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GP11: Analyzing Movie Reviews

1. Read in Data





In [1]:



    


import pandas

movies = pandas.read_csv("../data/GP11/fandango_score_comparison.csv")



    











In [2]:



    


movies.head(5)



    


















    
Out[2]:











  
    

      
      FILM
      RottenTomatoes
      RottenTomatoes_User
      Metacritic
      Metacritic_User
      IMDB
      Fandango_Stars
      Fandango_Ratingvalue
      RT_norm
      RT_user_norm
      ...
      IMDB_norm
      RT_norm_round
      RT_user_norm_round
      Metacritic_norm_round
      Metacritic_user_norm_round
      IMDB_norm_round
      Metacritic_user_vote_count
      IMDB_user_vote_count
      Fandango_votes
      Fandango_Difference
    

  
  
    

      0
      Avengers: Age of Ultron (2015)
      74
      86
      66
      7.1
      7.8
      5.0
      4.5
      3.70
      4.3
      ...
      3.90
      3.5
      4.5
      3.5
      3.5
      4.0
      1330
      271107
      14846
      0.5
    

    

      1
      Cinderella (2015)
      85
      80
      67
      7.5
      7.1
      5.0
      4.5
      4.25
      4.0
      ...
      3.55
      4.5
      4.0
      3.5
      4.0
      3.5
      249
      65709
      12640
      0.5
    

    

      2
      Ant-Man (2015)
      80
      90
      64
      8.1
      7.8
      5.0
      4.5
      4.00
      4.5
      ...
      3.90
      4.0
      4.5
      3.0
      4.0
      4.0
      627
      103660
      12055
      0.5
    

    

      3
      Do You Believe? (2015)
      18
      84
      22
      4.7
      5.4
      5.0
      4.5
      0.90
      4.2
      ...
      2.70
      1.0
      4.0
      1.0
      2.5
      2.5
      31
      3136
      1793
      0.5
    

    

      4
      Hot Tub Time Machine 2 (2015)
      14
      28
      29
      3.4
      5.1
      3.5
      3.0
      0.70
      1.4
      ...
      2.55
      0.5
      1.5
      1.5
      1.5
      2.5
      88
      19560
      1021
      0.5
    

  



5 rows × 22 columns

2. Histograms





In [3]:



    


import matplotlib.pyplot as plt
%matplotlib inline

plt.hist(movies["Fandango_Stars"])



    


















    
Out[3]:








(array([ 12.,   0.,  27.,   0.,   0.,  41.,   0.,  55.,   0.,  11.]),
 array([ 3. ,  3.2,  3.4,  3.6,  3.8,  4. ,  4.2,  4.4,  4.6,  4.8,  5. ]),
 <a list of 10 Patch objects>)










    
























In [4]:



    


plt.hist(movies["Metacritic_norm_round"])



    


















    
Out[4]:








(array([  1.,   2.,  20.,  14.,   0.,  22.,  27.,  20.,  25.,  15.]),
 array([ 0.5,  0.9,  1.3,  1.7,  2.1,  2.5,  2.9,  3.3,  3.7,  4.1,  4.5]),
 <a list of 10 Patch objects>)

Comments on Fandango vs Metacritic Scores

  • Fandango does not score below 3, and is centred around 4.5.
  • Metacritic is centred around 3 to 3.5

3. Mean, Median, And Standard Deviation





In [5]:



    


import numpy

f_mean = movies["Fandango_Stars"].mean()
m_mean = movies["Metacritic_norm_round"].mean()
f_std = movies["Fandango_Stars"].std()
m_std = movies["Metacritic_norm_round"].std()
f_median = movies["Fandango_Stars"].median()
m_median = movies["Metacritic_norm_round"].median()

print(f_mean)
print(m_mean)
print(f_std)
print(m_std)
print(f_median)
print(m_median)



    


















    






4.089041095890411
2.9726027397260273
0.5403859779787333
0.9909605613743364
4.0
3.0

Comments on Fandango vs Metacritic Methodology

  • Fandango appears to inflate ratings and isn't transparent about how it calculates and aggregates ratings.
  • Metacritic publishes each individual critic rating, and is transparent about how they aggregate them to get a final rating.

Comments on Fandango vs Metacritic number differences

  • The median metacritic score appears higher than the mean metacritic score because a few very low reviews "drag down" the median. The median fandango score is lower than the mean fandango score because a few very high ratings "drag up" the mean.
  • Fandango ratings appear clustered between 3 and 5, and have a much narrower random than Metacritic reviews, which go from 0 to 5.
  • Fandango ratings in general appear to be higher than metacritic ratings.
  • These may be due to movie studio influence on Fandango ratings, and the fact that Fandango calculates its ratings in a hidden way.

4. Scatter Plots





In [6]:



    


plt.scatter(movies["Metacritic_norm_round"], movies["Fandango_Stars"])



    


















    
Out[6]:








<matplotlib.collections.PathCollection at 0x7f669d7534e0>










    
























In [7]:



    


movies["fm_diff"] = numpy.abs(movies["Metacritic_norm_round"] - movies["Fandango_Stars"])



    











In [8]:



    


movies.sort_values(by="fm_diff", ascending=False).head(5)



    


















    
Out[8]:











  
    

      
      FILM
      RottenTomatoes
      RottenTomatoes_User
      Metacritic
      Metacritic_User
      IMDB
      Fandango_Stars
      Fandango_Ratingvalue
      RT_norm
      RT_user_norm
      ...
      RT_norm_round
      RT_user_norm_round
      Metacritic_norm_round
      Metacritic_user_norm_round
      IMDB_norm_round
      Metacritic_user_vote_count
      IMDB_user_vote_count
      Fandango_votes
      Fandango_Difference
      fm_diff
    

  
  
    

      3
      Do You Believe? (2015)
      18
      84
      22
      4.7
      5.4
      5.0
      4.5
      0.90
      4.20
      ...
      1.0
      4.0
      1.0
      2.5
      2.5
      31
      3136
      1793
      0.5
      4.0
    

    

      85
      Little Boy (2015)
      20
      81
      30
      5.9
      7.4
      4.5
      4.3
      1.00
      4.05
      ...
      1.0
      4.0
      1.5
      3.0
      3.5
      38
      5927
      811
      0.2
      3.0
    

    

      47
      Annie (2014)
      27
      61
      33
      4.8
      5.2
      4.5
      4.2
      1.35
      3.05
      ...
      1.5
      3.0
      1.5
      2.5
      2.5
      108
      19222
      6835
      0.3
      3.0
    

    

      19
      Pixels (2015)
      17
      54
      27
      5.3
      5.6
      4.5
      4.1
      0.85
      2.70
      ...
      1.0
      2.5
      1.5
      2.5
      3.0
      246
      19521
      3886
      0.4
      3.0
    

    

      134
      The Longest Ride (2015)
      31
      73
      33
      4.8
      7.2
      4.5
      4.5
      1.55
      3.65
      ...
      1.5
      3.5
      1.5
      2.5
      3.5
      49
      25214
      2603
      0.0
      3.0
    

  



5 rows × 23 columns

5. Correlations





In [9]:



    


from scipy.stats import pearsonr

r_value, p_value = pearsonr(movies["Fandango_Stars"], movies["Metacritic_norm_round"])

r_value



    


















    
Out[9]:








0.17844919073895918

Comments on Fandango and Metacritic correlation

  • The low correlation between Fandango and Metacritic scores indicates that Fandango scores aren't just inflated, they are fundamentally different.
  • For whatever reason, it appears like Fandango both inflates scores overall, and inflates scores differently depending on the movie.




In [10]:



    


from scipy.stats import linregress
slope, intercept, r_value, p_value, stderr_slope = linregress(movies["Metacritic_norm_round"], movies["Fandango_Stars"])



    











In [11]:



    


pred = 3 * slope + intercept
pred



    


















    
Out[11]:








4.0917071528212032

6. Finding Residuals





In [12]:



    


pred_1 = 1 * slope + intercept
pred_5 = 5 * slope + intercept
plt.scatter(movies["Metacritic_norm_round"], movies["Fandango_Stars"])
plt.plot([1,5],[pred_1,pred_5])
plt.xlim(1,5)
plt.show()

Content source: JasonMDev/guidedprojects

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