November 7, 2019
Introduction
What is text analysis?
- As statisticians and data scientistis, why restrict ourselves to numeric data?
- We can use text as data and employ statistical methods to derive patterns or trends
- Example: Given some song lyrics, can we determine who the singer is? What kinds of topics/sentiments does Lizzo like to sing about?
Tidytext
- We typically think of text as a (very) long string, but this format is quite hard to work with.
- Thus, we will work in the 'tidy text format': a table with one token per row and a document id
text <- c("We typically think of text as a (very) long string, but this format is quite hard to work with.",
"Specifically, we will work in the 'tidy text format': a table with one token per row")
text
## [1] "We typically think of text as a (very) long string, but this format is quite hard to work with." ## [2] "Specifically, we will work in the 'tidy text format': a table with one token per row"
text_df <- tibble(line = 1:2, text = text) text_df
## # A tibble: 2 x 2 ## line text ## <int> <chr> ## 1 1 We typically think of text as a (very) long string, but this forma… ## 2 2 Specifically, we will work in the 'tidy text format': a table with…
Tokenizing
- A token is a meaningful unit of text. For us, that will be a single word
- Tokenizing is simply splitting a body of text into tokens, which can achieved using the function
unnest_tokens()
tidy_text <- text_df %>% unnest_tokens(word, text) tidy_text
## # A tibble: 35 x 2 ## line word ## <int> <chr> ## 1 1 we ## 2 1 typically ## 3 1 think ## 4 1 of ## 5 1 text ## 6 1 as ## 7 1 a ## 8 1 very ## 9 1 long ## 10 1 string ## # … with 25 more rows
Tokenizing cont.
- With the
unnest_tokens()function, we can easily format any body of text into a user-friendly data frame - First argument: name of column/variable that text is being unnested into
- Second argument: name of column/variable that is to be unnested
- Other details:
- All other columns kept
- Punctuation removed
- Defaults is to convert tokens to lowercase
Twitter Data
Democratic candidate tweets
- Time to work with some fun data!
- We have pulled tweets from four Democratic candidates: Joe Biden, Kamala Harris, Bernie Sanders, and Elizabeth Warren
## screen_name ## 1 JoeBiden ## 2 JoeBiden ## 3 JoeBiden ## text ## 1 .@DrBiden and I are sending our best wishes to @BernieSanders, Jane, and the whole Sanders family. Anyone who knows Bernie understands what a force he is. We are confident that he will have a full and speedy recovery and look forward to seeing him on the trail soon. ## 2 President Trump is morally unfit to lead our country. https://t.co/r3SKYqK26K ## 3 President Trump asked a foreign government to interfere in our elections, and now he's spending $10 million on attack ads against me. It's clear that he is trying to influence the primary and pick his opponent.\n\nWhy? Because he knows I’ll beat him like a drum.
Tidy tweets
remove_reg <- "&|<|>" tidy_tweets <- tweets %>% mutate(text = str_remove_all(text, remove_reg))%>% unnest_tokens(word, text, token = "tweets") tidy_tweets %>% slice(1:5)
## timestamp screen_name word ## 1 2019-10-02 15:28:06 JoeBiden drbiden ## 2 2019-10-02 15:28:06 JoeBiden and ## 3 2019-10-02 15:28:06 JoeBiden i ## 4 2019-10-02 15:28:06 JoeBiden are ## 5 2019-10-02 15:28:06 JoeBiden sending
Word counts
- What are the most commonly tweeted words by these candidates?
