Motivation¶

  • What is the prevailing sentiment of popular songs in various countries?

  • How do the sentiments expressed in song lyrics vary across different countries?

  • Do they influence the popularity of songs?

Contents¶

  • Text analysis
    • Lyrics similarity
    • Sentiment Analysis
      • Vader
      • TextBlob
      • NRCLex
      • Afinn
    • ANOVA

Text Analysis ¶

In [1]:
# IMPORT DEPENDENCIES 
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.feature_extraction.text import CountVectorizer
import nltk
import time 
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import chart_studio
import chart_studio.plotly as py
import chart_studio.tools as tls
import plotly.offline as pyo
pyo.init_notebook_mode()
In [2]:
# READ DATA 
song_data = pd.read_csv('../Data/merged_finaltop100_revised.csv') 
# REMOVE ROWS WITH NULL VALUES  
song_data = song_data.dropna()
song_data.head()
Out[2]:
Unnamed: 0 track_id artist_names track_name source rank weeks_on_chart streams country danceability ... duration_ms time_signature album_release_date lyrics lyrics_trans continent iso_alpha3 len_words_orig len_words_trans lyrics_clean
0 0 0yLdNVWF3Srea0uzk55zFn Miley Cyrus Flowers Columbia 1 5 124198 United Arab Emirates 0.707 ... 200455.0 4.0 2023-01-13 We were good, we were gold\nKinda dream that c... we were good we were gold kinda dream that can... Asia ARE 334 334 good gold dream sell right til build home watc...
1 1 1Qrg8KqiBpW07V7PNxwwwL SZA Kill Bill Top Dawg Entertainment/RCA Records 2 10 106927 United Arab Emirates 0.644 ... 153947.0 4.0 2022-12-08 I'm still a fan even though I was salty\nHate ... im still a fan even though i was salty hate to... Asia ARE 362 362 fan even though salty hate see broad know happ...
2 2 6AQbmUe0Qwf5PZnt4HmTXv PinkPantheress, Ice Spice Boy's a liar Pt. 2 Warner Records 3 2 83627 United Arab Emirates 0.696 ... 131013.0 4.0 2023-02-03 Take a look inside your heart\nIs there any ro... take a look inside your heart is there any roo... Asia ARE 372 372 take look inside heart room room would hold br...
3 3 0WtM2NBVQNNJLh6scP13H8 Rema, Selena Gomez Calm Down (with Selena Gomez) Mavin Records / Jonzing World 4 25 79714 United Arab Emirates 0.801 ... 239318.0 4.0 2022-08-25 Vibez\nOh, no\nAnother banger\nBaby, calm down... vibez oh no another banger baby calm down calm... Asia ARE 495 495 another banger baby calm calm girl body put he...
4 4 2dHHgzDwk4BJdRwy9uXhTO Metro Boomin, The Weeknd, 21 Savage Creepin' (with The Weeknd & 21 Savage) Republic Records 5 11 79488 United Arab Emirates 0.715 ... 221520.0 4.0 2022-12-02 Ooh, ooh-ooh\nOoh-ooh-ooh, ooh, ooh-ooh (Just ... ooh oohooh oohoohooh ooh oohooh just cant beli... Asia ARE 458 456 believe man want somebody say saw person kiss ...

5 rows × 30 columns

Lyrics Similarity ¶

Cosine similarity between in lyrics of popular songs in 6 continents

In [3]:
# COMBINE LYRICS BY CONTINENT 
joined_lyrics = song_data.dropna(subset=['lyrics_clean'])
joined_lyrics = joined_lyrics.groupby('continent')['lyrics_clean'].apply(' '.join).reset_index()
#joined_lyrics = pd.merge(lyrics_joined, song_data[['country', 'continent']].drop_duplicates(), on='country', how='inner')
joined_lyrics
Out[3]:
continent lyrics_clean
0 Africa oppressor oppress silencer inside longer blame...
1 Asia good gold dream sell right til build home watc...
2 Europe good gold dream sell right til build home watc...
3 North America good gold dream sell right til build home watc...
4 Oceania good gold dream sell right til build home watc...
5 South America come back want get hurt lose count forgive dre...
In [4]:
# COMPUTE WORD FREQUENCIES 
vec = CountVectorizer(tokenizer = nltk.word_tokenize)
freq = vec.fit_transform(joined_lyrics['lyrics_clean'])
feat_names = vec.get_feature_names_out()
feat_names

C:\Users\kayan\Anaconda3\envs\py310\lib\site-packages\sklearn\feature_extraction\text.py:528: UserWarning:

The parameter 'token_pattern' will not be used since 'tokenizer' is not None'
Out[4]:
array(['abandon', 'abbot', 'abdomen', ..., 'zoo', 'zoom', 'zorro'],
      dtype=object)
In [5]:
# CONVERT TO DATAFRAME 
df1 = pd.DataFrame(freq.todense(), columns=feat_names,  index=joined_lyrics['continent'])
df1
Out[5]:
abandon abbot abdomen abdominal abduction abide ability able abnormal abolitionist ... zip zipper zloty zobo zodiac zombie zone zoo zoom zorro
continent
Africa 0 0 0 0 0 0 0 8 1 0 ... 9 0 0 1 0 2 8 1 0 0
Asia 6 0 0 0 1 2 4 38 2 3 ... 9 0 0 0 4 11 19 1 1 1
Europe 1 0 0 1 1 0 2 85 3 0 ... 14 6 2 0 4 3 56 3 11 2
North America 13 0 11 0 0 0 0 82 0 0 ... 2 0 0 0 0 0 9 0 0 16
Oceania 0 0 0 0 0 0 0 2 0 0 ... 2 0 0 0 0 3 2 0 0 0
South America 20 2 6 0 0 0 1 130 0 0 ... 0 0 0 0 0 3 8 0 0 10

6 rows × 11005 columns

In [9]:
# CALCULATE TF-IDF USING BIGRAM MODEL 
vec= TfidfVectorizer(ngram_range=(2, 2))
tfidf_bigram = vec.fit_transform(joined_lyrics['lyrics_clean'])
print(tfidf_bigram.todense()) #tf-idf using the bi-gram model

[[0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.00186526 0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.00033277 0.         0.        ]
 [0.00775453 0.         0.         ... 0.         0.00387726 0.00078805]
 [0.         0.         0.         ... 0.         0.         0.        ]
 [0.00916453 0.         0.00079829 ... 0.         0.00130922 0.        ]]
In [10]:
# COSINE SIMILARITY 
cos_sim = cosine_similarity(tfidf_bigram) #cosine similarity 
print(cos_sim)

[[1.         0.48433201 0.5207107  0.3766522  0.41550417 0.34198294]
 [0.48433201 1.         0.73300551 0.5086972  0.6085006  0.45857363]
 [0.5207107  0.73300551 1.         0.57597451 0.72360452 0.50337469]
 [0.3766522  0.5086972  0.57597451 1.         0.4462909  0.83967648]
 [0.41550417 0.6085006  0.72360452 0.4462909  1.         0.32806026]
 [0.34198294 0.45857363 0.50337469 0.83967648 0.32806026 1.        ]]
In [45]:
# PLOT COSINE SIMILARITY
fig = px.imshow(cos_sim.round(2),
                labels=dict(color="Cosine similarity"),
                x=['Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America'],
                y=['Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America'], 
                color_continuous_scale='GnBu',  text_auto=True, aspect="auto",
                title = 'Continental lyrics similary (bi-gram  tf-idf)'
               )
fig.update_xaxes(side="top")
fig.show()

Figure 2.1

The range of cosine similarities computed above is 0.3-0.8, indicating a generally moderate to high degree of similarity between the lyrics from 6 different continents. This suggests that there are some commonalities in the themes and topics covered in these lyrics. Notably, the highest similarities were observed between North and South America, which is reasonable considering their geographical proximity.

