def perform_create(self, serializer):
user_referred_id = Token.objects.filter(
key=self.request.headers['Authorization'].split()[1])[0].user.id
if serializer.is_valid():
saved_model = serializer.save(create_by_id=user_referred_id)
default_keywords = DefaultKeyword.objects.all()
num_of_tweets_per_default_keyword = NumOfTweetsPerState.objects.get(
id=1).value
num_of_tweets_per_day = NumOfTweetsPerDay.objects.get(id=1).value
days_of_trending_tweets = DaysOfTrendingTweets.objects.get(
id=1).value
num_of_popular_tweets = NoOfPopularTweets.objects.get(id=1).value
num_of_trending_keywords = NoOfTrendingKeywords.objects.get(
id=1).value
or_def_keywords = add_OR_in_strings(default_keywords)
or_def_keywords1 = or_def_keywords
or_def_keywords2 = or_def_keywords
sentiment_count_per_state = []
for custom_keyword in serializer.data["keywords"]:
or_def_keywords1 = or_def_keywords1 + " " + custom_keyword[
'keyword']
sentiment_count_per_state.append(
statewise_sentiments.state_tweets(
num_of_tweets_per_default_keyword, or_def_keywords1))
datewise_sentiments = []
for custom_keyword in serializer.data["keywords"]:
or_def_keywords2 = or_def_keywords2 + " " + custom_keyword[
'keyword']
datewise_sentiments.append(
statewise_sentiments.get_daily_tweets(or_def_keywords2,
num_of_tweets_per_day,
days_of_trending_tweets))
_trending_tweets = get_trends.popular_tweets(
add_OR_in_strings(default_keywords), num_of_popular_tweets)
_trending_keywords = get_trends.trending_keywords_india(
num_of_trending_keywords)
saved_model.statewise_tweets = json.dumps(
sentiment_count_per_state)
saved_model.datewise_tweets = json.dumps(datewise_sentiments)
saved_model.trending_tweets = json.dumps(_trending_tweets)
saved_model.trending_keywords = json.dumps(_trending_keywords)
saved_model.save()
def retrieve(self, request, *args, **kwargs):
default_keywords = DefaultKeyword.objects.all()
num_of_tweets_per_default_keyword = NumOfTweetsPerState.objects.all(
)[0].value
days_of_trending_tweets = DaysOfTrendingTweets.objects.all()[0].value
num_of_tweets_per_day = NumOfTweetsPerDay.objects.all()[0].value
num_of_popular_tweets = NoOfPopularTweets.objects.all()[0].value
num_of_trending_keywords = NoOfTrendingKeywords.objects.all()[0].value
or_def_keyword = add_OR_in_strings(default_keywords)
sentiment_count_per_state = []
sentiment_count_per_state.append(
statewise_sentiments.state_tweets(
num_of_tweets_per_default_keyword, or_def_keyword))
datewise_sentiments = []
datewise_sentiments.append(
statewise_sentiments.get_daily_tweets(or_def_keyword,
num_of_tweets_per_day,
days_of_trending_tweets))
_total_sentiments = total_sentiments(sentiment_count_per_state)
_total_indivudual_sentiments = total_indivudual_sentiments(
sentiment_count_per_state)
_tone_labels = get_tone_labels(sentiment_count_per_state)
_total_sentiment_count_per_state = total_sentiment_count_per_state(
sentiment_count_per_state)
_total_sentiment_count_per_date = total_sentiment_count_per_date(
datewise_sentiments)
_trending_tweets = get_trends.popular_tweets(
add_OR_in_strings(default_keywords), num_of_popular_tweets)
_trending_keywords = get_trends.trending_keywords_india(
num_of_trending_keywords)
return Response({
'sentiment_count_per_state': sentiment_count_per_state,
'total_sentiments': _total_sentiments,
'total_indivudual_sentiments': _total_indivudual_sentiments,
'tone_labels': _tone_labels,
'total_sentiment_count_per_state':
_total_sentiment_count_per_state,
'total_sentiment_count_per_date': _total_sentiment_count_per_date,
'trending_tweets': _trending_tweets,
'trending_keywords': _trending_keywords
})
def retrieve(self, request, *args, **kwargs):
default_keywords = DefaultKeyword.objects.all()
num_of_tweets_per_default_keyword = NumOfTweetsPerState.objects.get(id=1).value
days_of_trending_tweets = DaysOfTrendingTweets.objects.get(id=1).value
num_of_tweets_per_day = NumOfTweetsPerDay.objects.get(id=1).value
num_of_popular_tweets = NoOfPopularTweets.objects.get(id=1).value
num_of_trending_keywords = NoOfTrendingKeywords.objects.get(id=1).value
or_def_keyword = add_OR_in_strings(default_keywords)
sentiment_count_per_state = []
sentiment_count_per_state.append(statewise_sentiments.state_tweets(num_of_tweets_per_default_keyword, or_def_keyword))
# try:
# for default_keyword in default_keywords:
# sentiment_count_per_state.append(statewise_sentiments.state_tweets(num_of_tweets_per_default_keyword, default_keyword.keyword))
# except Exception:
# print()
# print()
# print()
# print('in sentiment_count_per_state exception')
# print()
# print()
# print()
# sentiment_count_per_state = [
# { 'Andhra Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 0, 'analytical': 3, 'confident': 0, 'tentative': 0 }, 'Arunachal Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 4, 'analytical': 1, 'confident': 0, 'tentative': 1 }, 'Assam': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 1, 'confident': 1, 'tentative': 1 }, 'Bihar': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 0, 'analytical': 1, 'confident': 0, 'tentative': 1 }, 'Chandigarh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 1, 'confident': 1, 'tentative': 1 }, 'Chhattisgarh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Daman and Diu': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 1, 'confident': 0, 'tentative': 1 }, 'Delhi': { 'anger': 0, 'disgust': 0, 'fear': 2, 'joy': 2, 'sadness': 0, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Goa': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 3, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Gujarat': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Haryana': { 'anger': 0, 'disgust': 0, 'fear': 3, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Himachal Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 3, 'sadness': 1, 'analytical': 0, 'confident': 1, 'tentative': 2 }, 'Jammu and Kashmir': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Jharkhand': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 2, 'analytical': 3, 'confident': 0, 'tentative': 5 }, 'Karnataka': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 3, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Kerala': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Ladakh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 2, 'analytical': 1, 'confident': 1, 'tentative': 2 }, 'Lakshadweep': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 2, 'analytical': 1, 'confident': 2, 'tentative': 1 }, 'Madhya Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 0, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Maharashtra': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 1 }, 'Manipur': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 5 }, 'Meghalaya': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 2, 'confident': 1, 'tentative': 2 }, 'Mizoram': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Nagaland': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 2, 'confident': 0, 'tentative': 3 }, 'Odisha': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 2, 'analytical': 1, 'confident': 1, 'tentative': 0 }, 'Puducherry': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 4, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Punjab': { 'anger': 0, 'disgust': 0, 'fear': 1, 'joy': 1, 'sadness': 0, 'analytical': 1, 'confident': 1, 'tentative': 1 }, 'Rajasthan': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 5, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Sikkim': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 3, 'sadness': 0, 'analytical': 1, 'confident': 1, 'tentative': 2 }, 'Tamil Nadu': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 3, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Telangana': { 'anger': 1, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 1, 'confident': 1, 'tentative': 0 }, 'Tripura': { 'anger': 0, 'disgust': 0, 'fear': 1, 'joy': 1, 'sadness': 1, 'analytical': 3, 'confident': 0, 'tentative': 1 }, 'Uttar Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 0, 'confident': 5, 'tentative': 0 }, 'Uttarakhand': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 2 }, 'West Bengal': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 1, 'confident': 1, 'tentative': 0 } },
# { 'Andhra Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 0, 'analytical': 3, 'confident': 0, 'tentative': 0 }, 'Arunachal Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 4, 'analytical': 1, 'confident': 0, 'tentative': 1 }, 'Assam': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 1, 'confident': 1, 'tentative': 1 }, 'Bihar': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 0, 'analytical': 1, 'confident': 0, 'tentative': 1 }, 'Chandigarh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 1, 'confident': 1, 'tentative': 1 }, 'Chhattisgarh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Daman and Diu': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 1, 'confident': 0, 'tentative': 1 }, 'Delhi': { 'anger': 0, 'disgust': 0, 'fear': 2, 'joy': 2, 'sadness': 0, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Goa': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 3, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Gujarat': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Haryana': { 'anger': 0, 'disgust': 0, 'fear': 3, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Himachal Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 3, 'sadness': 1, 'analytical': 0, 'confident': 1, 'tentative': 2 }, 'Jammu and Kashmir': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Jharkhand': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 2, 'analytical': 3, 'confident': 0, 'tentative': 5 }, 'Karnataka': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 3, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Kerala': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Ladakh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 2, 'analytical': 1, 'confident': 