LIWC analysis
Joseph
2/5/2021
Q1 MFD
This analysis used the extended moral foundations dictionary available on: http://github.com/medianeuroscience/emfd/tree/master/dictionaries. I pulled this link from the Hopp et al. (2020) paper you sent me.
I used LIWC + emfd to analyze words in the first response to “List some (5) words that come to mind when you think of holding a grudge”. Below you’ll see 2 graphs. The first is for the percent of mentions in each mfd category, and the second is the the number of counts of each word category. So they’re basically the same graph, the only difference is the first one is normalized to the total words. My initial plan was to just plot the percent (first graph) but when I did, I noticed the numbers were super low (i.e. the highest value was 4%), so I replotted with just the counts to see how many words were being captured. Turns out the MFD dictionary was only capturing 13 words out of over 400 words in our list. I looked through the dictionary words themselves to ensure its not an error. It wasn’t an error. Many of our words were just not in the mfd dictionary. For example, the word “anger”, which is our most common word, was nonexistent in the MFD. Which is what made me suggest we use our own custom dictionary, or perhaps a hybrid of other dictionaries as you suggested. A caveat is this is only for the question 1, and perhaps the other questions would capture more moral-foundation-esque words. I will run this analysis for the questions then let you know.
Which words did mfd pick up?
At the end of this code chunk I’ve paginated the data in a table, so you can browse all of its rows and columns. There are 27 rows in total and many are repeat rows.
df.desc1_mfd_anal %>%
slice(-1) %>%
dplyr::rename(Word = "Source (A)") %>%
select(-WC, -Dic) %>%
pivot_longer(cols = -Word,
names_to = "LIWC_categories",
values_to = "check") %>%
separate(LIWC_categories, c("Number", "Category"), " ") %>% #Spacing = 10 tabs!!
dplyr::select(-Number) %>%
drop_na() %>%
filter(check != 0) %>%
select(-check) %>%
tbl_df %>%
rmarkdown::paged_table()Graph 1:
Percent of words in each mfd category
# Normalized using Analyze Text =================== #
df.desc1_mfd_anal <- df.desc1_mfd_anal %>%
slice(-1) %>%
dplyr::rename(Word = "Source (A)") %>%
select(-WC, -Dic) %>%
pivot_longer(cols = -Word,
names_to = "LIWC_categories",
values_to = "check") %>%
separate(LIWC_categories, c("Number", "Category"), " ") %>% #Spacing = 10 tabs!!
dplyr::select(-Number) %>%
drop_na() %>%
group_by(Category) %>%
dplyr::summarize(mean = mean(check)) %>%
slice(1:11)
df.desc1_mfd_anal %>%
ggplot(aes(x = Category, y = mean, color = Category)) +
geom_point() +
labs(y = "Mean mentions (%)")
Graph 2:
Count of words in each category.
You will notice that this plot has only 5 categories (vs 11 in the previous one). That’s because the script I wrote dropped all NAs aka columns with 0 word mentions.
I’ve also paginated the table that shows the counts of words. The count column adds up to 13. So the dictionary only captured 13 out of the over 400 words we fed it.
# Count Breakdown ================================= #
df.desc1_mfd_cat <- df.desc1_mfd_cat %>%
slice(-1, -2) %>%
pivot_longer(cols = -Word,
names_to = "LIWC_categories",
values_to = "check") %>%
separate(LIWC_categories, c("Number", "Category"), " ") %>% #Spacing = 10 tabs!!
dplyr::select(-Number) %>%
drop_na() %>%
group_by(Category) %>% #at this point we're more interested in categories than the words
dplyr::summarize(mentions = n())
df.desc1_mfd_cat %>%
tbl_df %>%
rmarkdown::paged_table()df.desc1_mfd_cat %>%
ggplot(aes(x = Category, y = mentions, color = Category)) +
geom_point() +
labs(y = "Absolute mentions") 
Our data vs mfd
I uploaded the moral foundations dictionary here just so you could see a side-by-side of what types of words it has. Many of the words have similar themes with the ones in our data, it’s just that our words aren’t an exact match to the dictionary words. So, for example, the mfd has variations of “offend” but it doesn’t have the word “anger” and our list is the opposite; we have the latter but we don’t have the former.