Sentiment Analysis (Unsupervised) ¶

In order to discover unknown patterns in this dataset, unsupervised sentiment analysis techniques are used. Using these methods, the sentiment of lyrics (positive, negative, or neutral) can be identified.

I. Sentiment Scores: Vader ¶

In [12]:
# IMPORT DEPENDENCIES 
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment import SentimentIntensityAnalyzer

[nltk_data] Downloading package vader_lexicon to
[nltk_data]     C:\Users\kayan\AppData\Roaming\nltk_data...
[nltk_data]   Package vader_lexicon is already up-to-date!
In [13]:
def get_sentiment_1(text): 
    '''get sentiment scores of a text'''
    sia = SentimentIntensityAnalyzer() #instantiate sentiment analyzer object
    #newWords = {'good': 2.0, 'down': 2.0, 'normal': 2.0, 'well': 2.0}
    #sid.lexicon.update(newWords) #update words if needed 
    sentiment_score = sia.polarity_scores(text) #sentiment score of text 
    return sentiment_score
In [14]:
def get_scores_1(lst): 
    '''get scores for each text'''
    scores_ls = [] 
    for i in lst: 
        score = get_sentiment_1(i)
        scores_ls.append(score)
    return scores_ls

Sentiment scores for every lyrics

In [15]:
%%time 
# GET SENTIMENT SCORES FOR EVERY LYRICS 
scores_df = pd.DataFrame(get_scores_1(song_data['lyrics_clean']))
scores_df

CPU times: total: 49.2 s
Wall time: 49.6 s
Out[15]:
neg neu pos compound
0 0.085 0.231 0.685 0.9995
1 0.161 0.401 0.438 0.9953
2 0.208 0.446 0.346 0.9784
3 0.042 0.605 0.353 0.9970
4 0.084 0.654 0.261 0.9814
... ... ... ... ...
6804 0.088 0.718 0.195 0.9837
6805 0.133 0.741 0.126 0.4537
6806 0.048 0.426 0.526 0.9991
6807 0.000 0.843 0.157 0.9961
6808 0.129 0.805 0.066 -0.8885

6809 rows × 4 columns

In [16]:
# ADD TO DATAFRAME 
dt = song_data[['country', 'continent', 'lyrics_clean']].reset_index() 
df_all = pd.concat([dt, scores_df], axis=1)
df_all
Out[16]:
index country continent lyrics_clean neg neu pos compound
0 0 United Arab Emirates Asia good gold dream sell right til build home watc... 0.085 0.231 0.685 0.9995
1 1 United Arab Emirates Asia fan even though salty hate see broad know happ... 0.161 0.401 0.438 0.9953
2 2 United Arab Emirates Asia take look inside heart room room would hold br... 0.208 0.446 0.346 0.9784
3 3 United Arab Emirates Asia another banger baby calm calm girl body put he... 0.042 0.605 0.353 0.9970
4 4 United Arab Emirates Asia believe man want somebody say saw person kiss ... 0.084 0.654 0.261 0.9814
... ... ... ... ... ... ... ... ...
6804 7295 South Africa Africa cook thing man get high fade pure way hullabal... 0.088 0.718 0.195 0.9837
6805 7296 South Africa Africa sweet love yeah mean say love tight never let ... 0.133 0.741 0.126 0.4537
6806 7297 South Africa Africa first wisdom fear hear child piano first wisdo... 0.048 0.426 0.526 0.9991
6807 7298 South Africa Africa mother mother mother mother mother mother moth... 0.000 0.843 0.157 0.9961
6808 7299 South Africa Africa let dude know work go closet go break bone saw... 0.129 0.805 0.066 -0.8885

6809 rows × 8 columns

In [17]:
# COMPUTE AVERAGE SENTIMENT SCORES FOR EACH COUNTRY 
grouped_df = df_all.groupby('country').mean()[['neg','neu','pos','compound']].reset_index()
grouped_df

