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kafka_twitter_spark_streaming.py
777 lines (694 loc) · 27 KB
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kafka_twitter_spark_streaming.py
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"""
RUNNING PROGRAM;
1-Start Apache Kafka
bin/zookeeper-server-start.sh config/zookeeper.properties
bin/kafka-server-start.sh config/server.properties
2-Run kafka_push_listener.py (Start Producer)
PYSPARK_PYTHON=python3 bin/spark-submit kafka_push_listener.py
3-Run kafka_twitter_spark_streaming.py (Start Consumer)
PYSPARK_PYTHON=python3 bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.2.0 kafka_twitter_spark_streaming.py
"""
from __future__ import division
from collections import Counter
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from pyspark.sql.functions import udf
from pyspark.sql.types import FloatType
from pyspark.sql import SQLContext
from nltk.corpus import stopwords
from nltk.tokenize import TweetTokenizer
from googletrans import Translator
from datetime import datetime
from pyspark import sql
import MySQLdb
import json
import re
import nltk
import urllib.request as urllib2
import numpy as np
import pickle
import csv
import os
from pyspark.sql.types import Row
vector = { 'depth_retweets':0,
'ratio_retweets':0,
'hashtags':0,
'length':0,
'exclamations':0,
'questions':0,
'links':0,
'topicRepetition':0,
'replies':0,
'spreadVelocity':1,
'user_diversity':0,
'retweeted_user_diversity':0,
'hashtag_diversity':0,
'language_diversity':0,
'vocabulary_diversity':0}
t0_original = ''
data = {}
data_tweet_geo = {}
removal_list = ['\\','/',',','(',')','!',':','.']
stop = stopwords.words('english')
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.2.0 pyspark-shell'
os.environ["PYSPARK_PYTHON"]="/usr/bin/python3"
translator = Translator()
def getIntent(x):
tknzr = TweetTokenizer(reduce_len=True)
a = tknzr.tokenize(x)
# # # ##print "Twitter tokens: ", a
tagged = nltk.pos_tag(a)
# # # ##print 'Tagged: ', tagged
y = []
for x in tagged:
if x[1] == 'VB':
return x[0]
else:
return 'No Verb Found'
outFile = open('temp1.txt',"w")
timePerVector = 60
# writer.writerow(['depth_retweets','ratio_retweets','hashtags',
# 'length','exclamations','questions',
# 'links','topicRepetition','replies',
# 'spreadVelocity','user_diversity1',
# 'retweeted_user_diversity1','hashtag_diversity1',
# 'language_diversity1','vocabulary_diversity1'])
def saveDataToFile(x):
x = x.collect()
#print('xzxzxxxxxxxxxxxxxxxxxxxxzzzzzzzzzzzzzzzzzzzzzzzzzzzzz')
#print(x)
for data in x:
outFile.write(data)
outFile.write('\n')
# writer.writerow([data[0],data[1]])
def turnIntoVector(x):
#x = x.collect()
dataFrame = x.toDF()
def getRDD(x):
if(x!= None):
return x
def isReplies(a):
if a and a>0:
return 1
else:
return 0
numberItem = 0
def increaseBag(key, bag):
try:
##print("-------xxxxxxxxxxxxxx------------" + str(key))
##print(bag)
if str(key) in bag.keys():
bag[key]+=1
else:
bag[key]=1
except:
pass
def calShannon(bag):
sum=0
for item in bag:
temp = bag[item]/ numberItem
sum += temp*np.log(temp)
return -1*sum
#Average number of retweet levels in tweets.
depth_retweets=0
#Ratio of tweets that contain a retweet.
ratio_retweets=0
#Average number of hashtags in tweets.
hashtags=0
#Average length of tweets
length=0
#Number of tweets with exclamation signs.
exclamations=0
#Number of question signs in tweets.
questions=0
#Average number of links in tweets
links=0
cursor = None
#Average number of uses of the trending topic in tweets.
