如何更好地可视化给定文本的单词关联？

问题描述

我具体想要的是根据它们在文档中的显示方式，可视化与文档中的名词相关联的所有动词和形容词。

我在Python中找不到任何函数，因此我创建了自己的基本函数，如下所示。但是，可视化仍有不足之处：

import nltk
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

def word_association_graph(text):
    nouns_in_text = []

    for sent in text.split('.')[:-1]:   
        tokenized = nltk.word_tokenize(sent)
        nouns=[word for (word, pos) in nltk.pos_tag(tokenized) if is_noun(pos)]
        nouns_in_text.append(' '.join([word for word in nouns if not (word=='' or len(word)==1)]))

    nouns_list = []
    is_noun = lambda pos: pos[:2] == 'NN'

    for sent in nouns_in_text:
        temp = sent.split(' ')
        for word in temp:
            if word not in nouns_list:
                nouns_list.append(word)

    df = pd.DataFrame(np.zeros(shape=(len(nouns_list),2)), columns=['Nouns', 'Verbs & Adjectives'])
    df['Nouns'] = nouns_list

    is_adjective_or_verb = lambda pos: pos[:2]=='JJ' or pos[:2]=='VB'
    for sent in text.split('.'):
        for noun in nouns_list:
            if noun in sent:
                tokenized = nltk.word_tokenize(sent)
                adjectives_or_verbs = [word for (word, pos) in nltk.pos_tag(tokenized) if is_adjective_or_verb(pos)]
                ind = df[df['Nouns']==noun].index[0]
                df['Verbs & Adjectives'][ind]=adjectives_or_verbs

    fig = plt.figure(figsize=(30,20))
    G = nx.Graph()

    for i in range(len(df)):
        G.add_node(df['Nouns'][i])
        for word in df['Verbs & Adjectives'][i]:
            G.add_edges_from([(df['Nouns'][i], word)])

    pos = nx.spring_layout(G)
    nx.draw(G, with_labels=True, font_size=20) #font_weight='bold', 

因此，如果我们将Wikipedia对Wikipedia的描述的第一段作为我们想要可视化的示例文本，它将产生以下情节：

import re
text = "Wikipedia was launched on January 15, 2001, by Jimmy Wales and Larry Sanger.[10] Sanger coined its name,[11][12] as a portmanteau of wiki[notes 3] and 'encyclopedia'. Initially an English-language encyclopedia, versions in other languages were quickly developed. With 5,748,461 articles,[notes 4] the English Wikipedia is the largest of the more than 290 Wikipedia encyclopedias. Overall, Wikipedia comprises more than 40 million articles in 301 different languages[14] and by February 2014 it had reached 18 billion page views and nearly 500 million unique visitors per month.[15] In 2005, Nature published a peer review comparing 42 science articles from Encyclopadia Britannica and Wikipedia and found that Wikipedia's level of accuracy approached that of Britannica.[16] Time magazine stated that the open-door policy of allowing anyone to edit had made Wikipedia the biggest and possibly the best encyclopedia in the world and it was testament to the vision of Jimmy Wales.[17] Wikipedia has been criticized for exhibiting systemic bias, for presenting a mixture of 'truths, half truths, and some falsehoods',[18] and for being subject to manipulation and spin in controversial topics.[19] In 2017, Facebook announced that it would help readers detect fake news by suitable links to Wikipedia articles. YouTube announced a similar plan in 2018." 
text = re.sub("[[].*?[]]", "", text) # Do more processing (like lemmatization, stemming, etc if you want)
word_association_graph(text)

这个图的主要问题是，我似乎找不到一种方法来增加图中的集群内间隔。我尝试了documentation中提到的所有布局，但都不能解决此问题。

如果有人知道如何增加单词之间的类内分隔，那就太好了。否则，如果有其他好的现成的库来实现更花哨的单词联想可视化，那也是很棒的。

目前，我使用的"修复"是将绘图保存为SVG格式，并在浏览器上查看，这样我就可以更仔细地查看集群内部：

fig.savefig('pathwiki_net.svg', format='svg', dpi=1200)

---

解决方案

您可以通过调整布局和用于构建布局的参数来获得更好的分隔效果。更具体地说，如果您继续使用Spring_Layout，请使用‘k’参数来更好地分隔节点：

...
pos = nx.spring_layout(G, k=0.5)
nx.draw(G, pos, with_labels=True, font_size=20)
plt.show() 

k(浮点(默认值=无))-节点之间的最佳距离。如果没有 距离设置为1/sqrt(N)，其中n是节点数。增加 此值可将节点移动得更远。

k=0.5时，我得到：