical spaces”, the spatio-temporal information, derived from its text, its author, and readers, is extracted and fused to map the instantaneous spatial pattern perceived by readers in the flow of reading time.The discussion presents one of these possible interpretations on illustrating the growth of the novel’s male protagonist in the open framework of "Time-Space-time-Space", which unfolds dialogues between computation and literature, diachronic and synchronic, reader and the author.
Flows (black arrows) of variables and comparisons (white arrows) among variables in the logical loop of time–space-time–space
- Electronically scanned version of The Tale of Li Wa in Complete Library in Four Sections 四库全书
- Proofreading Text Edition of The Tale of Li Wa based on the version on 中国哲学书电子化计划（CText）
- Raster map of Tang Chang'an with location information.
It can be added in Arcmap/Qgis. It is an archaeological map in 「数字历史黄河·城市聚落资料集」from Remote Sensing Analysis of Historical Landscape and GIS Laboratory, Northwest Institute of Historical Environment and Socio-Economic Development, Shaanxi Normal University 陕西师范大学西北历史环境与经济社会发展研究院历史景观遥感分析与GIS实验室 - 黄大宏. A chronicle of Bai Xingjian
S1 Table.xlsx (sheet1_name: term, sheet2_name: POS, sheet3_name: so, sheet4_name: sentiment classification score)
we manually create this database including terms (unigram), parts of speech (POS), sentiment orientations (SO) value, and sentiment shifters by Excel.
Segment of Term Criteria: Broadening, Dictionary, and Semantic Transparency
Reference dictionary: 国学大师
Reference Word Segmentation platform: 语料库在线
| Tag | n | nt | nd | nl | nh | nhf | nhs | ns | nn | ni | no | nhh | v | vd | vl | vu | a | f | m | q | d | r | p | c | u | e | o | i | w |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pos |
Noun-general | Noun-time | Noun-direction | Noun-location | Noun-human | noun-last name | noun-first name | Noun-space | Noun-nation | Noun-institution | Noun-offical | noun-human’s pronoun | Verb | Verb- direction | Verb-linking | Verb-auxiliary | adjective | difference | numeral | quantity | adverb | pronoun | preposition | conjunction | auxiliary | exclamation | onaomatopoeia | idiom | punctuation |
The assignment of the SO value is as follows: each positive sentiment expression in the novel such as laugh (欢笑) (v.) and magnificent (瑰奇) (a.) is given an SO value of +1 (172 in total), and each negative sentiment expression such as whimper (呜咽) (v.) and poor (贫窭) (a.) is assigned a SO value of −1 (177 in total).
We do two rounds of sentiment orientations (SO) value assignment(LIU_SO value and MA_SO value).
The percentage of consent of two rounds of SO value assignment is 81.5%.
word2vec_python_code&raw_data
sentiment_network_Gephi.zip
result_weighted_outdegree_distribution.xlsx
We assumed that the sentiment words in ancient Chinese follow the same logic in today’s sentiment analysis–– sentiment words have context-dependent orientations, i.e., the total distance among words with the same orientation sentiment expression is closer than that among different.
Based on the unigrams removing stop words, we complete the training of its word2vec model with gensim package, and get the correlation(cosine_similarity, -0.4 ~ 1) between each two words.
This correlation between word A (Source) and word B (Target) multiplied by the SO value of word B is taking as the weight of the edge, to set up a words’ sentiment network in Gephi.
#tips for create edge to gephi.csv
| Source | Target | Weight |
|---|---|---|
| WordA | WordB | cosine_similarity*WordB_sentiment |
#tips for operation step in Gephi:
File → Improt spreadsheet → edge to gephi.csv → charset: GB2312 → Graph Type: directed
run all analysis of "Avg. Weighted Degree" on the right manue
Data Laboratory → Nodes → Export Table#
The weighted output distribution calculated by gephi is divided into three different types of manually collected sentient words (1, - 1,0), which are made into scatter diagram by Excel.
