def sentimentsAnalysisSingleTweetUsingNaiveBayes():
#Get the tweet from UI
mdiProjectTweet = request.form.get("tweet")
#Process the tweet
porcessedTweet = mdiProjectProcessTweet(mdiProjectTweet)
#Get all the stop words
stopWordList = mdiProjectStopWordList(
mdiProjectReadFiles("mdiProjectFiles", "StopWords.txt"))
#Get the features vector for a single tweet
featureVector = mdiProjectGetFeatureVectorForSingleTweet(
porcessedTweet, stopWordList)
#Get the feacture words
featureWords = mdiProjectExtractFeatures(featureVector)
#Get sentiments based on the feature words
tweetSentiment = NaiveBayesClassifierModel.classify(featureWords)
return render_template(
"naiveByesSingleTweet.html",
prediction_text="Sentiment of the tweet is {}".format(tweetSentiment))
def sentimentsAnalysisSingleTweetUsingNaiveBayes():
""" Predict sentiments of a tweet
---
produces:
- "application/xml"
- "application/json"
parameters:
- name: tweet
in: query
type: string
required: true
responses:
content:
application/json:
schema:
type: object
"""
#Open NaiveBayesClassifier picke file
with open("NaiveBayesClassifierModel.pkl", "rb") as NaiveBayesClassifier:
NaiveBayesClassifierModel = pickle.load(NaiveBayesClassifier)
#Get the tweet from UI
mdiProjectTweet = request.args.get("tweet")
#Process the tweet
porcessedTweet = mdiProjectProcessTweet(mdiProjectTweet)
#Get all the stop words
stopWordList = mdiProjectStopWordList(
mdiProjectReadFiles("mdiProjectFiles", "StopWords.txt"))
#Get the features vector for a single tweet
featureVector = mdiProjectGetFeatureVectorForSingleTweet(
porcessedTweet, stopWordList)
#Get the feacture words
featureWords = mdiProjectExtractFeatures(featureVector)
#Get sentiments based on the feature words
tweetSentiment = NaiveBayesClassifierModel.classify(featureWords)
return tweetSentiment
def naiveBayesClassifierTestModel():
mdiProjectSampleTweetList = mdiProjectReadSampleFile(
mdiProjectReadFiles("mdiProjectFiles", "SampleTrainingData.csv"))
mdiProjectTrainingSet, mdiProjectTestingSet = mdiProjectSplitTrainTest(
mdiProjectSampleTweetList)
mdiProjectStopWordsList = mdiProjectStopWordList(
mdiProjectReadFiles("mdiProjectFiles", "StopWords.txt"))
#mdiProjectTestingSet = mdiProjectTestingSet[1:10]
mdiProjectActualSentiments = []
mdiProjectPredictedSentiments = []
#Open NaiveBayesClassifier picke file
with open("NaiveBayesClassifierModel.pkl", "rb") as NaiveBayesClassifier:
NaiveBayesClassifierModel = pickle.load(NaiveBayesClassifier)
for mdiProjectTestingTweetRow in mdiProjectTestingSet:
mdiProjectSentiment = mdiProjectTestingTweetRow[0]
mdiProjectTweet = mdiProjectTestingTweetRow[1]
#Process the tweet
porcessedTweet = mdiProjectProcessTweet(mdiProjectTweet)
#Get the features vector for a single tweet
featureVector = mdiProjectGetFeatureVectorForSingleTweet(
porcessedTweet, mdiProjectStopWordsList)
#Get the feacture words
featureWords = mdiProjectExtractFeatures(featureVector)
#Get sentiments based on the feature words
tweetSentiment = NaiveBayesClassifierModel.classify(featureWords)
mdiProjectActualSentiments.append(mdiProjectSentiment)
mdiProjectPredictedSentiments.append(tweetSentiment)
return mdiProjectActualSentiments, mdiProjectPredictedSentiments
def sentimentsAnalysisBasedOnTweetTopicUsingNaiveBayes():
""" Predict sentiments of a tweet
---
parameters:
- name: tweetTopic
in: query
type: string
required: true
responses:
content:
application/json:
schema:
type: object
"""
#Open NaiveBayesClassifier picke file
with open("NaiveBayesClassifierModel.pkl", "rb") as NaiveBayesClassifier:
NaiveBayesClassifierModel = pickle.load(NaiveBayesClassifier)
#Get the tweet topic
mdiProjectTweetTopic = request.args.get("tweetTopic")
#Get the tweets based on the tweet topic and save in file
mdiProjectSearchTweetBasedOnSearchTerm(
mdiProjectTweeterAuthHandler(
mdiProjectGetNormalizedTweeterConfig(
"mdiProjectFiles/tweeterConfig.json")), mdiProjectTweetTopic)
#Process saves tweets json file
mdiProjectTweetTextList, mdiProjectTweetText = mdiProjectProcessTweetJsonFile(
"mdiProjectTweets.json")
#Get all the stop words
stopWordList = mdiProjectStopWordList(
mdiProjectReadFiles("mdiProjectFiles", "StopWords.txt"))
sentiments = []
for mdiProjectTweet in mdiProjectTweetTextList:
#Process the tweet
porcessedTweet = mdiProjectProcessTweet(mdiProjectTweet)
#Get the features vector for a single tweet
featureVector = mdiProjectGetFeatureVectorForSingleTweet(
porcessedTweet, stopWordList)
#Get the feacture words
featureWords = mdiProjectExtractFeatures(featureVector)
#Get sentiments based on the feature words
tweetSentiment = NaiveBayesClassifierModel.classify(featureWords)
tweetSentiment = tweetSentiment.replace("\"", "")
sentiments.append(tweetSentiment)
mdiProjectTweetText["sentiments"] = sentiments
#Makes an excel file with sentimets and make it downloadable
mdiProjectOutput = BytesIO()
mdiProjectOutputWriter = pd.ExcelWriter(mdiProjectOutput,
engine="xlsxwriter")
mdiProjectTweetText.to_excel(mdiProjectOutputWriter,
sheet_name="twitter_sentiments",
encoding="utf-8",
index=False)
mdiProjectOutputWriter.save()
memory_file = BytesIO()
with zipfile.ZipFile(memory_file, 'w') as zf:
names = ['twitter_sentiments.xlsx']
files = [mdiProjectOutput]
for i in range(len(files)):
data = zipfile.ZipInfo(names[i])
data.date_time = time.localtime(time.time())[:6]
data.compress_type = zipfile.ZIP_DEFLATED
zf.writestr(data, files[i].getvalue())
memory_file.seek(0)
response = make_response(
send_file(memory_file,
attachment_filename='twitter_sentiments.zip',
as_attachment=True))
response.headers['Access-Control-Allow-Origin'] = '*'
return response
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 21 12:33:25 2019
@author: Santosh Sah
"""
import nltk
from MDIProjectSentimentAnalysis import (mdiProjectExtractFeatures, mdiProjectGetFeatureVectorForSingleTweet, mdiProjectGetFeatureListAndTweetListForTrainingSet,
mdiProjectProcessTweet, mdiProjectReplaceTwoOrMore, mdiProjectStopWordList)
testTweet = "Old Quarter is the best place to visit in Hanoi.. excellent and awesome ambience"
mdiProjectStopWordsList = mdiProjectStopWordList("files/StopWords.txt")
mdiProcessTweet = mdiProjectProcessTweet(testTweet)
mdiProjectGetFeatureVectorList = mdiProjectGetFeatureVectorForSingleTweet(mdiProcessTweet,mdiProjectStopWordsList)
feature_words = mdiProjectExtractFeatures(mdiProjectGetFeatureVectorList)