tidy_tweets %>% count(word, sort = T)
## # A tibble: 14,542 x 2 ## word n ## <chr> <int> ## 1 the 6213 ## 2 to 5955 ## 3 and 4145 ## 4 a 2918 ## 5 of 2794 ## 6 in 2616 ## 7 we 2421 ## 8 for 2418 ## 9 is 1841 ## 10 our 1840 ## # … with 14,532 more rows
Stop words
- Common/non-distinctive words
data("stop_words")
head(stop_words)
## # A tibble: 6 x 2 ## word lexicon ## <chr> <chr> ## 1 a SMART ## 2 a's SMART ## 3 able SMART ## 4 about SMART ## 5 above SMART ## 6 according SMART
Remove stop words
tidy_tweets <- tidy_tweets %>%
filter(!word %in% stop_words $ word,
!word %in% str_remove_all(stop_words$word, "'"),
str_detect(word, "[a-z]"))
tidy_tweets %>%
count(word, sort = T)
## # A tibble: 13,577 x 2 ## word n ## <chr> <int> ## 1 people 683 ## 2 president 670 ## 3 trump 532 ## 4 fight 461 ## 5 care 457 ## 6 country 452 ## 7 health 446 ## 8 plan 387 ## 9 time 387 ## 10 change 312 ## # … with 13,567 more rows
Word frequency
- While it's nice to get raw counts, we may prefer to know which words are used more often to other words. For this we can look towards word frequencies:
frequency_all <- tidy_tweets %>% count(word, sort = T) %>% mutate(freq = n / sum(n))
Frequency plot
ggplot(frequency_all %>% top_n(10, freq) %>%
mutate(word = reorder(word, freq)), aes(x = word, y = freq))+
geom_col()+
coord_flip()
Frequency by candidate
frequency <- tidy_tweets %>%
group_by(screen_name) %>%
count(word, sort = T) %>%
left_join(tidy_tweets %>%
group_by(screen_name) %>%
summarise(total = n()),
by = "screen_name") %>%
mutate(freq = n / total)
frequency %>% slice(1:4)
## # A tibble: 16 x 5 ## # Groups: screen_name [4] ## screen_name word n total freq ## <fct> <chr> <int> <int> <dbl> ## 1 BernieSanders people 244 17006 0.0143 ## 2 BernieSanders @berniesanders 175 17006 0.0103 ## 3 BernieSanders health 155 17006 0.00911 ## 4 BernieSanders care 149 17006 0.00876 ## 5 ewarren fight 221 17161 0.0129 ## 6 ewarren people 150 17161 0.00874 ## 7 ewarren plan 146 17161 0.00851 ## 8 ewarren change 145 17161 0.00845 ## 9 JoeBiden president 288 18523 0.0155 ## 10 JoeBiden trump 179 18523 0.00966 ## 11 JoeBiden country 150 18523 0.00810 ## 12 JoeBiden care 119 18523 0.00642 ## 13 KamalaHarris president 212 16408 0.0129 ## 14 KamalaHarris people 178 16408 0.0108 ## 15 KamalaHarris fight 134 16408 0.00817 ## 16 KamalaHarris trump 125 16408 0.00762
Sentiment analysis
Sentiment lexicons
- Now that we have looked at a per-word/token level, we may be interested in determining the general emotion of the text/author
- We can consider a word to have a particular emotion attached to it
- We can consider a piece of text as a combination of its individual words, so the sentiment of an entire text is the sum of the sentiments of its individual words
- There are three general-purpose sentiment lexicons: AFINN, bing, and nrc
AFINN
- AFINN assigns each word an integer score between -5 and 5
- Negative scores indicate negative sentiment, and positive scores indicate the opposite
get_sentiments("afinn")
## # A tibble: 2,476 x 2 ## word score ## <chr> <int> ## 1 abandon -2 ## 2 abandoned -2 ## 3 abandons -2 ## 4 abducted -2 ## 5 abduction -2 ## 6 abductions -2 ## 7 abhor -3 ## 8 abhorred -3 ## 9 abhorrent -3 ## 10 abhors -3 ## # … with 2,466 more rows
bing
- The bing lexicon categorizes words into one of two categories: 'positive' or 'negative'
get_sentiments("bing")
## # A tibble: 6,788 x 2 ## word sentiment ## <chr> <chr> ## 1 2-faced negative ## 2 2-faces negative ## 3 a+ positive ## 4 abnormal negative ## 5 abolish negative ## 6 abominable negative ## 7 abominably negative ## 8 abominate negative ## 9 abomination negative ## 10 abort negative ## # … with 6,778 more rows
nrc
- The nrc lexicon categorizes words into one of 10 categories: anger, anticipation, disgust, fear, joy, negative, positive, sadness, surprise, or trust.