SO_value_effective = IF(sentiment_shifter_-1=-1,SO value * sentiment_shifter_-1,SO value)
for_phrase_sentiment_classification_score = IF(POS<>"w",SO_value_effective,"")
for_phrase_sentiment_classification_score = SUM(for_phrase_sentiment_classification_score)
#tips for operation step: Ctrl+G--> "Null(k)"--> "∑"(which means Automatic summation) #
phrase_sentiment_classification_score = IF(POS="w",for_phrase_sentiment_classification_score)
S2 Table.xlsx (sheet1_name: phrase, sheet2_name: time, sheet3_name: character, sheet4_name: character & SO, sheet5_name: place, sheet6_name: place & SO)
The phrase-level framework assigns the recalculated value of POS and SO value to a relevant phrase by Excel. These values can be applied to the next time level because the sequence number of phrases is defined as read-time. Specific data mining approaches for the following parameters, i.e. places, story-time, and sentiment classification scores are valuable.
sentiment_classification_score(SCS) inherits the value of phrase_sentiment_classification_score
storytime_day
Noun-time (nt.) such as the Tianbao period (天宝), 10 years later (十年), more than a month later (月余), and another day (他日), which is 2.7% of the total texts, are used to simulate the whole story-time in an interval of every single day. The entire story timeline we constructed from the texts started from when Student Zheng entered Chang’an in 747A.D. and ended around the happy ending of the novel, that is, the year Zheng is appointed to become an officer is 754 A.D., and Li Wa is conferred the title Lady Qian‘guo (汧国夫人) in 775 A.D. The story-time is defined by the exact time record of the story that occurred during the period of 742 to 746 A.D.(天宝年间), Bai Xingjian wrote the tale in August of 795 A.D. (贞元中……乙亥岁秋八月) and the nt. phrases.
readtime_phrase
the sequence number of phrases is defined as read-time
character1， character2， character3， and character4 contain one character in each phrase(since one phrase contains at most 4 characters).
ZHENG_SCS = IF(character1="郑生" or character2="郑生" or character3="郑生" or character4="郑生",sentiment_classification_score(SCS),"")
ZHENG_so_IF= SUM(ZHENG_SCS)
#tips for operation step: K15=SUM(J$15:J15), K18=SUM(J$15:J18), K50=SUM(J$15:J50)#
A noun-space (ns.) such as Chang’an City, and specific place names inside the city such as the Buzheng Ward (布政坊) and Xingyuan Garden (杏园) (located in Tongshan Ward [通善坊]), account for 1.1% of the total texts tagged as the level of residential wards and streets directly mentioned (e.g., Buzheng Ward) or most likely to be located (e.g., Tongshan Ward). These uniformly fine-grained places are applied to cover the corresponding story phrases of which plot takes place in these places.
Anyi_SCS = IF(ward_in_chang'an="安邑坊",sentiment_classification_score(SCS),"")
Anyi_so_IF= SUM(Anyi_SCS)
#tips for operation step: I526=SUM(H$526:H526), I535=SUM(H$526:H535), I606=SUM(H$526:H606)#
S3 Table.xlsx (sheet1_name: circumstance, sheet2_name: poems) is bassed on A chronicle of Bai Xingjian collated by 黄大宏
Detail contians Bai's specific experience every year
circumstance_orientation_value is assigned manually based on the good/bad of Detail. Such as "祖母殁于新郑县私第(Bai's grandmother died)"is assigned a value of −1, "行简进士及第...行简同年...应制举(Bai passed the Imperial Examination...Bai passed passed the Palace Examination)" is assigned a value of +2.
Circumstances_of_Bai = SUM(circumstance_orientation_value)
#tips for operation step: E2=SUM($D$2:D2), E10=SUM($D$2:D10), E52=SUM($D$2:D52)#
circumstance_orientation_value_chang'an = IF(Place="长安",circumstance_orientation_value,"")
S4 File.graph
Vector file of street of chang'an is created by Autocad and then imported into Depthmap(a technology used to analyze the spatial layouts, and human activity patterns in urban areas)
#tips for operation step:
Using Autocad to depict the main road axis map of Chang'an map(Vector file of street of chang'an) → Save as dxf file → Open the depthmap software and create a new workspace → Map-import-Choosing Chang'an Road Axis Chart → ap-convert drawing map → tools-axial/convex/pesh-run graph analysis-Radius/list of radii – input n,2,3,5,7-choose include choice（betweenness）/local measures/RA,RRA and total depth/weighted measures-length#
The degree of integration (a space syntax parameter) reflects the ease of access to streets, that is, it may determine which street is more likely to attract Zheng, as an explorer of Chang’an.
S3 File.zip(path.xlsx, place.xlsx, shikong-vt.shp, link_between_places.shp) combines Text database with spatial data by Arcmap.
create path.xlsx semi-manually by Excel based on Text database on phrase level
Each time when ward_in_chang'an in sheet "place & SO" changes, an ID is added in path.xlsx with Origin to Destination
sentiment score between places: value difference of sum_sentiment_effective_classification_score between Origin and Destination
straturm classification: The classification of social stratum of the character
The classification of social stratum in the story from untouchable to nobles is as follows: (1) beggar, servant, and sex worker; (2) businessman, civilian, madam; (3) ward head (里胥), candidate student, successful candidate, county judicial official (贼曹); and (4) Chang’an officials (京尹) and officials from other places.
create place.xlsx( sheet1_name: stratum_statistics, sheet2_name: place_statistics) by Excel based on Text database on phrase level
stratum_statistics inherits the value in path.xlsx
#tips for operation step in place_statistics:
place |
sum_sentiment_effective_classification_score |
phrase_count |
averge_sentiment_classification_score |
effective_sum_sentiment_classification_score |
effective_phrase_count |
averge_sentiment_effective_classification_score |
averge_stratum |
STDEV_stratum |
COUNT_stratum |
|---|---|---|---|---|---|---|---|---|---|
| ward | SUM(place_SCS) in sheet" place & SO" |
COUNT(place_SCS) in sheet" place & SO" |
AVERAGE(place_SCS) in sheet" place & SO" |
IF place_SCS<>0,SUM(place_SCS),"" in sheet" place & SO" |
IF place_SCS<>0,COUNT(place_SCS),"" in sheet" place & SO" |
IF place_SCS<>0,AVERAGE(place_SCS),"" in sheet" place & SO" |
AVERAGE(place_stratum) in sheet" stratum_statistics" |
STDEV(place_stratum) in sheet" stratum_statistics" |
COUNT(place_stratum) in sheet" stratum_statistics" |
3) Polygon
#tips for operation step:
create shapefile of Polygon( Ward& Palace) based onRaster map of Tang Chang'an with location information#
This shapefile is not uploaded to github due to copyright issues
4) Point
#tips for operation step:
- create Point( centroid of Polygon)--> add field
ward_in_chang'anand fill in - Text database on phrase level is joined with Point by field
ward_in_chang'an#
LIWA_data.shp
#tips for operation step:
- create Point( centroid of Polygon)--> add field
ward_in_chang'anand fill in - place.xlsx is joined with Point by field
NAME#
5) Polyline
S5 File.zip
#tips for operation step:
Arcmap--> Toolbox--> XY to Line--> import shikong-vt.shp#
- create polyline The rules of the simulation are from the characteristics of the streets: first, prefer the shortest path, and second, prefer the street sections with the highest degree of spatial syntax integration.