df.mft_original <- df.mft_original %>%
slice(14:346) %>%
dplyr::rename(Words = "%") %>%
separate(Words, c("MFT_Words", "Symbols", "*")) %>%
dplyr::select(-Symbols, -"*")
df.desc1_words <- df.desc1_words %>%
dplyr::rename(Our_RawData = "Source (A)") %>%
select(Our_RawData) %>%
slice(-1) %>%
drop_na() %>%
slice(1:333) #top 33 rows
cbind(df.mft_original, df.desc1_words)## MFT_Words
## 1 safe
## 2 peace
## 3 compassion
## 4 empath
## 5 sympath
## 6 care
## 7 caring
## 8 protect
## 9 shield
## 10 shelter
## 11 amity
## 12 secur
## 13 benefit
## 14 defen
## 15 guard
## 16 preserve
## 17 <NA>
## 18 harm
## 19 suffer
## 20 war
## 21 wars
## 22 warl
## 23 warring
## 24 fight
## 25 violen
## 26 hurt
## 27 kill
## 28 kills
## 29 killer
## 30 killed
## 31 killing
## 32 endanger
## 33 cruel
## 34 brutal
## 35 abuse
## 36 damag
## 37 ruin
## 38 ravage
## 39 detriment
## 40 crush
## 41 attack
## 42 annihilate
## 43 destroy
## 44 stomp
## 45 abandon
## 46 spurn
## 47 impair
## 48 exploit
## 49 exploits
## 50 exploited
## 51 exploiting
## 52 wound
## 53 <NA>
## 54 fair
## 55 fairly
## 56 fairness
## 57 fair
## 58 fairmind
## 59 fairplay
## 60 equal
## 61 justice
## 62 justness
## 63 justifi
## 64 reciproc
## 65 impartial
## 66 egalitar
## 67 rights
## 68 equity
## 69 evenness
## 70 equivalent
## 71 unbias
## 72 tolerant
## 73 equable
## 74 balance
## 75 homologous
## 76 unprejudice
## 77 reasonable
## 78 constant
## 79 honest
## 80 <NA>
## 81 unfair
## 82 unequal
## 83 bias
## 84 unjust
## 85 injust
## 86 bigot
## 87 discriminat
## 88 disproportion
## 89 inequitable
## 90 prejud
## 91 dishonest
## 92 unscrupulous
## 93 dissociate
## 94 preference
## 95 favoritism
## 96 segregat
## 97 exclusion
## 98 exclud
## 99 <NA>
## 100 together
## 101 nation
## 102 homeland
## 103 family
## 104 families
## 105 familial
## 106 group
## 107 loyal
## 108 patriot
## 109 communal
## 110 commune
## 111 communit
## 112 communis
## 113 comrad
## 114 cadre
## 115 collectiv
## 116 joint
## 117 unison
## 118 unite
## 119 fellow
## 120 guild
## 121 solidarity
## 122 devot
## 123 member
## 124 cliqu
## 125 cohort
## 126 ally
## 127 insider
## 128 <NA>
## 129 foreign
## 130 enem
## 131 betray
## 132 treason
## 133 traitor
## 134 treacher
## 135 disloyal
## 136 individual
## 137 apostasy
## 138 apostate
## 139 deserted
## 140 deserter
## 141 deserting
## 142 deceiv
## 143 jilt
## 144 imposter
## 145 miscreant
## 146 spy
## 147 sequester
## 148 renegade
## 149 terroris
## 150 immigra
## 151 obey
## 152 obedien
## 153 duty
## 154 law
## 155 lawful
## 156 legal
## 157 duti
## 158 honor
## 159 respect
## 160 respectful
## 161 respected
## 162 respects
## 163 order
## 164 father
## 165 mother
## 166 motherl
## 167 mothering
## 168 mothers
## 169 tradition
## 170 hierarch
## 171 authorit
## 172 permit
## 173 permission
## 174 status
## 175 rank
## 176 leader
## 177 class
## 178 bourgeoisie
## 179 caste
## 180 position
## 181 complian
## 182 command
## 183 supremacy
## 184 control
## 185 submi
## 186 allegian
## 187 serve
## 188 abide
## 189 defere
## 190 defer
## 191 revere
## 192 venerat
## 193 comply
## 194 <NA>
## 195 defian
## 196 rebel
## 197 dissent
## 198 subver
## 199 disrespect
## 200 disobe
## 201 sediti
## 202 agitat
## 203 insubordinat
## 204 illegal
## 205 lawless
## 206 insurgent
## 207 mutinous
## 208 defy
## 209 dissident
## 210 unfaithful
## 211 alienate
## 212 defector
## 213 heretic
## 214 nonconformist
## 215 oppose
## 