In [4]:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from pymongo import MongoClient
from geopy.geocoders import Nominatim
locate = Nominatim()
client = MongoClient()
db = client.twitterCleanDb
tweetCollection = db.tweets
times = []
polarities = []
subjectivities = []
locations = []
locationPolarities = []
lats = []
longs = []
modiTweets = tweetCollection.find({'cleantest' :{'$regex': '.*modi*.'}})
for tweet in modiTweets:
times.append(tweet['created_at'])
polarities.append(tweet['polarity'])
subjectivities.append(tweet['subjectivity'])
latlong = locate.geocode(tweet['user']['location'])
if latlong is None:
latlong = locate.geocode(tweet['user']['time_zone'])
if latlong is None:
locations.append(["p", "p"])
print('appended')
else:
locations.append([latlong.latitude, latlong.longitude])
print('appended 1', latlong, latlong.latitude, latlong.longitude)
locationPolarities.append(tweet['polarity'])
else:
locations.append([latlong.latitude, latlong.longitude])
print('appended 2', latlong, latlong.latitude, latlong.longitude)
locationPolarities.append(tweet['polarity'])
In [5]:
import dateutil
df = pd.DataFrame({'time':times, 'polarity': polarities, 'subjectivity': subjectivities})
df = df.sort_values(by=['time'])
df['time'] = df['time'].apply(dateutil.parser.parse)
df.dtypes
Out[5]:
In [20]:
plt.figure(figsize = (12, 5))
plt.title('Sentiment Comparision')
plt.plot(df['time'], df['polarity'])
plt.plot(df['time'], df['subjectivity'])
plt.xlabel('Time')
plt.ylabel('Sentiment Value')
Out[20]:
In [11]:
lats = [lat[0] for lat in locations]
longi = [long[1] for long in locations]
print(lats, " + ", longi)
In [22]:
import mplleaflet
mapdf = pd.DataFrame({'lat': lats, 'longi': longi, 'polarity':polarities[:len(lats)]})
plt.figure(figsize=(8, 8))
plt.scatter(longi, lats, c = polarities[:len(lats)])
mplleaflet.display()
Out[22]: