{"id":11299,"date":"2018-11-07T14:38:56","date_gmt":"2018-11-07T19:38:56","guid":{"rendered":"http:\/\/studentwork.prattsi.org\/infovis\/?p=11299"},"modified":"2019-01-10T23:53:43","modified_gmt":"2019-01-11T04:53:43","slug":"using-gephi-to-dive-into-a-novel-david-copperfield-by-charles-dickens","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/using-gephi-to-dive-into-a-novel-david-copperfield-by-charles-dickens\/","title":{"rendered":"Using Gephi to Dive into a novel : David Copperfield by Charles Dickens"},"content":{"rendered":"

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Introduction<\/span><\/em><\/h2>\n

On this page, I used Gephi as a visualization tool to see the common words(adjectives and nouns) in the novel “David Copperfield” .<\/p>\n

Inspiration<\/span><\/em><\/h2>\n

It brought delightment to me that things I learnt in Course 601: Foundation of Information connect to what I am doing in this project in Course 658: Data Visualization. Dr. Rabina just clarified that “Content Analysis” does not mean doing research on what message conveyed in a paper, it is rather a research on word count or paragraph as objects.<\/p>\n

Material<\/span><\/em><\/h2>\n

After researching on this page<\/a><\/span>\u00a0<\/strong>of datasets, it turns that i found my interest. There are datasets of Web and internet<\/strong>, Social network<\/strong>, Biological networks<\/strong>, Infrastructure networks<\/strong> and Other works<\/strong>. I chose to investigate more on Word adjacencies, which is an “adjacency network of common adjectives and nouns in the novel David Copperfield by Charles Dickens. Please cite M.E.J.Newman, Phys.Rev.E 74, 036104 (2006)”<\/p>\n

The source file is named “word_adjacencies.gml” . Since Gephi can open file with many forms<\/strong><\/a><\/span> including .gml, so I just need to open the program then import the file as a source.<\/p>\n

Methods<\/span><\/em><\/h2>\n

The goal of Gephi is to “help data analysts to make hypothesis, intuitively discover patterns, isolate structure singularities or faults during date sourcing”. In order to make reasonable hypothesis, I reviewed wikipedia<\/a><\/span>\u00a0for a general idea of this novel. Then I have the following questions or hypothesis:<\/p>\n

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  1. The author Charles hold this novel as the”favorite child”, Can I see this through the network?<\/li>\n<\/ol>\n

    2. This novel contains descriptions based on Charles’ own childhood experience, can I see it?<\/p>\n

    Results<\/span><\/em><\/h2>\n

    In the “Data Laboratory tab”, it can be seen that, there are 112 nodes with ID numbered from 0 to 111. As described in the name of the dataset, these 112 words are all common adjectives and nouns. Such as: agreeable, man, old, person, anything,short…..<\/p>\n

    Also in the Data Table in Data Laboratory of Gephi, it can be seen that value of each words are defined: adjective is defined as 0, noun is defined as 1. Besides, degree value is the sum of both In- Degree and Out- Degree.<\/p>\n

    The highest score of In- degree is 40, and the word is “little”; The highest score of Out- degree is 20, and the words are “same” and “good”; Overall, the top five highest score of Degree is 49: little, 33:old, 28:other,28:good and 21:same.<\/p>\n

    Interpretation<\/span><\/em><\/h2>\n
    filter:Degree>5\r\nA general map of common words can be seen.\r\nThe word \"little\"has the highest degree of connection.<\/strong><\/pre>\n
    \"\"<\/p>\n
    \"\"\"\"<\/p>\n
     <\/p>\n
    Filter:In-degree>5\r\nCommon Words:\r\nlittle, other, old, good<\/strong><\/pre>\n
     <\/p>\n
     <\/p>\n
     <\/p>\n
     <\/p>\n
     <\/p>\n
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     <\/p>\n
     <\/p>\n
    Filter:Out-degree>5\r\nCommon Words:\r\nLittle, other, good, same<\/strong><\/pre>\n
    Compared with the last graph, the word “old” is missing, and the word “same” is here with higher out-degree. It can be estimated that less words adjacent with word”old” , such as “little old”, but more words adjacent with “old”, such as “old friend”,”old man”…<\/p>\n
     <\/p>\n
    \"\"<\/h2>\n
     <\/p>\n
    In-degree > 8<\/strong><\/pre>\n
    When focused on nodes with higher degree(in>8 and out>6). It is easier to research more on the direction of the connection.<\/p>\n
     <\/p>\n
     <\/p>\n
     <\/p>\n
     <\/p>\n
    \"\"<\/h2>\n
     <\/p>\n
    Out-degree >6<\/strong><\/pre>\n
    All these layout are based on “Fruchterman Reingold”, it turns the graph of netword into a round display, which is simple and direct to show network with lower modularity class(<1).<\/p>\n
     <\/p>\n
     <\/p>\n
    <\/h2>\n
    Out-degree >6<\/strong><\/pre>\n
    \"\"<\/p>\n
    I adjusted color of nodes and size of nodes to show a better image of\u00a0 more information. I also switched the layout function to “ForceAtlas 2″<\/p>\n
    In this layout, nodes or words with the same degree of network are colored the same, as well as the size.<\/p>\n
    Below is a video\/gif of flashing displays under function”ForceAtlas 2”.<\/p>\n