C:\Users\kayan\AppData\Local\Temp\ipykernel_25464\3038061562.py:2: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
Out[17]:
country neg neu pos compound
0 Argentina 0.164818 0.561616 0.273566 0.525287
1 Australia 0.143470 0.579740 0.276770 0.532359
2 Austria 0.149606 0.555141 0.295202 0.546185
3 Belarus 0.193937 0.542189 0.263916 0.331966
4 Belgium 0.147360 0.579980 0.272610 0.425093
... ... ... ... ... ...
68 United Arab Emirates 0.134292 0.588344 0.277302 0.564866
69 United Kingdom 0.148408 0.578367 0.273194 0.513176
70 Uruguay 0.159333 0.561917 0.278729 0.575545
71 Venezuela 0.148606 0.571061 0.280313 0.500868
72 Vietnam 0.142391 0.533946 0.323707 0.708167

73 rows × 5 columns

In [18]:
# PLOT 
pd.options.plotting.backend = "plotly"
grouped_df.plot.bar(y='country', x=['neg','neu','pos','compound'], 
                    title = 'Average sentiment scores by country',
                    template = 'plotly_dark')

Figure 2.2

II. Sentiment Scores: TextBlob ¶

In [23]:
# IMPORT DEPENDENCIES 
from textblob import TextBlob
In [24]:
def get_sentiment_2(text): 
    '''get sentiment scores of a text'''
    blob = TextBlob(text) #instantiate sentiment analyzer object
    sentiment_score = blob.sentiment #sentiment score of text 
    dic = {'polarity': sentiment_score.polarity, 'subjectivity': sentiment_score.subjectivity} 
    return dic
In [25]:
def get_scores_2(lst): 
    '''get scores for each text'''
    scores_ls = [] 
    for i in lst:  
        score = get_sentiment_2(i)
        scores_ls.append(score)
    return scores_ls
In [26]:
%%time 
# GET SENTIMENT SCORES FOR EVERY LYRICS 
scores_df1 = pd.DataFrame(get_scores_2(song_data['lyrics_clean']))
df_all1 = pd.concat([df_all, scores_df1], axis=1)
df_all1

CPU times: total: 5.08 s
Wall time: 5.32 s
Out[26]:
index country continent lyrics_clean neg neu pos compound polarity subjectivity
0 0 United Arab Emirates Asia good gold dream sell right til build home watc... 0.085 0.231 0.685 0.9995 0.499696 0.549696
1 1 United Arab Emirates Asia fan even though salty hate see broad know happ... 0.161 0.401 0.438 0.9953 0.270764 0.394594
2 2 United Arab Emirates Asia take look inside heart room room would hold br... 0.208 0.446 0.346 0.9784 0.439015 0.563258
3 3 United Arab Emirates Asia another banger baby calm calm girl body put he... 0.042 0.605 0.353 0.9970 0.141603 0.504190
4 4 United Arab Emirates Asia believe man want somebody say saw person kiss ... 0.084 0.654 0.261 0.9814 0.152885 0.368269
... ... ... ... ... ... ... ... ... ... ...
6804 7295 South Africa Africa cook thing man get high fade pure way hullabal... 0.088 0.718 0.195 0.9837 0.163435 0.400835
6805 7296 South Africa Africa sweet love yeah mean say love tight never let ... 0.133 0.741 0.126 0.4537 0.010605 0.311858
6806 7297 South Africa Africa first wisdom fear hear child piano first wisdo... 0.048 0.426 0.526 0.9991 0.500275 0.801515
6807 7298 South Africa Africa mother mother mother mother mother mother moth... 0.000 0.843 0.157 0.9961 0.200000 1.000000
6808 7299 South Africa Africa let dude know work go closet go break bone saw... 0.129 0.805 0.066 -0.8885 -0.221528 0.622917

