字詞頻度與關係：
import os
import sys
from operator import itemgetter
import subprocess
class NewsAnalytics():
dataPath = None
def __init__(self, dataPath):
self.dataPath = dataPath
def NewsTermFreq(self, dataFileName):
termDict = dict()
dataFile = self.dataPath + "/" + dataFileName + ".txt"
if not os.path.isfile(dataFile):
return(False)
with open(dataFile, "r") as fileOp:
sourceData = fileOp.read()
v = sourceData.split()
for i in range(0, len(v) - 1):
key = v[i]
if termDict.get(key) is None:
termDict[key] = 1
else:
termDict[key] += 1
fileOp.closed
dataFile = self.dataPath + "/" + dataFileName + "Freq.csv"
with open(dataFile, "w") as fileOp:
fileOp.writelines("TERM, WGHT, SEQ\n")
i = 0
for term in sorted(termDict.items(), key=itemgetter(1), reverse=True):
i += 1
fileOp.writelines(u"{0:s}, {1:d}, {2:d}\n".format(term[0], term[1], i))
fileOp.closed
return(True)
def NewsTermNetwork(self, dataFileName):
linkDict = dict()
termDict = dict()
dataFile = self.dataPath + "/" + dataFileName + ".txt"
if not os.path.isfile(dataFile):
return(False)
with open(dataFile, "r") as fileOp:
sourceData = fileOp.read()
v = sourceData.split()
for i in range(0, len(v) - 1):
key = v[i]
if termDict.get(key) is None:
termDict[key] = i + 1
for i in range(0, len(v) - 2):
t1 = v[i]
t2 = v[i+1]
key = "%s,%s" % (t1, t2)
if linkDict.get(key) is None:
linkDict[key] = 1
else:
linkDict[key] += 1
fileOp.closed
dataFile = self.dataPath + "/" + dataFileName + "Net.csv"
with open(dataFile, "w") as fileOp:
fileOp.writelines("N1, N2, WGHT, TERM1, TERM2\n")
for term in sorted(linkDict.items(), key=itemgetter(1), reverse=True):
keyPair = term[0].split(",")
fileOp.writelines(u"{0:d}, {1:d}, {2:d}, {3:s}, {4:s}\n".format( \
termDict[keyPair[0]], termDict[keyPair[1]], term[1], \
keyPair[0], keyPair[1]))
fileOp.closed
return(True)
if __name__ == "__main__":
dataPath = "."
dataNews = "ChinaTimes"
if len(sys.argv) > 1:
dataNews = sys.argv[1]
if len(sys.argv) > 2:
dataPath = sys.argv[1]
dataNews = sys.argv[2]
newsAnalytics = NewsAnalytics(dataPath)
print("字詞頻度: ", end="")
if newsAnalytics.NewsTermFreq(dataNews):
print("OK")
else:
print(" FALSE")
print("字詞關係: ", end="")
if newsAnalytics.NewsTermNetwork(dataNews):
print("OK")
else:
print(" FALSE")
網路分析：
library(igraph)
rm(list=ls())
dataPath <- 'C:/Users/Rich Lee/Documents/News'
dataName <- 'NewsNet'
dataFile <- paste(dataName, 'csv', sep='.')
setwd(dataPath)
LoadData <- function(dataPath, dataFile) {
dataEdgeList <- read.csv(paste(dataPath, dataFile, sep='/'))
dataEdgeList[, 2] <- as.character(dataEdgeList[, 2])
dataEdgeList <- as.matrix(dataEdgeList)
dataGraph <- graph.edgelist(dataEdgeList[, 1:2])
E(dataGraph)$weight <- as.numeric(dataEdgeList[, 3])
V(dataGraph)$size <- degree(dataGraph)
dataGraph
}
dataGraph <- LoadData(dataPath, dataFile)
closeGraphList <- sort(closeness(dataGraph), decreasing=TRUE)
betweenGraphList <- sort(betweenness(dataGraph), decreasing=TRUE)
degreeGraph <- centralization.degree(dataGraph)$centralization
closeGraph <- centralization.closeness(dataGraph, mode="all")$centralization
evcentGraph <- centralization.evcent(dataGraph, directed=FALSE)$centralization
print("Closeness:")
print(closeGraphList)
print("Betweenness:")
print(betweenGraphList)
figureGraph <- data.frame(varible=c("Degree", "Closeness", "Eigen"),
figure=c(degreeGraph, closeGraph, evcentGraph))
bmpFile <- paste(dataName, 'bmp', sep='.')
bmp(paste(dataPath, bmpFile, sep='/'), width=2048, height=2048)
plot(dataGraph, layout=layout.fruchterman.reingold,
vertex.color="yellow",
vertex.label.font=24,
vertex.label=V(dataGraph)$name,
edge.width=E(dataGraph)$weight)
dev.off()
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Closeness：
司法院 研議 人民 專業 司法 正義 行政院會 疏離 法官
0.3012048 0.3003003 0.2985075 0.2857143 0.2659574 0.2652520 0.2570694 0.2557545 0.2531646
精密 法律 公平 抗議 無罪推定 病人 刑事 現代社會 可行性
0.2487562 0.2475248 0.2469136 0.2364066 0.2358491 0.2352941 0.2331002 0.2325581 0.2257336
倚賴 華爾街 主張 價值觀 證據主義 現代 義憤 台灣 陌生
0.2202643 0.2127660 0.2114165 0.2109705 0.2096436 0.2074689 0.2024291 0.1996008 0.1996008
阿拉伯之春 醫生 基本 普遍 司改 關鍵 兩面之詞 僵局 白玫瑰運動
0.1934236 0.1919386 0.1908397 0.1845018 0.1845018 0.1838235 0.1824818 0.1824818 0.1814882
民眾 基本理念 醫病關係 靜聽 確實 熱情 判斷 調查 論壇
0.1801802 0.1703578 0.1694915 0.1680672 0.1677852 0.1672241 0.1666667 0.1658375 0.1587302
僵硬 雙方 位置 高度 法定 蘋果日報 倨傲 罪刑 誤解
0.1567398 0.1560062 0.1557632 0.1550388 0.1550388 0.1485884 0.1457726 0.1449275 0.1396648
看法 關注 媒體 立法院 試行條例
0.1317523 0.1246883 0.1183432 0.1126126 0.1074114
Betweenness：
人民 專業 台灣 民眾 司法 陌生 疏離 義憤 調查
2232.8333 1529.8333 820.6667 712.0000 671.8333 632.0000 630.0000 560.0000 501.0000
試行條例 立法院 媒體 關注 看法 誤解 蘋果日報 白玫瑰運動 論壇
477.0000 475.0000 473.0000 471.0000 469.0000 467.0000 465.0000 463.0000 463.0000
基本理念 關鍵 無罪推定 法官 正義 普遍 高度 熱情 阿拉伯之春
461.0000 459.0000 343.6667 341.0000 335.3333 332.0000 320.0000 318.0000 288.3333
華爾街 抗議 倨傲 僵硬 醫病關係 司改 證據主義 罪刑 法定
286.3333 284.3333 180.0000 178.0000 176.0000 174.0000 166.0000 161.0000 159.0000
判斷 基本 價值觀 位置 僵局 靜聽 醫生 兩面之詞 主張
157.0000 125.6667 123.6667 115.5000 114.6667 113.5000 113.0000 111.5000 111.0000
病人 雙方 確實 現代 可行性 研議 倚賴 刑事 精密
109.0000 93.0000 91.0000 57.0000 57.0000 57.0000 57.0000 55.0000 55.0000
行政院會 司法院 現代社會 法律 公平
55.0000 0.0000 0.0000 0.0000 0.0000
varible figure
1 Degree 0.09839477
2 Closeness 0.28996402
3 Eigen 0.89711812
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