- path.xlsx is joined with polyline by field
ID
#tips for operation step:
create graph--> simple straight line--> data format--> XY Pair--> select data
data for X: storytime_day data for Y: SO_value_Integral_function(so_IF)#
#tips for operation step:
create graph--> multiple straight line--> data format--> XY Pair--> select data
data for X: storytime_day data for Y: SO_value_Integral_function(so_IF)
data for X: storytime_day data for Y: ZHENG_so_IF
data for X: storytime_day data for Y: LI_Wa_so_IF
data for X: storytime_day data for Y: LI_Wa's_mother_so_IF
data for X: storytime_day data for Y: ZHENG's_father_so_IF#
#tips for operation step:
create graph--> simple straight line--> data format--> XY Pair--> select data
data for X: storytime_day data for Y: SO_value_Integral_function(so_IF)
add new plot--> graph types--> vertical bar chart--> graph styles--> grouped bars--> data formats--> many Y
data for Y: Anyi_so_IF
data for Y: Buzheng_so_IF
data for Y: Chongren_so_IF
data for Y: EastMarket_IF
data for Y: Pingkang_IF
data for Y: DepartmentOfStateAffairs_IF
data for Y: TianmenStreet_so_IF
data for Y: Tongshan_so_IF
data for Y: Tongyi_so_IF
data for Y: WestMarket_so_IF
data for Y: XingqingPalace_IF
data for Y: Xuanyang_so_IF
graph page--> add axist--> Y#
#tips for operation step:
create graph--> multiple straight line--> data format--> XY Pair--> select data
data for X: storytime_day data for Y: SO_value_Integral_function(so_IF)
data for X: storytime_day data for Y: readtime_phrase
graph page--> add axist--> Y#
#tips for operation step:
create graph--> vertical bar chart--> graph styles--> grouped bar--> data format--> many Y
data for X: Age data for Y: Circumstances_of_Bai
data for X: Age data for Y: Circumstances_of_Bai_Chang'an#
import shikong-vt.shp, temporal simulation path in sapce.shp and link_between_places.shp into Arcscene
#tips for operation step:
"shikong-vt.shp"--> properties--> Element--> Single symbol
"temporal simulation path in sapce.shp"--> properties--> Symbolic System--> Graded colour--> value--> sheet1_em#
Visualization of path trajectory based on spatial discipline: characters’ appearance versus places’ appearance by Arcscene
#tips for operation step:
"shikong-vt.shp"--> properties--> Element--> Single symbol
"link_between_places.shp"--> properties--> Symbolic Systems--> category--> Unique value--> value--> 人物啊#
Visualization of path trajectory based on spatial discipline: characters’ appearance and the integral function of SO value versus places’ appearance by Arcscene
#tips for operation step:
"shikong-vt.shp"--> properties--> Element--> Single symbol
"link_between_places.shp"--> properties--> Symbolic Systems--> Graded colour--> value--> 情感差#
Visualization of path trajectory based on spatial discipline: characters’ appearance versus places’ appearance and the integral function of SO value by Arcscene
#tips for operation step:
"shikong-vt.shp"--> properties--> Symbolic System--> Graded colour--> value--> sheet1_em
"link_between_places.shp"--> properties--> Symbolic Systems--> category--> Unique value--> value--> 阶级值#
Visualization of path trajectory based on spatial discipline: characters’ appearance versus places’ appearance by Photoshop
#tips for operation step:
create the wordcloud image of POS of each place by [wordart]（ https://wordart.com/） --> imported in Photoshop --> move to relevant place --> export into png#
Inverse distance weighted (IDW) interpolation by ArcGIS of the sentiment classification score and places (a. sentiment classification score of place attribute, b. sentiment classification score/effective read-time of place attribute) by Arcmap
#tips for operation step:
Toolbox → geostatistical analyst- interpolation analysis – IDW→ input layer“点数据” → Environment-range-“皇城里坊”#
S7 File.zip(edge.csv, node.csv)
if two character co-occurr within two adjacent phrases, one edge will be added between them.
