def __init__(self, lexPath, docPath):
self.lexicon = util.LoadLexiconFromCSV(lexPath)
self.docPath = docPath
if docPath.endswith("json"):
self.docType = "json"
elif docPath.endswith("xml"):
self.docType = "xml"
else:
self.docType = "json"
self.iterator = self.GetIter()
def predict(self, filePath):
#PREDICT
lexicon = util.LoadLexiconFromCSV(
"../files/lexicons/SentiWordNet_Lexicon_concise.csv")
angel = Angel(lexicon, True)
parsedReviewsPath = os.path.join(os.path.dirname(filePath),
"YelpParsedReviews.json")
with open(parsedReviewsPath, 'r') as file:
TrainingFile = file.read()
classificationData = json.loads(TrainingFile)
for k in range(len(classificationData["ClassificationModel"])):
current = classificationData["ClassificationModel"][str(k + 1)]
notCount = current["NotCount"]
if "Sentences" in current:
if not isinstance(current["Sentences"], list):
current["Sentences"] = [current["Sentences"]]
sentences = current["Sentences"]
else:
continue
current["Label"] = Sentiment.GetSentimentClass(
angel.PredictReviewScore(sentences, notCount), 1)
angel.DumpDetails(sentences, current["Label"])
return classificationData