6809 rows × 10 columns

In [27]:
#c COMPUTE AVERAGE SCORES FOR EVERY COUNTRY 
grouped_df = df_all1.groupby('country').mean()[['neg','neu','pos','compound', 
                                                'polarity','subjectivity']].reset_index()
grouped_df

C:\Users\kayan\AppData\Local\Temp\ipykernel_25464\2918057648.py:2: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
Out[27]:
country neg neu pos compound polarity subjectivity
0 Argentina 0.164818 0.561616 0.273566 0.525287 0.104611 0.527113
1 Australia 0.143470 0.579740 0.276770 0.532359 0.144419 0.501962
2 Austria 0.149606 0.555141 0.295202 0.546185 0.119142 0.500247
3 Belarus 0.193937 0.542189 0.263916 0.331966 0.104841 0.513782
4 Belgium 0.147360 0.579980 0.272610 0.425093 0.128526 0.514742
... ... ... ... ... ... ... ...
68 United Arab Emirates 0.134292 0.588344 0.277302 0.564866 0.165916 0.491276
69 United Kingdom 0.148408 0.578367 0.273194 0.513176 0.115046 0.489386
70 Uruguay 0.159333 0.561917 0.278729 0.575545 0.110547 0.533040
71 Venezuela 0.148606 0.571061 0.280313 0.500868 0.109068 0.518744
72 Vietnam 0.142391 0.533946 0.323707 0.708167 0.163098 0.514426

73 rows × 7 columns

In [46]:
# PLOT 
pd.options.plotting.backend = "plotly"
grouped_df.plot.bar(y='country', x=['polarity','subjectivity'], 
                title = 'Average scores by country', template = 'plotly_dark')

Figure 2.3

In this plot, Pakistan's polarity bar is not visible due to its exceptionally low average polarity score of 0.000327.

III. Maps¶

In [31]:
# IMPORT DEPENDENCIES 
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
In [32]:
# GET ISO-3 CODES FOR EACH COUNTRY 
iso = song_data[['country','continent', 'iso_alpha3']].drop_duplicates()
merged_df = pd.merge(grouped_df, iso, on='country')
merged_df
Out[32]:
country neg neu pos compound polarity subjectivity continent iso_alpha3
0 Argentina 0.164818 0.561616 0.273566 0.525287 0.104611 0.527113 South America ARG
1 Australia 0.143470 0.579740 0.276770 0.532359 0.144419 0.501962 Oceania AUS
2 Austria 0.149606 0.555141 0.295202 0.546185 0.119142 0.500247 Europe AUT
3 Belarus 0.193937 0.542189 0.263916 0.331966 0.104841 0.513782 Europe BLR
4 Belgium 0.147360 0.579980 0.272610 0.425093 0.128526 0.514742 Europe BEL
... ... ... ... ... ... ... ... ... ...
68 United Arab Emirates 0.134292 0.588344 0.277302 0.564866 0.165916 0.491276 Asia ARE
69 United Kingdom 0.148408 0.578367 0.273194 0.513176 0.115046 0.489386 Europe GBR
70 Uruguay 0.159333 0.561917 0.278729 0.575545 0.110547 0.533040 South America URY
71 Venezuela 0.148606 0.571061 0.280313 0.500868 0.109068 0.518744 South America VEN
72 Vietnam 0.142391 0.533946 0.323707 0.708167 0.163098 0.514426 Asia VNM

73 rows × 9 columns

In [48]:
# TOP AND BOTTOM 5 COMPOUND SCORES 
merged_df[['country', 'compound']].sort_values('compound',ascending =False)
Out[48]:
country compound
34 Japan 0.793715
59 South Korea 0.760865
72 Vietnam 0.708167
26 Hong Kong 0.704298
63 Taiwan 0.656694
... ... ...
54 Romania 0.341520
3 Belarus 0.331966
15 Dominican Republic 0.321832
67 Ukraine 0.300451
65 Turkey 0.267588

73 rows × 2 columns

In [33]:
# PLOT RESULTS 
fig = px.scatter_geo(merged_df, locations='iso_alpha3', color="compound",
                     hover_name="country", size="compound", 
                     title='Average sentiment score (compound) by country' )
fig.update_geos(showcoastlines=True, coastlinecolor="white",
                showocean=True, oceancolor="black")
fig.show()

Figure 2.4

The average compound score appears to differ among various countries, with Japan, South Korea, Hong Kong, Vietnam, and Germany having the highest average compound scores. This suggests that people in these countries prefer to listen to songs with predominantly positive sentiments. Conversely, Turkey and Ukraine have the lowest average compound score. While the low average sentiment score in Ukraine may be attributed to the ongoing war in their country, there may be other political and social issues in Turkey that lead its people to favor songs with less positive themes.

In [49]:
# TOP AND BOTTOM 5 POLARITY SCORES 
merged_df[['country', 'polarity']].sort_values('polarity',ascending =False)
Out[49]:
country polarity
30 Indonesia 0.191214
39 Malaysia 0.174983
64 Thailand 0.168123
68 United Arab Emirates 0.165916
72 Vietnam 0.163098
... ... ...
23 Greece 0.078557
15 Dominican Republic 0.063725
65 Turkey 0.055759
29 India 0.052369
47 Pakistan 0.000327

73 rows × 2 columns

In [34]:
# PLOT RESULTS 
fig1 = px.scatter_geo(merged_df, locations='iso_alpha3', color="polarity",
                     hover_name="country", size="polarity", 
                     title='Average sentiment score (polarity) by country' )
fig1.update_geos(showcoastlines=True, coastlinecolor="white",
                 showocean=True, oceancolor="black")
fig1.show()

Figure 2.5

Similar to the average compound score plot in Figure 4.3, the average polarity scores for different countries also vary. In Figure 2.3 above, Pakistan's polarity bar is not visible due to its exceptionally low average polarity score of 0.000327. Other countries with low sentiment or polarity scores using TextBlob include Turkey, India, Dominican Republic, and Greece. Conversely, Indonesia, Malaysia, Thailand, Vietnam, and UAE have the highest average polarity scores.

In [50]:
# TOP AND BOTTOM 5 SUBJECTIVITY SCORES 
merged_df[['country', 'subjectivity']].sort_values('subjectivity',ascending =False)
Out[50]:
country subjectivity
53 Portugal 0.541602
9 Chile 0.540034
6 Brazil 0.538437
64 Thailand 0.537607
48 Panama 0.537318
... ... ...
51 Philippines 0.481905
55 Saudi Arabia 0.480588
23 Greece 0.473276
45 Nigeria 0.470287
29 India 0.460736

73 rows × 2 columns

In [35]:
# PLOT RESULTS 
fig1 = px.scatter_geo(merged_df, locations='iso_alpha3', color="subjectivity",
                     hover_name="country", size="subjectivity", 
                     title='Average subjectivity by country' )
fig1.update_geos(showcoastlines=True, coastlinecolor="white",
                 showocean=True, oceancolor="black")
fig1.show()

Figure 2.6

As for subjectivity, Portugal, Chile, Brazil, Thailand, and Panama have the highest average subjectivity scores among the countries analyzed. This means that these countries tend to listen to songs that likely contain subjective language and expressions of personal feelings or beliefs. In contrast, Philippines, Saudi Arabia, Greece, Nigeria, and India have the lowest subjectivity scores which means that they listen to songs with lyrics that are more objective and fact-based.

