In [ ]:
In [14]:
import pandas as pd
import numpy as np

pos = pd.read_csv('AMT_pos.csv')
neg = pd.read_csv('AMT_neg.csv')
In [89]:
pos[:5]
Out[89]:
HITId HITTypeId Title Description Keywords Reward CreationTime MaxAssignments RequesterAnnotation AssignmentDurationInSeconds ... RejectionTime RequesterFeedback WorkTimeInSeconds LifetimeApprovalRate Last30DaysApprovalRate Last7DaysApprovalRate Input.text Answer.sentiment.label Approve Reject
0 3VMV5CHJZ8F47P7CECH0H830NF4GTP 3N0K7CX2I27L2NR2L8D93MF8LIRA5J Sentiment analysis Sentiment analysis sentiment, text $0.02 Fri Nov 01 12:11:19 PDT 2019 3 BatchId:3821427;OriginalHitTemplateId:928390909; 10800 ... NaN NaN 355 0% (0/0) 0% (0/0) 0% (0/0) funny like a clown\nGreetings again from the d... Positive NaN NaN
1 3VMV5CHJZ8F47P7CECH0H830NF4GTP 3N0K7CX2I27L2NR2L8D93MF8LIRA5J Sentiment analysis Sentiment analysis sentiment, text $0.02 Fri Nov 01 12:11:19 PDT 2019 3 BatchId:3821427;OriginalHitTemplateId:928390909; 10800 ... NaN NaN 487 0% (0/0) 0% (0/0) 0% (0/0) funny like a clown\nGreetings again from the d... Neutral NaN NaN
2 3VMV5CHJZ8F47P7CECH0H830NF4GTP 3N0K7CX2I27L2NR2L8D93MF8LIRA5J Sentiment analysis Sentiment analysis sentiment, text $0.02 Fri Nov 01 12:11:19 PDT 2019 3 BatchId:3821427;OriginalHitTemplateId:928390909; 10800 ... NaN NaN 1052 0% (0/0) 0% (0/0) 0% (0/0) funny like a clown\nGreetings again from the d... Positive NaN NaN
3 36GJS3V78VQATMB72I3WF2GRPDDJGN 3N0K7CX2I27L2NR2L8D93MF8LIRA5J Sentiment analysis Sentiment analysis sentiment, text $0.02 Fri Nov 01 12:11:19 PDT 2019 3 BatchId:3821427;OriginalHitTemplateId:928390909; 10800 ... NaN NaN 4 0% (0/0) 0% (0/0) 0% (0/0) Only certain people can relate\nThis is a mov... Negative NaN NaN
4 36GJS3V78VQATMB72I3WF2GRPDDJGN 3N0K7CX2I27L2NR2L8D93MF8LIRA5J Sentiment analysis Sentiment analysis sentiment, text $0.02 Fri Nov 01 12:11:19 PDT 2019 3 BatchId:3821427;OriginalHitTemplateId:928390909; 10800 ... NaN NaN 666 0% (0/0) 0% (0/0) 0% (0/0) Only certain people can relate\nThis is a mov... Positive NaN NaN

5 rows × 31 columns

In [27]:
pos.columns
Out[27]:
Index(['HITId', 'HITTypeId', 'Title', 'Description', 'Keywords', 'Reward',
       'CreationTime', 'MaxAssignments', 'RequesterAnnotation',
       'AssignmentDurationInSeconds', 'AutoApprovalDelayInSeconds',
       'Expiration', 'NumberOfSimilarHITs', 'LifetimeInSeconds',
       'AssignmentId', 'WorkerId', 'AssignmentStatus', 'AcceptTime',
       'SubmitTime', 'AutoApprovalTime', 'ApprovalTime', 'RejectionTime',
       'RequesterFeedback', 'WorkTimeInSeconds', 'LifetimeApprovalRate',
       'Last30DaysApprovalRate', 'Last7DaysApprovalRate', 'Input.text',
       'Answer.sentiment.label', 'Approve', 'Reject'],
      dtype='object')
In [99]:
unique_turkers_pos = np.unique(pos['WorkerId'], return_counts=True)
print(len(unique_turkers_pos[0]))
turker_df = pd.DataFrame(zip(unique_turkers_pos[0], unique_turkers_pos[1]))
sorted(turker_df)
df = pd.DataFrame(turker_df.sort_values(by=1, ascending=False))
# plt.plot(df)
# plt.show()
type(df)

