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154#!/usr/bin/python
import pandas as pd
import numpy as np
import random
import sys
def main():
# initialize global variables
disable_pandas_warnings()
getUserInputFile()
setWeight()
setIdx()
random.seed()
# calculate sentiment for galaxy
galaxySentIdx = ['samsunggalaxy']
outputColLabelGal = ['galaxySentiment']
galaxySentFactors = ['samsungcampos', 'samsungcamneg', 'samsungcamunc',
'samsungdispos', 'samsungdisneg', 'samsungdisunc', 'samsungperpos',
'samsungperneg', 'samsungperunc', 'googleperpos', 'googleperneg',
'googleperunc']
dfGalaxy = roboLabel(galaxySentIdx, galaxySentFactors, outputColLabelGal).ix[:,outputColLabelGal[0]]
# calculate sentiment for iphone
iphoneSentIdx = ['iphone']
outputColLabelIp = ['iphoneSentiment']
iphoneSentFactors = ['iphonecampos', 'iphonecamneg', 'iphonecamunc',
'iphonedispos', 'iphonedisneg', 'iphonedisunc', 'iphoneperpos',
'iphoneperneg', 'iphoneperunc', 'iosperpos', 'iosperneg',
'iosperunc']
dfIphone = roboLabel(iphoneSentIdx, iphoneSentFactors, outputColLabelIp).ix[:,outputColLabelIp[0]]
# concatenate galaxy and iphone dataframes, write to file
sentiment = pd.concat([dfGalaxy, dfIphone], axis=1)
sentiment.index.name = 'id'
sentiment.to_csv('./sentiment.csv')
print "All tasks successful, output file is \'sentiment.csv\'"
sys.exit()
def getUserInputFile():
# read input from user, file is global
global file
file = raw_input("Enter input file (leave blank for './concatenated_factors'): ")
if file == "":
file = "./concatenated_factors.csv"
return file
def roboLabel(sentIdx, colnames, sentimentColName):
# input target device (sentIdx), features (colnames), and resultant
# sentiment column header (sentimentColName), returns a dataframe
# appended with robo-labeled sentiment. Positive values are positive
# sentiment, negative values are negative. Minor noise is added.
# Note that the value of sentIdx must be non-zero for a sentiment result
# to be returned.
df = pd.read_csv(file, delimiter=',')
colIndiciesToParse = []
for i in range(0,len(colnames)):
colIndiciesToParse.append(colIndex.get(colnames[i]))
for i, j, k in zip(colIndiciesToParse, range(0,len(colIndiciesToParse)), colnames):
if j == 0:
df[sentimentColName[0]] = df.ix[:,i] * getWeight(k)
if j > 0:
df[sentimentColName[0]] = df[sentimentColName[0]] + (df.ix[:,i] * getWeight(k))
# zero sentiment values where sentIdx == 0 (nobody talked about the device), add noise
for i, j, k in zip(df[sentimentColName[0]], df.ix[:,colIndex.get(sentIdx[0])], df.ix[:,0]):
if j == 0:
# following line should get fixed in future version to resolve warning
df.iloc[k][sentimentColName[0]] = 0
else:
df.iloc[k][sentimentColName[0]] = df.iloc[k][sentimentColName[0]] + random.randrange(-2,2,1)
return df
def getIdx(colname):
# input string column name, returns dataframe col index num
return colIndex[colname]
def setIdx():
# creates dictionary of key/value of column name/column index
df = pd.read_csv(file, delimiter=',')
global colIndex
colIndex = {}
for i, j in zip(df.columns.values, range(0,len(df.columns.values))):
colIndex[i] = j
def getWeight(colname):
# input string column name, returns the weight applied to the sentiment
return weightDict.get(colname)
def setWeight():
# assigns weights to factors within feature matrix, this is configurable
df = pd.read_csv(file, delimiter=',')
#initialize column indicies, set all weights to 0
weightList = [0] * len(df.columns.values)
global weightDict
weightDict = {}
for i, j in zip(df.columns.values, weightList):
weightDict[i] = j
# device
weightDict['iphone'] = 0
weightDict['samsunggalaxy'] = 0
# OS
weightDict['ios'] = 0
weightDict['googleandroid'] = 0
# camera
weightDict['iphonecampos'] = 10
weightDict['samsungcampos'] = 10
weightDict['iphonecamneg'] = -10
weightDict['samsungcamneg'] = -10
weightDict['iphonecamunc'] = 1
weightDict['samsungcamunc'] = 1
# display
weightDict['iphonedispos'] = 10
weightDict['samsungdispos'] = 10
weightDict['iphonedisneg'] = -10
weightDict['samsungdisneg'] = -10
weightDict['iphonedisunc'] = 1
weightDict['samsungdisunc'] = 1
# device performance
weightDict['iphoneperpos'] = 10
weightDict['samsungperpos'] = 10
weightDict['iphoneperneg'] = -10
weightDict['samsungperneg'] = -10
weightDict['iphoneperunc'] = 1
weightDict['samsungperunc'] = 1
# OS performance
weightDict['iosperpos'] = 10
weightDict['googleperpos'] = 10
weightDict['iosperneg'] = -10
weightDict['googleperneg'] = -10
weightDict['iosperunc'] = 1
weightDict['googleperunc'] = 1
def disable_pandas_warnings():
# disable warning raised in roboLabel function (not an issue)
import warnings
warnings.filterwarnings('ignore')
if __name__ == "__main__":
main()