III. Emotions: NRCLex ¶

In [36]:
# IMPORT DEPENDENCIES 
from nrclex import NRCLex
In [37]:
def get_emotion(ls): 
    '''get dominant emotions'''
    emotions =[]
    for i in ls:
        text_obj = NRCLex(i)
        #print(text_object.raw_emotion_scores)
        emotion= text_obj.affect_frequencies
        max_key =max(emotion, key=emotion.get)
        emotions.append(max_key)
    return emotions
In [38]:
%%time
# GET EMOTIONS 
em = get_emotion(song_data['lyrics_clean'])
len(em)

CPU times: total: 5.25 s
Wall time: 5.38 s
Out[38]:
6809
In [39]:
# CONVERT TO DATAFRAME 
df_all1['emotions'] = em
df_all1.head()
Out[39]:
index country continent lyrics_clean neg neu pos compound polarity subjectivity emotions
0 0 United Arab Emirates Asia good gold dream sell right til build home watc... 0.085 0.231 0.685 0.9995 0.499696 0.549696 positive
1 1 United Arab Emirates Asia fan even though salty hate see broad know happ... 0.161 0.401 0.438 0.9953 0.270764 0.394594 positive
2 2 United Arab Emirates Asia take look inside heart room room would hold br... 0.208 0.446 0.346 0.9784 0.439015 0.563258 negative
3 3 United Arab Emirates Asia another banger baby calm calm girl body put he... 0.042 0.605 0.353 0.9970 0.141603 0.504190 positive
4 4 United Arab Emirates Asia believe man want somebody say saw person kiss ... 0.084 0.654 0.261 0.9814 0.152885 0.368269 positive
In [40]:
# PLOT RESULTS 
fig = px.histogram(df_all1, x="emotions", color = 'emotions', 
                   color_discrete_sequence=px.colors.qualitative.Dark2,
                  title = 'Emotion-wise distribution of lyrics', template = 'plotly_dark')
fig.show()

Figure 2.7

Upon examining this graph, it becomes apparent that popular songs predominantly express positive emotions and a relatively small number of song lyrics convey feelings of anger. This is useful because it may enable us to draw inferences about the collective emotions of music listeners and identify any global social or political issues that may be contributing to this trend.

In [51]:
# PLOT EMOTIONS BY REGION 
fig = px.histogram(df_all1, x="emotions", color = 'continent', 
                   color_discrete_sequence=px.colors.qualitative.Pastel, 
                  title = 'Regional lyrics emotions', template = 'plotly_dark')
fig.show()

Figure 2.8

It appears that the most songs with positive emotions are popular in European and Asian countries.

IV. Sentiment: Afinn ¶

In [54]:
# IMPORT DEPENDENCIES
from afinn import Afinn
In [55]:
# COMPUTE SENTIMENT SCORE FOR EACH LYRICS 
afinn = Afinn() #intiate afinn object 
sentiment_scores = []
for text in song_data['lyrics_clean']:
    sentiment_scores.append(afinn.score(text))

# ASSIGN SENTIMENT LABELS BASED ON SENTIMENT SCORES 
sentiment_labels = ['positive' if score > 0 else 'negative' 
                    if score < 0 else 'neutral' 
                    for score in sentiment_scores]
print(sentiment_labels) #print 

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'positive', 'positive', 'negative']

Improvement plan: Collect large amount of lyrics training data with labels to train a model on and use trained model to predict sentiment of the lyrics in this data

