def queryGenerator(raw_input_string, change_sentiment):
#
#Step 0: Seperate if user is asking for meaning or for a debate response
#
word, isMeaning = MeaningExtractor.getIfMeaning(str(raw_input_string))
if isMeaning:
meaning = MeaningExtractor.getMeaning(word)
return meaning, True
#
#Step 1: Obtain input from the user
#
s = str(raw_input_string)
#
#Step 2: Convert the sentence into blob and MBSP Sentence objects respectively
#
input_string = TextBlob(s)
clipsSentence = MBSP.Sentence(MBSP.parse(s), token=[MBSP.WORD,MBSP.POS,MBSP.CHUNK,MBSP.PNP,MBSP.REL,MBSP.ANCHOR,MBSP.LEMMA])
#
#Step 3: Define the variable required for the analysis and interpretation of input
#
query = ""
w = []
subjPhrases, verbPhrases, predPhrases = [], [], []
pNouns, verbs = [], []
pnps, anchors = [], []
#
#Step 4: Obtain all the proper nouns from the sentence
#
for sentence in input_string.sentences:
tagged = sentence.tags
for word_tag in tagged:
if word_tag[1]=='NNP' or word_tag[1]=='NNPS':
pNouns.append(word_tag[0])
#
#Step 5: Obtain the different parts, i.e. subject, predicate, object of the sentence
#
for chunk in clipsSentence.chunks:
if chunk.role == 'SBJ' and chunk not in subjPhrases:
subjPhrases.append(chunk.string)
elif (chunk.type == 'VP' or chunk.type == 'ADVP') and chunk not in verbPhrases:
verbPhrases.append(chunk.string)
elif (chunk.role == 'PRD' or chunk.role == 'OBJ') and chunk not in predPhrases:
predPhrases.append(chunk.string)
#
#Step 6: Detect the noun phrase and the anchors corresponding to them(Ref:CLIPS docs)
#
pnps = clipsSentence.pnp
for item in pnps:
if item.anchor not in anchors:
anchors.append(item.anchor)
#
#Step 7.0: Train the classifier for sentiment data
#
###with open('sentiment_training_formatted.csv', 'r') as fp:
### classifier = NaiveBayesClassifier(fp, format='csv')
#
#Step 7.1: Classify the user input and record the sentiment
#
'''def getSentiment(sentence):
sentiObj= TextBlob(s, analyzer=NaiveBayesAnalyzer()).sentiment
pos_ratio = sentiObj.p_pos
neg_ratio = sentiObj.p_neg
if pos_ratio>=neg_ratio:
return 'pos'
else:
return 'neg'
input_sentiment = getSentiment(input_string)'''
#
#Step 8: Generate the final query
#
for pNoun in pNouns:
for sbj in subjPhrases:
if pNoun not in subjPhrases:
query = query+pNoun+" "
#print 'pNoun:'+pNoun
for sbj in subjPhrases:
query = query+sbj+" "
#print 'sbj:'+sbj
for prd in predPhrases:
query = query+prd+" "
#print 'prd:'+prd
for vr in verbPhrases:
query = query+vr+" "
#print 'vr:'+vr
for anc in anchors:
anc = anc.string
query = query+anc+" "
#print 'anc:'+anc
for pnp in pnps:
pnp = pnp.string
query = query+pnp+" "
#print 'pnp:'+pnp
#
#Step 9: [Blank]
#
#
#Step 10: Remove repetitive words from the sentence
#
query_blob = TextBlob(query)
wrds = query_blob.words
final_words = []
for wrd in wrds:
#print wrd
if str(wrd).lower() not in final_words:
final_words.append(wrd)
final_query = ""
for wrd in final_words:
final_query = final_query+wrd+" "
#
#Step 11: Sort the query words in order of the input
#
index_dict = {}
indexes = []
for word in TextBlob(final_query).words:
try:
index_dict[input_string.index(str(word))] = str(word)
indexes.append(input_string.index(str(word)))
except(ValueError):
print "Word not in main string:", word
indexes.sort()
final_query = ""
for index in indexes:
final_query = final_query+index_dict[index]+" "
#
#Step 12.0: Filter query for articles: a,an,the,is
#
reps = {' a ':' ', ' an ':' ', ' the ':' ', ' is ':' '}
query = replace_all(query, reps)
if change_sentiment:
#
#Step 12.1: Build a dictionary of the replaceable words
#
#Note:make priority lists for different set of words
def create_replace_dict(lines):
replace_dict = {}
for line in lines:
kv = line.split(',')
replace_dict[kv[0]] = kv[1][:-1]
return replace_dict
#
#Step 12.2: Replace words from the given phrase
#
def replace_words(phrase):
l = open('replace_list.csv', 'r').readlines()
new_phrase = replace_all(phrase, create_replace_dict(l))
if new_phrase==phrase:
l=open('replace_list2.csv', 'r').readlines()
new_phrase = replace_all(phrase, create_replace_dict(l))
return new_phrase
final_query = replace_words(final_query)
#
#Step 13: Get the sentiment of the final query
#
'''final_query_sentiment = getSentiment(final_query)'''
#
#Step 14: Print out the query
#
print "<------------------------------------------->"
print "in:", s
print "out:", final_query
print "<------------------------------------------->"
return final_query, False
else:
print "<------------------------------------------->"
print "in:", s
print "out:", s
print "<------------------------------------------->"
return s, False