Statistics of characters in co-occurrence network, Girvan-Newman clustering, and betweenness centrality by Gephi
Statistics of characters in co-occurrence network, modularity class, and betweenness centrality by Gephi
#tips for operation step in Gephi:
File → Improt spreadsheet → edge.csv → charset: GB2312 → Graph Type: indirected
Tools → pluginss → Availible Plugins → Install
run all analysis of "Statistics" on the right manue
appearence → nodes → color → Partition → Modularity class → run
appearence → nodes → color → Partition → Cluster-ID → run
appearence → nodes → size → Ranking → Betweenness centrality → run#
Network parameters:
node：26
edge：38
Average Degree: 1.462
Average Weighted Degree: 4.731
Diameter: 5
Radius: 1
Average Path length: 2.265625
Density: 0.095
Randomize: On (Modularity)
Use edge weights: Off (Modularity)
Resolution: 0.8 (Modularity)
Modularity: 0.351 (Modularity)
Modularity with resolution: 0.218 (Modularity)
Number of Communities: 6 (Modularity)
Number of communities:4 (Girvan-Newman Clustering)
Maximum found modularity:0.39129266 (Girvan-Newman Clustering)
Network Interpretation: undirectedAverage
Clustering Coefficient: 0.506
Total triangles: 6
S8 File.zip(direct-node.csv, edge.csv, indirect-node.csv) is based on path.xlsx
Network analysis of characters and places in modularity class analysis (use edge weights: On) and weighted degree centrality based on a full-text, spatially embedded, undirected network of characters by Gephi
. Network analysis of characters and places in Girvan-Newman clustering analysis and weighted degree centrality based on a full-text, spatially embedded, undirected network of characters by Gephi
#tips for operation step in Gephi:
File → Improt spreadsheet → edge.csv → charset: GB2312 → Graph Type: indirected
Tools → pluginss → Availible Plugins → Install
run all analysis of "Statistics" on the right manue
appearence → nodes → color → Partition → Cluster-ID → run
appearence → nodes → color → Partition → Modularity class → run
appearence → nodes → size → Ranking → Weighed Degree → run#
Network analysis of characters and places in closeness centrality and betweenness centrality based on a full-text spatially embedded undirected network of characters by Gephi
#tips for operation step in Gephi:
File → Improt spreadsheet → edge.csv → charset: GB2312 → Graph Type: indirected
run all analysis of "Statistics" on the right manue
appearence → nodes → color → Ranking → Closeness centrality → run
appearence → nodes → size → Ranking → Betweenness centrality → run#
Network analysis of characters and places in authority and hub analysis based on a full-text spatially embedded directed network of characters by Gephi
#tips for operation step in Gephi:
File → Improt spreadsheet → edge.csv → charset: GB2312 → Graph Type: directed
run all analysis of "Statistics" on the right manue
appearence → nodes → color → Ranking → Authority → run
appearence → nodes → size → Ranking → Hub → run#
Network parameters:
node：16
edge：24
Average Degree: 3.000
Average Weighted Degree: 7.750
Diameter: 6
Radius: 3
Average Path length: 2.265625
Density: 0.200
Randomize: On (Modularity)
Use edge weights: On (Modularity)
Resolution: 0.8 (Modularity)
Modularity: 0.326 (Modularity)
Modularity with resolution: 0.194 (Modularity)
Number of Communities: 4 (Modularity)
Number of communities:4 (Girvan-Newman Clustering)
Maximum found modularity:0.3949653 (Girvan-Newman Clustering)
Network Interpretation: undirectedAverage
Clustering Coefficient: 0.474
Total triangles: 9
MA Zhaoyi ayi987654321@163.com
LIU Shuaishuai liushuai_1994@sina.com
Dr. HE Jie janushe@tju.edu.cn
XIAO Tianyi 137365121@qq.com
The authors gratefully acknowledge the Tang Chang’an GIS basemap provided by Prof. Pan Wei from Yunan University and his team from the GIS Lab at Northwest Institute of Historical Environment and Socio-Economic Development of Shaanxi Normal University. The first version of the historical GIS data of Tang Chang’an in this research are from Prof. Timothy Baker of National Dong Hwa University and Dr. Liao Hsiung-Ming of Acadmia Sinica.
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