In [56]:
def sentimentscore_to_label(x):
    '''convert sentiment scores to label'''
    if(x >= -1 and x < 0):
        return 'negative'
    if(x == 0):
        return 'neutral'
    if(x > 0 and x <= 1):
        return 'positive'
#song_data.label = song_data.label.apply(polarity_to_label)
In [57]:
# COMBINE ALL 
df_all1['streams']= song_data['streams']
df_all1['sentiment_afinn'] = sentiment_labels #afinn sentiment 
df_all1['sentiment_compound'] = [sentimentscore_to_label(i) for i in df_all1['compound']] 
df_all1['sentiment_polarity'] = [sentimentscore_to_label(i) for i in df_all1['polarity']] 
df_all1.head()
Out[57]:
index country continent lyrics_clean neg neu pos compound polarity subjectivity emotions streams sentiment_afinn sentiment_compound sentiment_polarity
0 0 United Arab Emirates Asia good gold dream sell right til build home watc... 0.085 0.231 0.685 0.9995 0.499696 0.549696 positive 124198.0 positive positive positive
1 1 United Arab Emirates Asia fan even though salty hate see broad know happ... 0.161 0.401 0.438 0.9953 0.270764 0.394594 positive 106927.0 positive positive positive
2 2 United Arab Emirates Asia take look inside heart room room would hold br... 0.208 0.446 0.346 0.9784 0.439015 0.563258 negative 83627.0 positive positive positive
3 3 United Arab Emirates Asia another banger baby calm calm girl body put he... 0.042 0.605 0.353 0.9970 0.141603 0.504190 positive 79714.0 positive positive positive
4 4 United Arab Emirates Asia believe man want somebody say saw person kiss ... 0.084 0.654 0.261 0.9814 0.152885 0.368269 positive 79488.0 positive positive positive
In [58]:
# GET DOMINANT SENTIMENT 
df = df_all1[['emotions','sentiment_afinn','sentiment_compound','sentiment_polarity']]
df_all1['max_sentiment'] = [ df.loc[i, :].value_counts().idxmax() for i in range(len(df_all1['emotions']))]
df_all1.head()
Out[58]:
index country continent lyrics_clean neg neu pos compound polarity subjectivity emotions streams sentiment_afinn sentiment_compound sentiment_polarity max_sentiment
0 0 United Arab Emirates Asia good gold dream sell right til build home watc... 0.085 0.231 0.685 0.9995 0.499696 0.549696 positive 124198.0 positive positive positive positive
1 1 United Arab Emirates Asia fan even though salty hate see broad know happ... 0.161 0.401 0.438 0.9953 0.270764 0.394594 positive 106927.0 positive positive positive positive
2 2 United Arab Emirates Asia take look inside heart room room would hold br... 0.208 0.446 0.346 0.9784 0.439015 0.563258 negative 83627.0 positive positive positive positive
3 3 United Arab Emirates Asia another banger baby calm calm girl body put he... 0.042 0.605 0.353 0.9970 0.141603 0.504190 positive 79714.0 positive positive positive positive
4 4 United Arab Emirates Asia believe man want somebody say saw person kiss ... 0.084 0.654 0.261 0.9814 0.152885 0.368269 positive 79488.0 positive positive positive positive

V. ANOVA ¶

Question: Does popularity measured by number of streams change according to different sentiments?

In [61]:
# IMPORT DEPENDENCIES 
import researchpy as rp
import scipy.stats as stats
from statsmodels.formula.api import ols
import statsmodels.api as sm
In [62]:
# GET SUMMARY OF STREAMS BY DOMINANT SENTIMENT
rp.summary_cont(df_all1['streams'].groupby(df_all1['max_sentiment']))
Out[62]:
N Mean SD SE 95% Conf. Interval
max_sentiment
negative 1601 419954.1443 898650.5880 22459.2473 375901.5041 4.640068e+05
neutral 4 477886.7500 732376.0070 366188.0035 -687486.9088 1.643260e+06
positive 4724 449741.8254 984073.6010 14317.6795 421672.4959 4.778112e+05
trust 3 91571.6667 112031.3244 64681.3153 -186729.5712 3.698729e+05
In [69]:
# FIT OLS WITH DOMINANT SENTIMENT 
model = ols('streams ~ C(max_sentiment)', data=df_all1).fit()
aov_table = sm.stats.anova_lm(model, typ=2)
aov_table
Out[69]:
sum_sq df F PR(>F)
C(max_sentiment) 1.434785e+12 3.0 0.515796 0.671392
Residual 5.867508e+15 6328.0 NaN NaN

The p-values are much greater than the significance level (0.05), which indicates that the observed difference in means is very likely to have occurred by chance, and therefore provides evidence against the alternative hypothesis. Hence, there is no statistically significant difference between the popularity of different sentiments. In other words, popularity of songs does not change according to sentiment.