topicRepetition=0
#Average number of tweets that are replies to others
replies=0
#Average number of tweets per second in the trend
spreadVelocity=0
user_diversity=dict()
retweeted_user_diversity=dict()
hashtag_diversity=dict()
language_diversity=dict()
vocabulary_diversity=dict()
timeStart = datetime.now()
timeEnd = datetime.now()
def checkExistFile(filepath):
if not os.path.isfile(filepath):
f= open(filepath,"w+")
f.close()
import os.path
from pathlib import Path
def loadDataFromFile(trends):
global timeStart
global timeEnd
global numberItem
global depth_retweets
global ratio_retweets
global hashtags
global length
global exclamations
global questions
global links
global topicRepetition
global replies
global spreadVelocity
global user_diversity
global retweeted_user_diversity
global hashtag_diversity
global language_diversity
global vocabulary_diversity
myFile = Path("trends/" +str(trends) + "/data.txt")
if(myFile.is_file()):
f = open("trends/" + str(trends) + "/data.txt", "r")
#print('XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXx')
#print(f.readlines()[0] + 'wtf')
f = open("trends/" + str(trends) +"/data.txt", "r")
numberItem = int(f.readlines()[0])
#print('XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXx')
#print(numberItem)
f = open("trends/" + str(trends) + "/data.txt", "r")
ratio_retweets = float(f.readlines()[1])
f = open("trends/" + str(trends) + "/data.txt", "r")
hashtags = float(f.readlines()[2])
f = open("trends/" + str(trends) + "/data.txt", "r")
length = float(f.readlines()[3])
f = open("trends/" + str(trends) + "/data.txt", "r")
exclamations = float(f.readlines()[4])
f = open("trends/" + str(trends) + "/data.txt", "r")
questions = float(f.readlines()[5])
f = open("trends/" + str(trends) + "/data.txt", "r")
links = float(f.readlines()[6])
f = open("trends/" + str(trends) + "/data.txt", "r")
topicRepetition = float(f.readlines()[7])
f = open("trends/" + str(trends) + "/data.txt", "r")
replies = float(f.readlines()[8])
f = open("trends/" + str(trends) + "/data.txt", "r")
tempTime = f.readlines()[9].replace("\n", "")
# #print("SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS")
# #print(tempTime)
if (len(tempTime) > 23):
timeStart = datetime.strptime(tempTime, '%Y-%m-%d %H:%M:%S.%f')
else:
#print("SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS")
#print(tempTime)
timeStart = datetime.strptime(tempTime, '%Y-%m-%d %H:%M:%S')
f = open("trends/" + str(trends) + "/data.txt", "r")
tempTime = f.readlines()[10].replace("\n", "")
# #print("SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS")
# #print(tempTime)
if (len(tempTime) > 23):
timeStart = datetime.strptime(tempTime, '%Y-%m-%d %H:%M:%S.%f')
else:
timeStart = datetime.strptime(tempTime, '%Y-%m-%d %H:%M:%S')
f.close()
try:
user_diversity = json.load(open("trends/" + str(trends)+ "/user_diversity.txt"))
retweeted_user_diversity = json.load(open("trends/" + str(trends)+ "/retweeted_user_diversity.txt"))
hashtag_diversity = json.load(open("trends/" + str(trends)+ "/hashtag_diversity.txt"))
language_diversity = json.load(open("trends/" + str(trends)+ "/language_diversity.txt"))
vocabulary_diversity = json.load(open("trends/" + str(trends)+ "/vocabulary_diversity.txt"))
except:
pass
def saveDataToFile(trends):