38
Out[99]:
pandas.core.frame.DataFrame
In [35]:
unique_turkers_neg = np.unique(neg['WorkerId'], return_counts=True)
len(unique_turkers_neg[0])
Out[35]:
53
In [36]:
pos_workers = pos.groupby('WorkerId')
In [38]:
len(pos_workers)
Out[38]:
38
In [43]:
bot = neg[neg['WorkerId'] == 'A3HAEQW13YPT6A']
In [84]:
for_plot = np.unique(bot['Answer.sentiment.label'].tolist(), return_counts=True)
for_plot = zip(for_plot[0], for_plot[1])
for_plot
Out[84]:
<zip at 0x11017b048>
In [86]:
import seaborn as sns
def make_plot(x, y, col, color, size, data):
    sns.set()
    sns.relplot(x=x, y=y, col=col, hue=color, size=size, data=data)

data = sns.load_dataset("tips")
make_plot('total_bill', 'tip', 'time', 'smoker', 'size', data)
In [88]:
data[:5]
Out[88]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
In [90]:
# make_plot('total_bill', 'tip', 'time', 'smoker', 'size', pos)

# sns.scatterplot(x="total_bill", y="tip", hue="day", data=tips, ax=axes[1]);

sns.boxplot(x="Answer.sentiment.label", y="WorkTimeInSeconds", data=pos)
Out[90]:
<matplotlib.axes._subplots.AxesSubplot at 0x10e49c048>
In [91]:
sns.catplot(x="Answer.sentiment.label", y="WorkTimeInSeconds", kind="bar", data=pos);
In [92]:
sns.catplot(x="Answer.sentiment.label", y="WorkTimeInSeconds", kind="bar", data=neg);
In [100]:
unique_turkers_pos = np.unique(pos['WorkerId'], return_counts=True)
print(len(unique_turkers_pos[0]))
turker_df = pd.DataFrame(zip(unique_turkers_pos[0], unique_turkers_pos[1]))
sorted(turker_df)
df = pd.DataFrame(turker_df.sort_values(by=1, ascending=False))

38
In [102]:
df.plot(kind='bar',x=0,y=1)
Out[102]:
<matplotlib.axes._subplots.AxesSubplot at 0x10f39a5c0>
In [103]:
from sklearn.metrics import cohen_kappa_score
y1 = [0,1,2,3,4,0,1,2,3,4,0,1,2,3,4]
y2 = [0,1,2,2,4,1,2,3,0,0,0,2,2,4,4]
cohen_kappa_score(y1,y2)
Out[103]:
0.33333333333333337
In [105]:
df1=pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: 
                                                            (x=='Neutral').sum()).reset_index(name='Neutral')
df2=pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: 
                                                            (x=='Positive').sum()).reset_index(name='Positive')
df3=pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: 
                                                            (x=='Negative').sum()).reset_index(name='Negative')
In [109]:
# pd.concat([df1, df2, df3], p='')
In [14]:
import pandas as pd
import numpy as np
np.random.seed(2016)

df = pd.DataFrame({'Turker': ['A', 'C', 'B', 'A', 'C', 'C'],
                   'SENTIMENT': ['Neg', 'Pos', 'Neg', 'Neg', 'Pos', 'Neu'],
                   'REVIEW': [1, 2, 3, 4, 5, 6]})

grouped = df.groupby('Turker')
values = grouped['REVIEW'].agg('sum')
id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()
id_df = id_df.rename(columns={i: 'SENTIMENT{}'.format(i + 1) for i in range(id_df.shape[1])})
result = pd.concat([id_df, values], axis=1)
print(result.T)

Turker        A    B    C
SENTIMENT1  Neg  Neg  Pos
SENTIMENT2  Neg  NaN  Pos
SENTIMENT3  NaN  NaN  Neu
REVIEW        5    3   13
In [12]:
df
Out[12]:
Turker REVIEW SENTIMENT
0 A Neg 1
1 C Pos 2
2 B Neg 3
3 A Neg 4
4 C Pos 5
5 C Neu 6
In [ ]: