#DISSATISFACTION Tumblr posts

  • Disconnected (Out of Touch)

    Trapt

    You never listen to me,

    You cannot look me in the eyes

    And I have struggled to see, why its so easy to push me aside

    I no longer believe that you were ever at my side

    How could you know what I need? When I am the last thing on your mind

    Too out of touch out of touch

    To touch you [Repeat: x3]

    So disconnected go through the motions

    You get so disconnected everything goes over your

    Head so disconnected,

    You got me hanging by a thread

    So disconnected

    When will this cycle end?

    You don’t really know me

    I don’t think you ever even tried

    Were on the same routine and still you never have the time

    Who do you want me to be?

    And do you want me in your life?

    I feel so incomplete, you left me too far behind

    Too out of touch out of touch

    To touch you [Repeat: x3]

    So disconnected go through the motions

    You get so disconnected everything goes over your

    Head so disconnected,

    You got me hanging by a thread

    So disconnected

    When will this cycle end?

    Its to hard to just move on its too hard to just move on

    It’s easier said then done [Repeat: x3]

    Too out of touch out of touch

    To touch you [Repeat: x3]

    So disconnected go through the motions

    You get so disconnected everything goes over your

    Head so disconnected,

    You got me hanging by a thread

    So disconnected

    When will this cycle end?

    So disconnected so disconnected

    When will this cycle end?

    Source: LyricFind

    Songwriters: Deena Henry / Brandon Charles Brown / Ian Eskelin / Seth Anderson / Jaqueline Anderson

    Disconnected (Out of Touch) lyrics © Sony/ATV Music Publishing LLC, Warner Chappell Music, Inc

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  • Here, leave it on my chair

    Here, would you like to wear?

    I feel my soul

    Inside your tears

    Now can’t you see

    I do mind?

    And what I feel

    Is what you need

    Don’t turn around

    Here, I’m covered by your face, oh

    Here are moments of that days

    So stay alone

    With all your fears

    Now can’t you see I do mind?

    My everlasting aching seam.

    Don’t turn around

    Each moment of that day

    Run over me

    And I got to stay

    I’m so alone

    On empty scene

    Now can’t you see

    I do mind?

    I’m so alone

    You might never see it!

    Source: Musixmatch

    Songwriters: Ema Brabcova / Filip Misek

    Pathetic lyrics © Emi April Music Inc Obo Emi Music Pub Czech Republic

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  • In this post, I will try my hand at some more data management techniques on the dataframe created in the last post (link).

    Dealing with Missing Values

    Upon inspection, I found that in all my variables missing information was coded as ‘-1’. Therefore, I first went ahead and replaced it with missing values (nan in python).

    #Replacing all ’-1’ with nan

    dat.iloc[:,:] = dat.iloc[:,:].replace(-1, np.nan) #numpy will be required for .nan

    New Variables

    I then created two new variables ‘Dissatisfaction’ and ‘Activism from the existing variables by summing the relevant ones. For example, ‘Gen_Anger’ (which represents general anger among people), ‘Gov_Apathy’ (how strongly people feel government is sensitive to them) and ‘Ex_Apathy(how far people feel government officials are sensitive towards them) were combined to get a dissatisfaction score. Similarly, variables of Protest, March and Petition were combined to get a score for ‘Activism’.

    #Created new variables Dissatisfaction and Activism

    dat[‘Dissatisfaction’] = dat[['Gen_Anger’, 'Gov_Apathy’, 'Ex_Apathy’]].sum(axis=1)

    dat['Activism’] = dat[['Protest’, 'March’, 'Petition’]].sum(axis=1)

    Recoding Variables

    Variables of ‘Religiosity’ and ‘Hope’ were recoded so that higher values reflected higher strength of the variable i.e. higher score on religiosity means person is more religious.

    #Recoding values of Religiosity to make it more logical i.e. higher scores reflect more of it

    dat['Religiosity’] = dat['Religiosity’].replace({1:6, 2:5, 3:4, 4:3, 5:2, 6:1, 7:np.nan})

    dat['Hope’] = dat['Hope’].replace({1:7, 2:6, 3:5, 5:3, 6:2, 7:1})

    Created New Variables by Collapsing Existing Variables

    Some variables had a large number of categories (e.g. religiosity) while some others would be more meaningful if categorized. Therefore, I created four new variables by collapsing and categorizing those variables which were of interest to my problem. The new variables are ‘Dis_Ten’ which was collapsed from Dissatisfaction, ‘Act_Ten’ which was collapsed from ‘Activism’ etc.