#print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXSAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVESAVE")
if not os.path.exists("trends/" + str(trends)):
os.makedirs("trends/" + str(trends))
checkExistFile("trends/" + str(trends)+ "/data.txt")
checkExistFile("trends/" + str(trends)+ "/user_diversity.txt")
checkExistFile("trends/" + str(trends)+ "/retweeted_user_diversity.txt")
checkExistFile("trends/" + str(trends)+ "/hashtag_diversity.txt")
checkExistFile("trends/" + str(trends)+ "/language_diversity.txt")
checkExistFile("trends/" + str(trends)+ "/vocabulary_diversity.txt")
f = open("trends/" + str(trends)+ "/data.txt", 'w+')
f.truncate()
f.write(str(numberItem))
f.write("\n")
f.write(str(ratio_retweets))
f.write("\n")
f.write(str(hashtags))
f.write("\n")
f.write(str(length))
f.write("\n")
f.write(str(exclamations))
f.write("\n")
f.write(str(questions))
f.write("\n")
f.write(str(links))
f.write("\n")
f.write(str(topicRepetition))
f.write("\n")
f.write(str(replies))
f.write("\n")
f.write(str(timeStart))
f.write("\n")
f.write(str(timeEnd))
f.close()
f = open("trends/" + str(trends)+ "/user_diversity.txt", 'w+')
f.truncate()
f.close()
f = open("trends/" + str(trends)+ "/retweeted_user_diversity.txt", 'w+')
f.truncate()
f.close()
f = open("trends/" + str(trends)+ "/language_diversity.txt", 'w+')
f.truncate()
f.close()
f = open("trends/" + str(trends)+ "/vocabulary_diversity.txt", 'w+')
f.truncate()
f.close()
f = open("trends/" + str(trends)+ "/hashtag_diversity.txt", 'w+')
f.truncate()
f.close()
json.dump(user_diversity, open("trends/" + str(trends)+ "/user_diversity.txt",'w'))
json.dump(retweeted_user_diversity, open("trends/" + str(trends)+ "/retweeted_user_diversity.txt",'w'))
json.dump(hashtag_diversity, open("trends/" + str(trends)+ "/hashtag_diversity.txt",'w'))
json.dump(language_diversity, open("trends/" + str(trends)+ "/language_diversity.txt",'w'))
json.dump(vocabulary_diversity, open("trends/" + str(trends)+ "/vocabulary_diversity.txt",'w'))
def getIDFromDB(trend, time, tableName):
global cursor
trend = trend.replace("\n","")
sql = "SELECT MAX(ID) FROM %s WHERE TREND = '%s'" % (tableName,trend)
print(sql)
sql = sql.encode("utf-8")
cursor.execute(sql)
row = cursor.fetchone()
try:
print("HKJOPJBNMKJHBNMLKJKNMKLJBNKJKIJHUGFVBJHGVBNJGV" + row[0])
except:
pass
if (getTimeFromDB(row[0], time, tableName) == True):
return row
sql = "SELECT MAX(ID) FROM %s" % (tableName)
cursor.execute(sql)
row = cursor.fetchone()
print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxxXXXXXX",row[0])
if(row[0] == None):
return 0
return row[0] + 1
from datetime import datetime, date
def getTimeFromDB(ID, timeTweet, tableName):
global cursor
global timePerVector
try:
sql = "SELECT TIME FROM %s WHERE ID = %s" % (tableName,ID)
print(sql)
cursor.execute(sql)
time = cursor.fetchone()
time = time[0]
if(len(time) > 20):
datetime_object = datetime.strptime(time, '%Y-%m-%d %H:%M:%S.%f')
else:
datetime_object = datetime.strptime(time, '%Y-%m-%d %H:%M:%S')
print(timeTweet)
print(datetime_object)
timeCpr = abs(timeTweet - datetime_object)
print("XXXXXXXXXXXXXXXXXXXXXX TIME", timeCpr)
if(timeCpr.total_seconds() < timePerVector * 60):
print("XXXXXXXXXXXXXXXXXFALSE CAI BEEP")
return True
else:
print("XXXXXXXXXXXXXXXXXFALSE ")
return False
except Exception as e:
print("XXXXXXXXXXXXXXXXXFALSE FALSE" + str(e))
return False
def getFeature(x):
# print("WTFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF"+x[1])
global numberItem
global timeEnd
global timeStart
global depth_retweets
global ratio_retweets
global hashtags
global length
global exclamations
global questions
global links
global topicRepetition
global replies
global spreadVelocity
global user_diversity
global retweeted_user_diversity
global hashtag_diversity
global language_diversity
global vocabulary_diversity
global db
global cursor
# print("XXXXXXXXXXXXX")
# Open database connection
db = MySQLdb.connect("localhost", "htduongdl96", "motorola", "DBtweet")
# prepare a cursor object using cursor() method
cursor = db.cursor()
try:
sql = "use DBtweet"
cursor.execute(sql)
sql = """CREATE TABLE ALL_TWEET_VECTOR (
ID INT NOT NULL AUTO_INCREMENT,
TREND CHAR(200) CHARACTER SET utf8 COLLATE utf8_general_ci,
TIME VARCHAR(30),
DEPTH_RETWEETS FLOAT ,
RATIO_RETWEETS FLOAT ,
HASHTAGS FLOAT ,
LENGTH FLOAT ,
EXCLAMATIONS FLOAT ,
QUESTIONS FLOAT ,
LINKS FLOAT ,
TOPICREPETITION FLOAT ,
REPLIES FLOAT ,
SPREADVELOCITY FLOAT ,
USER_DIVERSITY FLOAT ,
RETWEETED_USER_DIVERSITY FLOAT ,
HASHTAG_DIVERSITY FLOAT ,
LANGUAGE_DIVERSITY FLOAT ,
VOCABULARY_DIVERSITY FLOAT ,
CLASS INT,
CONFIRMED TINYINT(1),
PRIMARY KEY (ID)
)"""
cursor.execute(sql)
sql = """CREATE TABLE TWEET_VECTOR_TRAIN (
ID INT NOT NULL AUTO_INCREMENT,
TREND CHAR(200) CHARACTER SET utf8 COLLATE utf8_general_ci,
TIME VARCHAR(30),
DEPTH_RETWEETS FLOAT ,
RATIO_RETWEETS FLOAT ,
HASHTAGS FLOAT ,
LENGTH FLOAT ,
EXCLAMATIONS FLOAT ,
QUESTIONS FLOAT ,
LINKS FLOAT ,
TOPICREPETITION FLOAT ,
REPLIES FLOAT ,
SPREADVELOCITY FLOAT ,
USER_DIVERSITY FLOAT ,
RETWEETED_USER_DIVERSITY FLOAT ,
HASHTAG_DIVERSITY FLOAT ,
LANGUAGE_DIVERSITY FLOAT ,
VOCABULARY_DIVERSITY FLOAT ,
CLASS INT,
CONFIRMED TINYINT(1),
PRIMARY KEY (ID)
)"""
cursor.execute(sql)
sql = """CREATE TABLE DETAIL_TWEET (
ID INT NOT NULL AUTO_INCREMENT,
ID_TWEET BIGINT,
TREND CHAR(200) CHARACTER SET utf8 COLLATE utf8_general_ci,
PRIMARY KEY (ID))"""
cursor.execute(sql)
except:
pass
#print ('------------' + json.dumps(x, indent = 4) + '----------------')
res = json.loads(x)
# #print('asddddddddddddddddddadsada',res['text'])
print(res)
# print(res['id'])
# try:
# test = { 'userId':res['user']['id'],
# 'tweet': translator.translate(res['text']).text,
# 'retweet_count': res['retweet_count'],
# 'arr_hashtags': res['entities']['hashtags'],
# 'links': len(res['entities']['urls']),
# 'isReplies': isReplies(res['in_reply_to_status_id']),
# 'created': res['created_at'],
# 'lang': res['lang']}
# except:
# return
# pass
trend = checkTrend("trends.txt", res['tweet'])
if trend == "a":
return
timeStartTweet = res['created']
stringTime = timeStartTweet[4:7] + ' ' + timeStartTweet[8:10] + ' ' + timeStartTweet[-4:] + ' ' + timeStartTweet[11:13] + ':' + timeStartTweet[14:16] + ':' + timeStartTweet[
17:19]
##print(stringTime)
datetime_object = datetime.strptime(stringTime, '%b %d %Y %H:%M:%S')
ID = getIDFromDB(trend,datetime_object,"ALL_TWEET_VECTOR")
# print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXx" + ID)
loadDataFromFile(ID)
numberItem = numberItem + 1
###print('------------' + json.dumps(test, indent = 4) + '------------')
#Return feature
# 2
tweetJson = res
###print(json.dumps(test, indent = 4))
###print (tweetJson)
if tweetJson['retweet_count']>0 :
##print('--------------------------------1-----' + str(depth_retweets))
depth_retweets=depth_retweets+1
##print('--------------------------------1-----' + str(depth_retweets))
if tweetJson['retweet_count']>0 :
##print('--------------------------------2-----' + str(ratio_retweets))
ratio_retweets=ratio_retweets+1
##print('--------------------------------2-----' + str(ratio_retweets))
hashtags+=len(tweetJson['arr_hashtags'])
length+=len(tweetJson['tweet'])
if '!' in tweetJson['tweet']:
##print('--------------------------------3-----' + str(exclamations))
exclamations=exclamations+1
##print('--------------------------------3-----' + str(exclamations))
if '?' in tweetJson['tweet']:
##print('--------------------------------4-----' + str(questions))
questions=questions+1
##print('--------------------------------4-----' + str(questions))
links+=tweetJson['links']
##print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"+ str(tweetJson['userId']))
topicRepetition += tweetJson['tweet'].lower().count(trend.lower().replace("#",""))
if tweetJson['isReplies']>0:
replies+=1
increaseBag(tweetJson['userId'], user_diversity)
if tweetJson['retweet_count']>0:
increaseBag(tweetJson['userId'], retweeted_user_diversity)
for hashtag in tweetJson['arr_hashtags']:
increaseBag(hashtag['text'], hashtag_diversity)
increaseBag(tweetJson['lang'], language_diversity)
print("12313132")
try:
tweetJson['tweet'] = translator.translate(tweetJson['tweet']).text
except:
pass
print("1312444123")
newBag = [w.lower() for w in tweetJson['tweet'].split()]
if len(newBag)>0:
for word in newBag:
increaseBag(word, vocabulary_diversity)
time = tweetJson['created']
#Sun Apr 29 11:03:32 +0000 2018
#print("####################################################################")
#print(time)
##print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
##print(time)
stringTime = time[4:7]+' '+time[8:10]+' '+time[-4:]+' '+time[11:13]+':'+time[14:16]+':'+time[17:19]
##print(stringTime)
datetime_object = datetime.strptime(stringTime, '%b %d %Y %H:%M:%S')
if datetime_object > timeEnd:
timeEnd = datetime_object
if datetime_object < timeStart:
timeStart = datetime_object
##print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" + stringTime)
spreadVelocity = timeEnd - timeStart
depth_retweets= depth_retweets/numberItem
ratio_retweets= ratio_retweets/numberItem
hashtags= hashtags/numberItem
length= length/numberItem
exclamations= exclamations/numberItem
questions= questions/numberItem
links= links/numberItem
##print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
topicRepetition= topicRepetition/numberItem
replies= replies/numberItem
spreadVelocity= spreadVelocity.total_seconds()/numberItem
user_diversity1= calShannon(user_diversity)
retweeted_user_diversity1= calShannon(retweeted_user_diversity)
hashtag_diversity1= calShannon(hashtag_diversity)
language_diversity1= calShannon(language_diversity)
vocabulary_diversity1= calShannon(vocabulary_diversity)
sql = "INSERT INTO DETAIL_TWEET(ID_TWEET,TREND) VALUES (%d, '%s')" % (int(res["id"]), trend)
sql.encode('utf-8')
print(sql)
try:
cursor.execute(sql)
except:
pass
#print ("OMGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG" + str(cursor.lastrowid))
##print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
vector = []
vector.append(depth_retweets)
vector.append(ratio_retweets)
vector.append(hashtags)
vector.append(length)
vector.append(exclamations)
vector.append(questions)
vector.append(links)
vector.append(topicRepetition)
vector.append(replies)
vector.append(spreadVelocity)
vector.append(user_diversity1)
vector.append(retweeted_user_diversity1)
vector.append(hashtag_diversity1)
vector.append(language_diversity1)
vector.append(vocabulary_diversity1)
print(trend)
if numberItem > 50:
Class = predictTrend(vector,trend,timeStart)
else:
Class = -1
addNewTrend("ALL_TWEET_VECTOR",trend,depth_retweets,ratio_retweets,hashtags,
length,exclamations,questions,
links,topicRepetition,replies,
spreadVelocity,user_diversity1,
retweeted_user_diversity1,hashtag_diversity1,
language_diversity1,vocabulary_diversity1,Class, timeStart)
saveDataToFile(ID)
return [str(depth_retweets),str(ratio_retweets),str(hashtags),
str(length),str(exclamations),str(questions),
str(links),str(topicRepetition),str(replies),
str(spreadVelocity),str(user_diversity1),
str(retweeted_user_diversity1),str(hashtag_diversity1),
str(language_diversity1),str(vocabulary_diversity1)]
#return [str(), str(), ]
def exportModel(model,filename):
pickle.dump(model,open(filename,'wb'))
def importModel(filename):
with open(filename, 'rb') as fin:
classifier = pickle.load(fin)
return classifier
#check that input belong to any trend:
def addNewTrend(TableName, trend,depth_retweets,ratio_retweets,hashtags,
length,exclamations,questions,
links,topicRepetition,replies,
spreadVelocity,user_diversity,
retweeted_user_diversity,hashtag_diversity,
language_diversity,vocabulary_diversity,Class, time):
if(getTimeFromDB(getIDFromDB(trend,time,TableName),time,TableName) == False):
sql = "INSERT INTO %s(\
TREND, DEPTH_RETWEETS, RATIO_RETWEETS, HASHTAGS,\
LENGTH, EXCLAMATIONS, QUESTIONS,LINKS ,TOPICREPETITION , REPLIES,\
SPREADVELOCITY ,USER_DIVERSITY ,RETWEETED_USER_DIVERSITY ,HASHTAG_DIVERSITY ,\
LANGUAGE_DIVERSITY, VOCABULARY_DIVERSITY, CLASS, TIME)"\
"VALUES ('%s', %f, %f,\n %f,\
%f, %f, %f, %f, %f, %f, \
%f, %f, %f, %f, \
%f, %f, %d, '%s');" % \
(TableName, trend.rstrip(), depth_retweets, ratio_retweets, hashtags,
length, exclamations, questions,
links, topicRepetition, replies,
spreadVelocity, user_diversity,
retweeted_user_diversity, hashtag_diversity,
language_diversity, vocabulary_diversity, Class, time)
print(sql)
sql = sql.encode("utf-8")
cursor.execute(sql)
db.commit()
else:
updateTrend(TableName,getIDFromDB(trend,time,TableName), depth_retweets,ratio_retweets,hashtags,
length,exclamations,questions,
links,topicRepetition,replies,
spreadVelocity,user_diversity,
retweeted_user_diversity,hashtag_diversity,
language_diversity,vocabulary_diversity,Class)
def updateTrend(TableName,ID,depth_retweets,ratio_retweets,hashtags,
length,exclamations,questions,
links,topicRepetition,replies,
spreadVelocity,user_diversity,
retweeted_user_diversity,hashtag_diversity,
language_diversity,vocabulary_diversity,Class):
sql = "Update %s" \
" set DEPTH_RETWEETS = %f,RATIO_RETWEETS= %f,HASHTAGS= %f, \