1, 'tentative': 2 }, 'Lakshadweep': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 2, 'analytical': 1, 'confident': 2, 'tentative': 1 }, 'Madhya Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 0, 'analytical': 0, 'confident': 3, 'tentative': 0 }, 'Maharashtra': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 1 }, 'Manipur': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 5 }, 'Meghalaya': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 2, 'confident': 1, 'tentative': 2 }, 'Mizoram': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Nagaland': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 2, 'confident': 0, 'tentative': 3 }, 'Odisha': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 2, 'analytical': 1, 'confident': 1, 'tentative': 0 }, 'Puducherry': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 4, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Punjab': { 'anger': 0, 'disgust': 0, 'fear': 1, 'joy': 1, 'sadness': 0, 'analytical': 1, 'confident': 1, 'tentative': 1 }, 'Rajasthan': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 5, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Sikkim': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 3, 'sadness': 0, 'analytical': 1, 'confident': 1, 'tentative': 2 }, 'Tamil Nadu': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 3, 'sadness': 0, 'analytical': 0, 'confident': 0, 'tentative': 0 }, 'Telangana': { 'anger': 1, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 1, 'confident': 1, 'tentative': 0 }, 'Tripura': { 'anger': 0, 'disgust': 0, 'fear': 1, 'joy': 1, 'sadness': 1, 'analytical': 3, 'confident': 0, 'tentative': 1 }, 'Uttar Pradesh': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 0, 'confident': 5, 'tentative': 0 }, 'Uttarakhand': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 2 }, 'West Bengal': { 'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 1, 'analytical': 1, 'confident': 1, 'tentative': 0 } },
# ]
#
datewise_sentiments = []
datewise_sentiments.append(statewise_sentiments.get_daily_tweets(or_def_keyword, num_of_tweets_per_day, days_of_trending_tweets))
# try:
# for default_keyword in default_keywords:
# datewise_sentiments.append(statewise_sentiments.get_daily_tweets(default_keyword.keyword, num_of_tweets_per_default_keyword, days_of_trending_tweets))
# except Exception:
# print()
# print()
# print()
# print('in datewise_sentiments exception')
# print()
# print()
# print()
# datewise_sentiments = [
# {'02-07-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 1}, '01-07-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 3, 'analytical': 0, 'confident': 2, 'tentative': 1}, '30-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 4, 'sadness': 0, 'analytical': 0, 'confident': 4, 'tentative': 1}, '29-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 0, 'confident': 1, 'tentative': 2}, '28-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 1, 'tentative': 0}, '27-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 0, 'confident': 1, 'tentative': 0}, '26-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 1, 'analytical': 0, 'confident': 1, 'tentative': 0}},
# {'02-07-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 0, 'tentative': 1}, '01-07-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 3, 'analytical': 0, 'confident': 2, 'tentative': 1}, '30-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 4, 'sadness': 0, 'analytical': 0, 'confident': 4, 'tentative': 1}, '29-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 0, 'analytical': 0, 'confident': 1, 'tentative': 2}, '28-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 2, 'sadness': 1, 'analytical': 0, 'confident': 1, 'tentative': 0}, '27-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'analytical': 0, 'confident': 1, 'tentative': 0}, '26-06-2020': {'anger': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'sadness': 1, 'analytical': 0, 'confident': 1, 'tentative': 0}},
# ]
#
_total_sentiments = total_sentiments(sentiment_count_per_state)
_total_indivudual_sentiments = total_indivudual_sentiments(sentiment_count_per_state)
_tone_labels = get_tone_labels(sentiment_count_per_state)
_total_sentiment_count_per_state = total_sentiment_count_per_state(sentiment_count_per_state)
_total_sentiment_count_per_date = total_sentiment_count_per_date(datewise_sentiments)
_trending_tweets = get_trends.popular_tweets(add_OR_in_strings(default_keywords), num_of_popular_tweets)
_trending_keywords = get_trends.trending_keywords_india(num_of_trending_keywords)
return Response({
'sentiment_count_per_state': sentiment_count_per_state,
'total_sentiments': _total_sentiments,
'total_indivudual_sentiments': _total_indivudual_sentiments,
'tone_labels': _tone_labels,
'total_sentiment_count_per_state': _total_sentiment_count_per_state,
'total_sentiment_count_per_date': _total_sentiment_count_per_date,
'trending_tweets': _trending_tweets,
'trending_keywords': _trending_keywords
})