get_sentiments("nrc")
## # A tibble: 13,901 x 2 ## word sentiment ## <chr> <chr> ## 1 abacus trust ## 2 abandon fear ## 3 abandon negative ## 4 abandon sadness ## 5 abandoned anger ## 6 abandoned fear ## 7 abandoned negative ## 8 abandoned sadness ## 9 abandonment anger ## 10 abandonment fear ## # … with 13,891 more rows
Important notes about sentiment lexicons
- Not every English word is in a lexicon
get_sentiments("bing") %>%
filter(word == "data")
## # A tibble: 0 x 2 ## # … with 2 variables: word <chr>, sentiment <chr>
- These lexicons do not account for qualifiers before a word (ex. "not happy") because these lexicons are constructed for one-word tokens only
- If we sum up each word's sentiment, it is possible for a piece of text to average out to a sentiment of zero/neutral
Sentiment of Elizabeth Warren's tweets: bing lexicon
- We will use the bing lexicon to estimate the sentiment of Elizabeth Warren's tweets:
tidy_tweets %>%
filter(screen_name == "ewarren") %>%
inner_join(get_sentiments("bing"), by = "word") %>%
count(sentiment) %>%
spread(sentiment, n) %>%
mutate(sentiment = positive - negative)
## # A tibble: 1 x 3 ## negative positive sentiment ## <int> <int> <int> ## 1 1118 1130 12
- It appears that Warren is pretty neutral…or is she?
Sentiment of Elizabeth Warren's tweets by month
- What if we look at Warren's tweets by month?
tidy_tweets %>%
filter(screen_name == "ewarren") %>%
inner_join(get_sentiments("bing"), by = "word") %>%
count(months(timestamp), sentiment) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment = positive - negative)
## # A tibble: 4 x 4 ## `months(timestamp)` negative positive sentiment ## <chr> <dbl> <dbl> <dbl> ## 1 August 386 391 5 ## 2 July 322 312 -10 ## 3 October 27 27 0 ## 4 September 383 400 17
Most common positive/negative words
bing_counts <- tidy_tweets %>%
inner_join(get_sentiments("bing"), by = "word") %>%
count(word, sentiment, sort = T)
Customize stop words
- For this analysis, we should consider removing the word 'trump' as a word with positive connotation
- To do so, we can make a custom list of stop-words:
my_stop_words <- tibble(word = c("trump"), lexicon = c("custom"))
custom_stop_words <- bind_rows(my_stop_words, stop_words)
custom_stop_words
## # A tibble: 1,150 x 2 ## word lexicon ## <chr> <chr> ## 1 trump custom ## 2 a SMART ## 3 a's SMART ## 4 able SMART ## 5 about SMART ## 6 above SMART ## 7 according SMART ## 8 accordingly SMART ## 9 across SMART ## 10 actually SMART ## # … with 1,140 more rows
Most common positive/negative words with custom stop words
Comparing candidates
Word frequencies of Biden vs. Warren
- We can visualize the differences and similarities in word frequencies between two candidates:
# make into nice plot frequency <- frequency %>% select(screen_name, word, freq) %>% spread(screen_name, freq) %>% arrange(BernieSanders, ewarren, JoeBiden, KamalaHarris) # choose 2 candidates ggplot(frequency2, aes(JoeBiden, ewarren))+ # hide discreteness geom_jitter(alpha = 0.1, size = 2.5, width = 0.25, height = 0.25) + geom_text(aes(label = word), check_overlap = T, vjust = 1.5)+ scale_x_log10(labels = percent_format())+ scale_y_log10(labels = percent_format())+ geom_abline(color = "blue")
- Words near the blue line are used with roughly equal frequencies between Joe Biden and Elizabeth Warren
- Words far away from the blue line are favored much more by one candidate than the other
Probability of using a word
- We have examined which words have high tf-idf, that is, which words are distinctive for a particular candidate
- Now we can also explore questions such as: given a word, which candidate is more likely to use that word in a tweet?