216 protest
## 217 refuse
## 218 denounce
## 219 remonstrate
## 220 riot
## 221 obstruct
## 222 <NA>
## 223 piety
## 224 pious
## 225 purity
## 226 pure
## 227 clean
## 228 steril
## 229 sacred
## 230 chast
## 231 holy
## 232 holiness
## 233 saint
## 234 wholesome
## 235 celiba
## 236 abstention
## 237 virgin
## 238 virgins
## 239 virginity
## 240 virginal
## 241 austerity
## 242 integrity
## 243 modesty
## 244 abstinen
## 245 abstemiousness
## 246 upright
## 247 limpid
## 248 unadulterated
## 249 maiden
## 250 virtuous
## 251 refined
## 252 decen
## 253 immaculate
## 254 innocent
## 255 pristine
## 256 church
## 257 <NA>
## 258 disgust
## 259 deprav
## 260 disease
## 261 unclean
## 262 contagio
## 263 indecen
## 264 sin
## 265 sinful
## 266 sinner
## 267 sins
## 268 sinned
## 269 sinning
## 270 slut
## 271 whore
## 272 dirt
## 273 impiety
## 274 impious
## 275 profan
## 276 gross
## 277 repuls
## 278 sick
## 279 promiscu
## 280 lewd
## 281 adulter
## 282 debauche
## 283 defile
## 284 tramp
## 285 prostitut
## 286 unchaste
## 287 intemperate
## 288 wanton
## 289 profligate
## 290 filth
## 291 trashy
## 292 obscen
## 293 lax
## 294 taint
## 295 stain
## 296 tarnish
## 297 debase
## 298 desecrat
## 299 wicked
## 300 blemish
## 301 exploitat
## 302 pervert
## 303 wretched
## 304 <NA>
## 305 righteous
## 306 moral
## 307 ethic
## 308 value
## 309 upstanding
## 310 good
## 311 goodness
## 312 principle
## 313 blameless
## 314 exemplary
## 315 lesson
## 316 canon
## 317 doctrine
## 318 noble
## 319 worth
## 320 ideal
## 321 praiseworthy
## 322 commendable
## 323 character
## 324 proper
## 325 laudable
## 326 correct
## 327 wrong
## 328 evil
## 329 immoral
## 330 bad
## 331 offend
## 332 offensive
## 333 transgress
## Our_RawData
## 1 revenge
## 2 Resentment
## 3 pain
## 4 cold
## 5 Resentment
## 6 wanting to retaliate
## 7 exes
## 8 Petty
## 9 Anger
## 10 Hurt
## 11 petty
## 12 anger
## 13 Anger
## 14 stubborn
## 15 sad
## 16 anger
## 17 anger
## 18 Judgement
## 19 angry
## 20 petty
## 21 Stubborn
## 22 Anger
## 23 Ignore
## 24 angry
## 25 resentment
## 26 Betrayal
## 27 Hate
## 28 angry
## 29 Anger
## 30 Shame
## 31 anger
## 32 betrayed
## 33 Anger
## 34 Revenge
## 35 jealous
## 36 resentment
## 37 Justice
## 38 anger
## 39 Betrayal
## 40 Anger
## 41 mad
## 42 stuck
## 43 Stressful
## 44 anger
## 45 Festering
## 46 Petty
## 47 Waste
## 48 Resentment
## 49 Stubborn
## 50 anger
## 51 Consuming
## 52 Lingering
## 53 resentment
## 54 pissed
## 55 Hurt
## 56 resent
## 57 anger
## 58 Stingy
## 59 Anger
## 60 Anger
## 61 pride
## 62 Irritability
## 63 bitterness
## 64 Anger
## 65 anger
## 66 anger
## 67 anger
## 68 painful
## 69 Madness
## 70 Hate
## 71 Petty
## 72 Time
## 73 revenge
## 74 resentful
## 75 anger
## 76 Hate
## 77 Anger
## 78 Angry
## 79 mad
## 80 Drinking poison
## 81 Anger
## 82 Wrong
## 83 spite
## 84 anger
## 85 unfair
## 86 family
## 87 friends
## 88 Stony
## 89 Revenge
## 90 negative
## 91 Rage
## 92 angry
## 93 exhausting
## 94 Anger
## 95 Anger
## 96 Resentment
## 97 anger
## 98 Resentment
## 99 Angry
## 100 revenge
## 101 nothing
## 102 anger
## 103 resent someone
## 104 Immature
## 105 petty
## 106 angry
## 107 resentment
## 108 revenge
## 109 anger
## 110 Vengeance
## 111 Anger
## 112 angry
## 113 the silent treatment
## 114 mad
## 115 Petty
## 116 betrayal
## 117 Anger
## 118 Emotional
## 119 anger
## 120 angry
## 121 annoying
## 122 Angry
## 123 anger
## 124 Stubborn
## 125 HURT
## 126 Judgemental
## 127 Anger
## 128 Hate
## 129 anger
## 130 Immature
## 131 hate
## 132 bitter
## 133 Revenge
## 134 Unhealthy
## 135 Long
## 136 negative
## 137 stubborn
## 138 anger
## 139 Anger
## 140 Upset
## 141 Anger
## 142 bitter
## 143 contempt
## 144 scared
## 145 anger
## 146 frustrating
## 147 Decided
## 148 stodgy
## 149 angry
## 150 revenge
## 151 angry
## 152 Quarrel
## 153 anger
## 154 pain
## 155 petty
## 156 Anger
## 157 anger
## 158 Anger
## 159 anger
## 160 Anger
## 161 Bitterness
## 162 vice
## 163 Punishment
## 164 Stress
## 165 mad
## 166 Stupidity
## 167 bitch
## 168 Justice
## 169 angry
## 170 Pettiness
## 171 small
## 172 bitterness
## 173 Anger
## 174 betrayal
## 175 anger
## 176 Hurt the person
## 177 bitter
## 178 anger
## 179 anger
## 180 stubborn
## 181 Petty
## 182 Angry
## 183 Hurt
## 184 Anger
## 185 Anger
## 186 Hurt
## 187 mean
## 188 anger
## 189 Spite
## 190 hate
## 191 Anger
## 192 shock
## 193 anger
## 194 hurt
## 195 angry
## 196 why
## 197 i don't know
## 198 anger
## 199 anger
## 200 anger
## 201 resentment
## 202 anger
## 203 upset
## 204 stubborn
## 205 Two wrongs don't make a right
## 206 angry
## 207 forgiveness
## 208 Bitter
## 209 Anger
## 210 revenge
## 211 anger
## 212 Anger
## 213 futility
## 214 anger
## 215 resentment
## 216 sadness
## 217 anger
## 218 Irrational
## 219 hate
## 220 Resentment
## 221 angry
## 222 troublesome
## 223 Jealously
## 224 revenge
## 225 angry
## 226 anger
## 227 revenge
## 228 anger
## 229 Anger
## 230 fight
## 231 hurt
## 232 vengeful
## 233 angee
## 234 anger
## 235 Anger
## 236 angry
## 237 Bitter
## 238 Angry
## 239 History
## 240 anger
## 241 destroy
## 242 Petty
## 243 ignoring
## 244 anger
## 245 When we think about them, i feelings are negative.
## 246 Past
## 247 Pissed
## 248 Pain
## 249 Anger
## 250 Anger
## 251 revenge
## 252 emotional
## 253 angry
## 254 Fight
## 255 unforgiving
## 256 Murder
## 257 point-less
## 258 Resentful
## 259 bitter
## 260 revenge
## 261 betrayal
## 262 mad
## 263 Anger
## 264 angry
## 265 revenge
## 266 regret
## 267 focuse on present
## 268 Revenge
## 269 anger
## 270 forgive
## 271 resentment
## 272 pain
## 273 apology
## 274 kill
## 275 tired
## 276 hatred
## 277 Bitterness
## 278 intention
## 279 REVENGE
## 280 vengeful
## 281 mistake
## 282 pissed off
## 283 Anger
## 284 Anger
## 285 Anger
## 286 annoyance
## 287 inspirational
## 288 regret
## 289 Hatred
## 290 hatred
## 291 Betrayal
## 292 I feel like to settle issues even when i'm the one on the right.
## 293 Anger
## 294 victim
## 295 nervous
## 296 revenge
## 297 move on
## 298 Serious
## 299 anger
## 300 anger
## 301 forgiveness
## 302 rancor
## 303 bitterness
## 304 anger
## 305 Lost
## 306 mad
## 307 shrewd
## 308 stubborn
## 309 revenge
## 310 Anger
## 311 Anger
## 312 anger
## 313 Anger
## 314 familiar
## 315 Get even
## 316 Anger
## 317 Rage
## 318 Solitude
## 319 Irritated
## 320 Resentment
## 321 Single minded
## 322 negative
## 323 anger
## 324 Anger
## 325 Anger
## 326 angry
## 327 Irrational
## 328 Weak
## 329 Revenge
## 330 anger
## 331 Angry
## 332 anger
## 333 fightQ2 MFD
- Describe a circumstance where you think its right to hold a grudge
- Describe a circumstance where you think its wrong to hold a grudge
Q3 MFD
For what reason do people typically hold grudges?
Q4 MFD
List some (5) emotions you think people feel when they hold a grudge
Q5 MFD
Describe a circumstance where you have held a grudge