    #Created new variables by recoding existing variables

    dat['Dis_Ten’] = pd.qcut(dat['Dissatisfaction’], 4, labels = ['Low’, 'Med’, 'High’, 'Ext’])

    dat['Act_Ten’] = pd.cut(dat['Activism’], 3, labels = ['Low’, 'Med’, 'High’])

    dat['Rel_Ten’] = pd.cut(dat['Activism’], 3, labels = ['Low’, 'Med’, 'High’])

    dat['OL’] = pd.cut(dat['Hope’], 3, labels = ['Low’, 'Med’, 'High’] )

    Displaying Frequencies and Proportions

    Then the frequencies and proportions of these collapsed variables are as follows:

    Dissatisfaction:

     #Displaying Frequencies and Proportions of variables of interest

    print('Dissatisfaction among respondents:’)

    print ('Count:\n’, dat['Dis_Ten’].value_counts(sort=True))

    print ('Proportion:\n’, dat['Dis_Ten’].value_counts(sort=True, normalize=True))


    Dissatisfaction among respondents:
    Count:
    Low     940
    High    646
    Med     360
    Ext     348
    Name: Dis_Ten, dtype: int64
    Proportion:
    Low     0.409765
    High    0.281604
    Med     0.156931
    Ext     0.151700

     It can be seen that nearly 43% of respondents are highly or extremely dissatisfied with the state of affairs.

     

    Religiosity:

    In the last post (link) we have already seen the distribution of respondents according to the religion they follow. Here we wanted to see the strength of their religious beliefs.

    print('Religiosity among respondents:’)

    print ('Count:\n’, dat['Rel_Ten’].value_counts(sort=True))

    print ('Proportion:\n’, dat['Rel_Ten’].value_counts(sort=True, normalize=True))

    Religiosity among respondents:
    Count:
    High    1998
    Med      225
    Low       71
    Name: Rel_Ten, dtype: int64
    Proportion:
    High    0.870968
    Med     0.098082
    Low     0.030950

    Surprisingly, 87% of the respondents report high religiosity. Yet, the dissatisfaction is high. This looks like something different than what we expected based on the literature review done earlier.  

    Activism:

    Next we look at Activism among the respondents:

     print('Activism among respondents:’)

    print ('Count:\n’, dat['Act_Ten’].value_counts(sort=False))

    print ('Proportion:’, dat['Act_Ten’].value_counts(sort=False, normalize=True))

    Activism among respondents:
    Count:
    Low       71
    Med      225
    High    1998
    Name: Act_Ten, dtype: int64
    Proportion: Low     0.030950
    Med     0.098082
    High    0.870968

     This indicates that nearly 86% of the respondents were high on activism (by our criteria). This is again surprising because I was thinking Activism would be low when Religiosity is high.

     Hopefulness:

    It is also important to look at hopefulness in the respondents. We did this by using a single item from Ool dataset (W1_F3) that asked the respondents how true they thought that typical American dream was.  However, we used hopefulness by collapsing and categorizing values on Hope into Low, Med and High for Low, Medium and High respectively.

     print('Hopefullness among respondents:’)

    print ('Count:\n’, dat['OL’].value_counts(sort=False))

    print ('Proportion:\n’, dat['OL’].value_counts(sort=False, normalize=True))

    Hopefullness among respondents:
    Count:
    Low     482
    Med     796
    High    972
    Name: OL, dtype: int64
    Proportion:
    Low     0.214222
    Med     0.353778
    High    0.432000

    This too presents an unpleasant picture. With only 43% Americans still thinking that the American dream is achievable for everyone (potentially).

    In this post we have looked at utilizing some of the data management techniques for dataframe. However, the findings here cannot be taken as conclusive because they are mere description and further inferential techniques will be required to see whether they support my hypothesis. We shall continue our journey in future posts.

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  • image
    #releasing my anger through art #art#art therapy#anger#dissatisfaction
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  • Ennui at 2am.

    She loves me all that she can

    And her ways to mine resign

    But she was not made for any man

    AND SHE NEVER WILL BE ALL MINE.

    -Edna St. Vincent Millay

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  • diss-satisfaction

    colored pencils on paper, 2020

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  • diss-tanz

    colored pencils on paper, 2020

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  • Lost rainbows vanish

    unfounded dreams simply die

    breathing becomes hard.

    .

    D W Eldred

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  • Do you ever feel life you should be doing something at the moment but you don’t know what and at the same time you can’t bring yourself to do anything so it results in you laying on top of your bed for 1+ hours doing nothing with a sense of extreme dissatisfaction with life and where you are in it at the moment and the knowledge that you can’t do anything to change it or is that just me?

    #tired#dissatisfaction #why does life have to be the way it is #im so tired #i just want to sleep until i have motivation to actually do something again #it's been a long time since i had that motivation
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  • When you keep talking to yourself time flies so soon. You don’t feel like waiting for anything immediate. Just an endless musing over eternal dissatisfactions.

    — 6:38 pm, 16 May 2020, Saturday.

    #time#introspection #talking to myself #dissatisfaction#musings#thoughts#journals#tumblr #tumblr writing society #tumblr writing community #me#dark
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  • Untitled

    -

    Why do I bother writing?

    As if it changes some part of me.

    As if it fixes what inspired me to speak.

    As if it can morph me into something new.

    As if these words could climb up from these pages

    And hold and comfort me.

    Why do I bother writing?

    When each time I reach the last line

    I am still just as fucked up as I was when I began.

    -

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  • Theory time! After the COVID-19 quarantine, we’ll probably be happy for just living because there might not be as much stress. 

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