def get_sentiment_value(tweets):
data = twitterSentiment.StructureStatusesData(tweets)
structured_data = data.getTweet()
sentiment = twitterSentiment.SentimentScore(structured_data)
sentiment_value = sentiment.getSentimentClassification()
return round(sentiment_value, 2)
def seed_tweets(self, request):
twitter_client_key = config('TWITTER_CLIENT_KEY')
twitter_client_secret = config('TWITTER_CLIENT_SECRET')
os.environ.setdefault("TWITTER_CLIENT_KEY", twitter_client_key)
os.environ.setdefault("TWITTER_CLIENT_SECRET", twitter_client_secret)
api = twitterSentiment.API()
today = date.today()
for i in range(7):
current_day = today - timedelta(days=i)
tweet = api.querySearch(q='covid', geocode=None, lang='en', result_type='recent', count=100,
until=current_day.strftime('%Y-%m-%d'),
since_id=None, max_id=None, include_entities=False, tweet_mode="extended",
return_json=True)
data = twitterSentiment.StructureStatusesData(tweet)
for item in tweet['statuses']:
Tweet.objects.create(id=item['id'], text=item['full_text'], created_at=item['created_at'])
structured_tweets = data.getTweet()
sentiment = twitterSentiment.SentimentScore(structured_tweets)
score = sentiment.getSentimentClassification()
tweet_score_id = int(f'{current_day.day}{current_day.month}{current_day.year}')
TweetScore.objects.create(id=tweet_score_id, tweet_score=score, date=current_day)
return Response(1)
def search(self, request):
nltk.data.path.append("./venv/nltk")
twitter_client_key = config('TWITTER_CLIENT_KEY')
twitter_client_secret = config('TWITTER_CLIENT_SECRET')
os.environ.setdefault("TWITTER_CLIENT_KEY", 'X8CEgZEOMZkHHrHKYy3b5MFZo')
os.environ.setdefault("TWITTER_CLIENT_SECRET", '5FJHXLfWPB18eMFB89ceCjRLX8sCxey9Lt0fRYzf6fBrw0lL2Z')
api = twitterSentiment.API()
today = date.today()
current_day = today
tweet = api.querySearch(q='covid ' + request.query_params['keyword'], geocode=None, lang='en',
result_type='recent', count=1000,
until=current_day.strftime('%Y-%m-%d'),
since_id=None, max_id=None, include_entities=False, tweet_mode="extended",
return_json=True)
data = twitterSentiment.StructureStatusesData(tweet)
structured_tweets = data.getTweet()
tweets = []
for item in structured_tweets[:100]:
tweets.append({'text': item['full_text']})
sentiment = twitterSentiment.SentimentScore(structured_tweets)
score = sentiment.getSentimentClassification()
texts = ''
for tweet in tweets:
texts += tweet['text'] + ' '
tokenizer = RegexpTokenizer(r'\w+')
allWords = tokenizer.tokenize(texts)
allWordDist = nltk.FreqDist(w.lower() for w in allWords)
stopwords = nltk.corpus.stopwords.words('english')
allWordExceptStopDist = nltk.FreqDist(w.lower() for w in allWords if w not in stopwords)
filtered_words = {key: value for key, value in allWordExceptStopDist.items() if value >= 5}
excluded_words = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "0", "15", "23", "19", "a", "the", "it",
"me",
"my", "we", "and", "in", "us", "33", "https", "http", "co", "i", "rt", "get"]
for item in excluded_words:
filtered_words.pop(item, None)
sa = sorted(filtered_words.items(), key=lambda x: x[1], reverse=True)
data_sorted = {k: v for k, v in sorted(filtered_words.items(), key=lambda x: x[1], reverse=True)}
return Response({'tweets': tweets, 'tweet_score': score, 'keys': list(data_sorted.keys())[:20],
'values': list(data_sorted.values())[:20]})
def testGetUser(self):
structured_data = twitterSentiment.StructureStatusesData(self.search)
user = structured_data.getUser()
self.assertEqual(12, len(user[0]))
def testGetTweets(self):
structured_data = twitterSentiment.StructureStatusesData(self.search)
tweet = structured_data.getTweet()
self.assertGreaterEqual(11, len(tweet[0]))
def testGetData(self):
structured_data = twitterSentiment.StructureStatusesData(self.search)
data = structured_data.getData()
self.assertEqual(3, len(data))