LENGTH= %f, EXCLAMATIONS= %f, QUESTIONS = %f,LINKS = %f ,TOPICREPETITION = %f ,REPLIES = %f ,\
SPREADVELOCITY = %f ,USER_DIVERSITY = %f ,RETWEETED_USER_DIVERSITY = %f ,HASHTAG_DIVERSITY = %f,\
LANGUAGE_DIVERSITY= %f, VOCABULARY_DIVERSITY= %f, CLASS = %d " \
"WHERE ID = %d;" \
% (TableName, depth_retweets, ratio_retweets, hashtags,
length, exclamations, questions,
links, topicRepetition, replies,
spreadVelocity, user_diversity,
retweeted_user_diversity, hashtag_diversity,
language_diversity, vocabulary_diversity,Class, ID[0])
print(sql)
sql = sql.encode("utf-8")
cursor.execute(sql)
db.commit()
def updateTrendClass(TableName,ID,Class):
sql = "Update %s" \
"set CLASS = %d" \
"WHERE ID = %d" \
% (TableName, Class, ID)
cursor.execute(sql)
db.commit()
def checkTrend(filename, input):
f = open(filename,"r", encoding='utf-8')
# #print(input)
#print("AAAAAA")
for i in f:
if i.replace("#","").rstrip() in input:
# #print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" + i)
return i
break
return "a"
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import CountVectorizer
def predictTrend(vector,trend, timeStart):
classifier = importModel("model.pkl")
# sc = StandardScaler()
# vector = sc.transform(vector)
# print("DMJMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM" + str(vector))
test = [vector[0], vector[1], vector[2], vector[3],vector[4], vector[5], vector[6], vector[7], vector[8]
, vector[9], vector[10], vector[11], vector[12], vector[13], vector[14]]
class_probabilities = classifier.predict_proba([test])
CLASS = classifier.predict([test])[0]
print("CHO PHUOC XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",(class_probabilities.max(1)))
bagOfWordClassifier = importModel("SVM_BagofWords.pkl")
count_vect = CountVectorizer()
if(class_probabilities.max(1)[0] > 0.8):
if(numberItem > 50):
addNewTrend("ALL_TWEET_VECTOR", trend, vector[0], vector[1], vector[2], vector[3],vector[4], vector[5], vector[6], vector[7], vector[8]
, vector[9], vector[10], vector[11], vector[12], vector[13], vector[14], CLASS, timeStart)
else:
sql = ""
# print(classifier.predict([test]))
return CLASS
def forEachBatch(x):
tweet = x[1].split("\n")
for i in tweet:
try:
getFeature(i)
i = 0
except:
pass
if __name__ == "__main__":
try:
#Create Spark Context to Connect Spark Cluster
sc = SparkContext(appName="PythonStreamingKafkaTweetCount")
#Set the Batch Interval is 2 sec of Streaming Context
ssc = StreamingContext(sc, 2)
# sqlContext = sql.SQLContext(sc)
#Create Kafka Stream to Consume Data Comes From Twitter Topic
#localhost:2181 = Default Zookeeper Consumer Address
kafkaStream = KafkaUtils.createStream(ssc, 'localhost:2181', 'spark-streaming', {'twitter':1})
#Parse Twitter Data as json
parsed = kafkaStream.map(lambda v: forEachBatch(v))
#parsed = kafkaStream.map(lambda x: x[1])
#kafkaStream.saveAsTextFiles('test.txt')
#Count the number of tweets per Usere
#lines = parsed.map(lambda x: x[1])
# tweets = parsed.map(getFeature)
# ##print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
#print('asdljaslkdjaslkjdasjdlkajdlasjkd',len(tweets))
parsed.pprint()
#tweets.saveDataToFile("1")
# vector = np.array(tweets)
#rdd = tweets.foreachRDD(getRDD)
#turnIntoVector(rdd)
# ##print(vector)
#Start Execution of Streams
ssc.start()
ssc.awaitTermination()
except:
pass