Emotional Text Analysis

Statistical text analysis for emotional extraction and some generalizations

Alberto Barradas

@abcsds

Emotion, and behaviour.

There are several theories of emotion, all indicate that emotions are a precursor of behaviour.

Models of Emotion

Affect

Most used model for text analysis. Maps into 2D plane. Cannot map back into emotions.

Bio-chemical

Very precise, closest to biochemical definition of emotions. Not practical.

Ekman's model of emotion

Widely used in face recognition programs. Widely accepted. Requires face-recognition software, thus very costly.

Functional

Based on etological observations. Superset of Ekman's model. http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm

The dictionary

In [19]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import string
%matplotlib inline
In [2]:
emotions = ["anger",
            "anticipation",
            "disgust",
            "fear",
            "joy",
            "negative",
            "positive",
            "sadness",
            "surprise",
            "trust"]
df = pd.read_csv("dict.csv")
df.head(10)
Out[2]:
Word anger anticipation disgust fear joy negative positive sadness surprise trust
0 aback 0 0 0 0 0 0 0 0 0 0
1 abacus 0 0 0 0 0 0 0 0 0 1
2 abandon 0 0 0 1 0 1 0 1 0 0
3 abandoned 1 0 0 1 0 1 0 1 0 0
4 abandonment 1 0 0 1 0 1 0 1 1 0
5 abate 0 0 0 0 0 0 0 0 0 0
6 abatement 0 0 0 0 0 0 0 0 0 0
7 abba 0 0 0 0 0 0 1 0 0 0
8 abbot 0 0 0 0 0 0 0 0 0 1
9 abbreviate 0 0 0 0 0 0 0 0 0 0
In [3]:
df[emotions].sum()
Out[3]:
anger           1247
anticipation     839
disgust         1058
fear            1476
joy              689
negative        3324
positive        2312
sadness         1191
surprise         534
trust           1231
dtype: int64
In [15]:
print(len(df))
df[emotions].sum()/len(df)*100
14181
Out[15]:
anger            8.793456
anticipation     5.916367
disgust          7.460687
fear            10.408293
joy              4.858614
negative        23.439814
positive        16.303505
sadness          8.398561
surprise         3.765602
trust            8.680629
dtype: float64
In [30]:
fig = plt.figure(figsize=(16, 10))
y_pos = np.arange(len(emotions))
percentage = df[emotions].sum()/len(df)*100
percentage = percentage.tolist()

ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
colors = ["#007C37","#79BF2A","#EBC527","#E66F11","#DB1245","#0C2C30","#F53022","#7D4CA1","#296CAB","#1781AA"]
bars = ax.bar(y_pos, percentage, align='center')
for bar,c in zip(bars,colors):
    bar.set_facecolor(c)

plt.xticks(y_pos, emotions)
plt.ylabel('Percentage')
Out[30]:
<matplotlib.text.Text at 0x1110ffba8>

Applying it

Using tweepy for twitter acces, but can be done with any text. http://www.tweepy.org/

Tools used:

  • Tweepy listener for extracting twitter data.
  • Tweepy Stream and OauthHandler class for conectivity
  • Python's json library for extracting text from tweets
  • A csv to python-dictionary reader from the csv library
  • Seaborn for visualizations https://seaborn.pydata.org/
In [31]:
from tweepy.streaming import StreamListener
from tweepy import Stream
from tweepy import OAuthHandler

import json
from csv import DictReader
import seaborn as sns
In [32]:
import myKeys

api_key = myKeys.api_key
api_secret = myKeys.api_secret
access_token_key = myKeys.access_token_key
access_token_secret = myKeys.access_token_secret

Read the csv into a python dictionary

In [33]:
cols = ['anger', 'anticipation', 'disgust', 'fear',
        'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust']

dictFile  = 'dict.csv'
mainDict = {}
with open(dictFile) as csvFile:
        reader = DictReader(csvFile)
        for row in reader:
            mainDict[row['Word']] = [int(row[i]) for i in cols]

Create a tweepy listener

The StreamListener class can be used to handle the incoming tweets. Here every tweet will be given an emotional score in the shape of a 10-valued vector.

In [34]:
class ColorListener(StreamListener):

    def __init__(self):
        self.tweets = pd.DataFrame(columns=('tweet', 'anger', 'anticipation',
                'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust'))

    def on_data(self, data):
        try:
            tweet = json.loads(data)
            vector = self.score(tweet)
            print(vector)
            row = pd.Series([tweet['text']]+vector, index=['tweet', 'anger', 'anticipation',
                'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust'])
            self.tweets = self.tweets.append(row, ignore_index=True)
        except UnboundLocalError:
            raise UnboundLocalError # Tweet doesn't have text
        except:
            pass
        return True

    def score(self, tweet):
        line = tweet['text'].replace('.','').replace(',','').replace(';','').replace(':','').replace('\t',' ').replace('\n',' ')
        words = line.split(' ')
        score = [0] * 10
        for word in words:
            if word in mainDict:
                for i in range(len(score)):
                    score[i] += mainDict[word][i]
        return score

    def on_error(self, status):
        print("Error: ", status)

Let's run it: We can pick other listening filters.

In [35]:
cListener = ColorListener()
auth = OAuthHandler(api_key, api_secret)
auth.set_access_token(access_token_key, access_token_secret)

stream = Stream(auth, cListener)

# Start reading stream for english tweets with the color words
stream.filter(languages=['en'], track=['red', 'green','blue'])
# stream.filter(languages=['en'], track=['trump'])
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 2, 0, 3, 0, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 2, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 0, 0, 1, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[1, 0, 1, 1, 0, 2, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 1, 0, 0, 2, 2, 2, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 2, 0, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[2, 2, 1, 0, 0, 3, 0, 2, 0, 0]
[0, 1, 1, 1, 0, 2, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 1, 1, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 1, 0, 1, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 3, 0, 0, 2, 0, 3, 1, 0, 3]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 2, 1, 2, 0, 1]
[0, 0, 0, 0, 1, 0, 2, 0, 0, 2]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 1, 0, 0, 0, 1]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[1, 0, 0, 1, 1, 2, 1, 2, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 1, 0, 0, 1, 1, 1, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 2, 0, 2, 0, 0, 2]
[0, 0, 1, 0, 1, 1, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 1, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 1, 2, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 1, 0, 0, 1, 0, 1, 0, 1, 1]
[0, 0, 0, 1, 0, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 1, 0, 1, 0, 1, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 1, 0, 1, 0, 1, 0, 0]
[1, 0, 0, 1, 1, 1, 1, 1, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 1, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[1, 1, 1, 2, 0, 2, 1, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 1, 0, 1, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 1, 1, 1, 1, 1, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 1, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 2, 0, 0, 2, 0, 2, 0, 2, 2]
[0, 0, 1, 0, 0, 1, 2, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 1, 0, 0, 0, 0, 0, 0]
[1, 0, 1, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[2, 0, 0, 0, 0, 2, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 2, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 1, 0, 1, 0, 1, 0, 0, 0, 1]
[1, 0, 0, 1, 0, 1, 0, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 0]
[0, 2, 0, 0, 1, 1, 1, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[1, 0, 0, 1, 0, 1, 2, 0, 0, 2]
[1, 0, 0, 0, 0, 1, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 1]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 1, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 2, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 1, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 3, 1, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 0, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 2, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 3, 0, 0, 1]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[1, 0, 1, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 1, 1, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 1, 0, 1, 0, 1, 0, 0]
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[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
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[0, 0, 0, 0, 0, 0, 1, 1, 0, 0]
[0, 1, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 2, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]
[2, 2, 0, 1, 3, 0, 4, 0, 1, 3]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 1, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 1, 1, 2, 3, 0, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[1, 0, 1, 1, 1, 1, 2, 2, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 2, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 0, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 1, 0]
[0, 1, 0, 0, 1, 0, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 1, 0, 2, 0, 0, 1]
[2, 2, 1, 0, 0, 3, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 1, 0, 0, 1, 0, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 1, 1, 0, 1]
[1, 0, 0, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 1, 0, 1]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1]
[0, 0, 0, 0, 1, 1, 1, 0, 0, 2]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 1, 0, 0, 1, 0, 3, 1, 0, 0]
[0, 0, 2, 0, 0, 2, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[2, 2, 1, 0, 0, 3, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[2, 2, 1, 0, 0, 3, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 1, 1, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 0, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
[1, 1, 1, 1, 0, 1, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 1, 1, 1, 0, 1]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 2, 0, 1, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 0, 0, 1, 1, 1, 1, 1, 0, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 2, 0, 2, 0, 0, 1]
[0, 0, 1, 0, 0, 2, 0, 0, 0, 1]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 2, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[2, 2, 1, 0, 0, 3, 0, 2, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 2, 0, 0, 2, 0, 2, 1, 2, 2]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[1, 1, 0, 0, 1, 0, 1, 0, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 0, 1, 0]
[0, 1, 0, 0, 1, 0, 1, 0, 0, 1]
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 1, 0, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 1, 0, 1, 1, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 1, 0, 2, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 1, 0, 1, 1, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 0, 0, 0, 0, 1, 0, 0, 1]
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-35-7098589ea4ce> in <module>()
      6 
      7 # Start reading stream for english tweets with the color words
----> 8 stream.filter(languages=['en'], track=['red', 'green','blue'])
      9 # stream.filter(languages=['en'], track=['trump'])

/usr/local/lib/python3.6/site-packages/tweepy/streaming.py in filter(self, follow, track, async, locations, stall_warnings, languages, encoding, filter_level)
    443         self.session.params = {'delimited': 'length'}
    444         self.host = 'stream.twitter.com'
--> 445         self._start(async)
    446 
    447     def sitestream(self, follow, stall_warnings=False,

/usr/local/lib/python3.6/site-packages/tweepy/streaming.py in _start(self, async)
    359             self._thread.start()
    360         else:
--> 361             self._run()
    362 
    363     def on_closed(self, resp):

/usr/local/lib/python3.6/site-packages/tweepy/streaming.py in _run(self)
    261                     self.snooze_time = self.snooze_time_step
    262                     self.listener.on_connect()
--> 263                     self._read_loop(resp)
    264             except (Timeout, ssl.SSLError) as exc:
    265                 # This is still necessary, as a SSLError can actually be

/usr/local/lib/python3.6/site-packages/tweepy/streaming.py in _read_loop(self, resp)
    311             length = 0
    312             while not resp.raw.closed:
--> 313                 line = buf.read_line().strip()
    314                 if not line:
    315                     self.listener.keep_alive()  # keep-alive new lines are expected

/usr/local/lib/python3.6/site-packages/tweepy/streaming.py in read_line(self, sep)
    177             else:
    178                 start = len(self._buffer)
--> 179             self._buffer += self._stream.read(self._chunk_size)
    180 
    181     def _pop(self, length):

/usr/local/lib/python3.6/site-packages/urllib3/response.py in read(self, amt, decode_content, cache_content)
    382             else:
    383                 cache_content = False
--> 384                 data = self._fp.read(amt)
    385                 if amt != 0 and not data:  # Platform-specific: Buggy versions of Python.
    386                     # Close the connection when no data is returned

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py in read(self, amt)
    447             # Amount is given, implement using readinto
    448             b = bytearray(amt)
--> 449             n = self.readinto(b)
    450             return memoryview(b)[:n].tobytes()
    451         else:

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py in readinto(self, b)
    481 
    482         if self.chunked:
--> 483             return self._readinto_chunked(b)
    484 
    485         if self.length is not None:

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py in _readinto_chunked(self, b)
    576         try:
    577             while True:
--> 578                 chunk_left = self._get_chunk_left()
    579                 if chunk_left is None:
    580                     return total_bytes

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py in _get_chunk_left(self)
    544                 self._safe_read(2)  # toss the CRLF at the end of the chunk
    545             try:
--> 546                 chunk_left = self._read_next_chunk_size()
    547             except ValueError:
    548                 raise IncompleteRead(b'')

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/client.py in _read_next_chunk_size(self)
    504     def _read_next_chunk_size(self):
    505         # Read the next chunk size from the file
--> 506         line = self.fp.readline(_MAXLINE + 1)
    507         if len(line) > _MAXLINE:
    508             raise LineTooLong("chunk size")

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socket.py in readinto(self, b)
    584         while True:
    585             try:
--> 586                 return self._sock.recv_into(b)
    587             except timeout:
    588                 self._timeout_occurred = True

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/ssl.py in recv_into(self, buffer, nbytes, flags)
   1000                   "non-zero flags not allowed in calls to recv_into() on %s" %
   1001                   self.__class__)
-> 1002             return self.read(nbytes, buffer)
   1003         else:
   1004             return socket.recv_into(self, buffer, nbytes, flags)

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/ssl.py in read(self, len, buffer)
    863             raise ValueError("Read on closed or unwrapped SSL socket.")
    864         try:
--> 865             return self._sslobj.read(len, buffer)
    866         except SSLError as x:
    867             if x.args[0] == SSL_ERROR_EOF and self.suppress_ragged_eofs:

/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/ssl.py in read(self, len, buffer)
    623         """
    624         if buffer is not None:
--> 625             v = self._sslobj.read(len, buffer)
    626         else:
    627             v = self._sslobj.read(len)

KeyboardInterrupt: 
In [36]:
df = cListener.tweets
print(len(df.index)) # Number of rows
1548
In [37]:
df.head(10) # How the data looks like
Out[37]:
tweet anger anticipation disgust fear joy negative positive sadness surprise trust
0 Vote 4 John H Cox 4 #CAGov Lets turn CA all RE... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 RT @Petunia5SOS: hitting\nevery\nred\nlight\nk... 0.0 0.0 0.0 0.0 2.0 0.0 3.0 0.0 0.0 2.0
2 RT @DarrenCriss: So what's more red? My sunbur... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 RT @Panthers: First look at @CMcCaffrey5 in bl... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 0.0 0.0
4 Starting Friday June 30- July 4th we will be r... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 RT @ncaprio: Please @GameOfThrones get @zane a... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6 @Ntshuxi_Masingi Lol blue? 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
7 RT @ncaprio: Please @GameOfThrones get @zane a... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
8 @camilluddington i hope we get a scene of jole... 0.0 1.0 0.0 0.0 1.0 0.0 1.0 2.0 1.0 1.0
9 RT @Simpboyz: he took gucci manes jacket and a... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
In [38]:
df.plot(figsize=(16, 6)) # Plot the sentiment as a time series
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x113b3e5f8>
In [39]:
df['trust'].plot(figsize=(16, 6))
Out[39]:
<matplotlib.axes._subplots.AxesSubplot at 0x1140370f0>
In [40]:
df.plot(subplots=True, figsize=(16, 10))
Out[40]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x114373198>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1143d6b70>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1144424e0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x114499208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1144fcc18>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1144fcc50>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1145b6fd0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x114617208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x114678940>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x1146d1a58>], dtype=object)
In [41]:
df['trust'].plot.kde(figsize=(16, 6))
Out[41]:
<matplotlib.axes._subplots.AxesSubplot at 0x1148f0518>
In [43]:
df.sum()
Out[43]:
tweet           Vote 4 John H Cox 4 #CAGov Lets turn CA all RE...
anger                                                         120
anticipation                                                  297
disgust                                                        95
fear                                                          108
joy                                                           406
negative                                                      344
positive                                                      614
sadness                                                       336
surprise                                                      153
trust                                                         408
dtype: object
In [48]:
fig = plt.figure(figsize=(16, 10))
y_pos = np.arange(len(emotions))
percentage = df[emotions].sum()/len(df)*100
percentage = percentage.tolist()

ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
colors = ["#007C37","#79BF2A","#EBC527","#E66F11","#DB1245","#0C2C30","#F53022","#7D4CA1","#296CAB","#1781AA"]
bars = ax.bar(y_pos, percentage, align='center')
for bar,c in zip(bars,colors):
    bar.set_facecolor(c)

plt.xticks(y_pos, emotions)
plt.ylabel('Percentage')
Out[48]:
<matplotlib.text.Text at 0x11075c588>
In [44]:
cor = df.corr()
cor
Out[44]:
anger anticipation disgust fear joy negative positive sadness surprise trust
anger 1.000000 0.365157 0.506145 0.501966 0.058073 0.664673 0.055672 0.456228 0.097249 0.089732
anticipation 0.365157 1.000000 0.177677 0.131297 0.617743 0.223845 0.575675 0.233815 0.243140 0.522471
disgust 0.506145 0.177677 1.000000 0.301815 -0.008650 0.618289 -0.009502 0.291204 -0.011408 0.072098
fear 0.501966 0.131297 0.301815 1.000000 0.037593 0.416117 0.095065 0.321911 0.129501 0.126652
joy 0.058073 0.617743 -0.008650 0.037593 1.000000 -0.031620 0.840555 0.023149 0.197098 0.743312
negative 0.664673 0.223845 0.618289 0.416117 -0.031620 1.000000 -0.021924 0.633363 0.256877 0.053953
positive 0.055672 0.575675 -0.009502 0.095065 0.840555 -0.021924 1.000000 0.030783 0.180880 0.749628
sadness 0.456228 0.233815 0.291204 0.321911 0.023149 0.633363 0.030783 1.000000 0.327330 0.063654
surprise 0.097249 0.243140 -0.011408 0.129501 0.197098 0.256877 0.180880 0.327330 1.000000 0.217169
trust 0.089732 0.522471 0.072098 0.126652 0.743312 0.053953 0.749628 0.063654 0.217169 1.000000
In [45]:
sns.heatmap(cor)
Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x114a778d0>
In [46]:
sns.clustermap(cor)
/usr/local/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.
  warnings.warn(message, mplDeprecation, stacklevel=1)
Out[46]:
<seaborn.matrix.ClusterGrid at 0x1176f2cc0>
In [47]:
from matplotlib.pyplot import figure, show, rc

fig = figure(figsize=(10, 10))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)

colors = ['#007C37','#79BF2A','#EBC527','#E66F11','#DB1245','#7D4CA1','#296CAB','#1781AA']

N = 8
theta = np.arange(0, 2*np.pi, 2*np.pi/N)-(np.pi/(2*N))
radii = df[['fear','trust','joy','anticipation','anger','disgust','sadness','surprise']].sum()
width = np.pi/N
bars = ax.bar(theta, radii, width=width, bottom=0.0)
for r,bar,c in zip(radii,bars,colors):
    bar.set_facecolor(c)
    bar.set_alpha(1)

show()

Where else has this been used?

Where can it be used?

Generalization: Dictionary based lexical analysis.

Correlation with other variables.

  • Correlating with digital profiles: Personality
  • Correlating with digital activity: Motivation
  • Correlating with physical data: Behaviour