- We will utilize the log-odds ratio
Odds ratio and log-odds ratio
- Below, we define the odds ratio for Candidate A versus Candidate B \[\text{OR}_{A:B}(word) = \dfrac{\text{odds}_A}{\text{odds}_B} = \dfrac{\text{Prob(A uses word)}}{\text{Prob(B uses word)}}\]
- The probability that Candidate A uses the word is the number of times A used the word, divided by the total number of words used by A: \[\text{Prob(A uses word)} = \dfrac{n_A}{total_A}\]
Log-odds ratio
\[ \log \text{OR}_{A:B}(word) = \log \left( \dfrac{n_A / total_A}{n_B / total_B}\right) \]
- Equal odds corresponds to OR = 1, which corresponds to log(OR) = 0
- If candidate A uses the word with higher probability, then log(OR) > 0
- We use the following approximation in case a candidate does not use the word at all \[ \log \text{OR}_{A:B}(word) \approx \log \left( \dfrac{(n+1)_A / (total+1)_A}{(n+1)_B / (total+1)_B}\right)\]
Word usage: equally likely
-Here, we calculate log-odds ratios for Biden versus Warren and display them in ascending order of absolute value of log-odds ratio
## # A tibble: 691 x 2 ## word logratio ## <chr> <dbl> ## 1 easy 0.00136 ## 2 immediately 0.00136 ## 3 major 0.00136 ## 4 program 0.00136 ## 5 table 0.00136 ## 6 tune 0.00136 ## 7 rural -0.00520 ## 8 actions -0.0111 ## 9 agree -0.0111 ## 10 attacks -0.0111 ## # … with 681 more rows
- Words about equally likely to be tweeted from the two candidates are non-buzzwords
Word usage: most distinctive
- The words that are most likely to be from Biden and not Warren will have the largest, most positive ratios
- The words that are most likely to be from Warren and not Biden will have the smallest, most negative ratios
- The plot on the next slides shows the 20 most positive and negative ratios
Word usage: most distinctive cont.
- The most distinctive words are much more personal/characteristic of the candidate - Biden talks a lot about himself, whereas Warren talks more about her platform
Beyond one-word tokens
n-grams
- We can tokenize text into consecutive sequences of \(n\) words called n-grams
- One-word token is a `unigram', two consecutive word tokens are 'bigrams', etc.
tweet_bigrams <- tweets %>% select(screen_name, text) %>% mutate(text = str_remove_all(text, remove_reg))%>% unnest_tokens(bigram, text, token = "ngrams", n = 2) tweet_bigrams
## screen_name bigram
## 1 BernieSanders happening soon
## 2 BernieSanders soon join
## 3 BernieSanders join our
## 4 BernieSanders our campaign
## 5 BernieSanders campaign manager
## 6 BernieSanders manager fshakir
## 7 BernieSanders fshakir at
## 8 BernieSanders at 9pm
## 9 BernieSanders 9pm et
## 10 BernieSanders et 6pm
## 11 BernieSanders 6pm pt
## 12 BernieSanders pt for
## 13 BernieSanders for a
## 14 BernieSanders a special
## 15 BernieSanders special strategy
## 16 BernieSanders strategy call
## 17 BernieSanders call with
## 18 BernieSanders with supporters
## 19 BernieSanders supporters you
## 20 BernieSanders you can
## 21 BernieSanders can join
## 22 BernieSanders join by
## 23 BernieSanders by dialing
## 24 BernieSanders dialing 855
## 25 BernieSanders 855 286
## 26 BernieSanders 286 0288
## 27 BernieSanders 0288 i’m
## 28 BernieSanders i’m not
## 29 BernieSanders not going
## 30 BernieSanders going anywhere
## 31 BernieSanders anywhere but
## 32 BernieSanders but billionaires
## 33 BernieSanders billionaires can
## 34 BernieSanders can be
## 35 BernieSanders be damn
## 36 BernieSanders damn sure
## 37 BernieSanders sure they’ll
## 38 BernieSanders they’ll pay
## 39 BernieSanders pay their
## 40 BernieSanders their fair
## 41 BernieSanders fair share
## 42 BernieSanders share when
## 43 BernieSanders when i
## 44 BernieSanders i am
## 45 BernieSanders am president
## 46 BernieSanders president https
## 47 BernieSanders https t.co
## 48 BernieSanders t.co ov4bj9gm3t
## 49 BernieSanders ov4bj9gm3t this
## 50 BernieSanders this is
## 51 BernieSanders is an
## 52 BernieSanders an enormous
## 53 BernieSanders enormous victory
## 54 BernieSanders victory for
## 55 BernieSanders for workers
## 56 BernieSanders workers over
## 57 BernieSanders over 40,000
## 58 BernieSanders 40,000 child
## 59 BernieSanders child care
## 60 BernieSanders care providers
## 61 BernieSanders providers now
## 62 BernieSanders now have
## 63 BernieSanders have the
## 64 BernieSanders the right
## 65 BernieSanders right to
## 66 BernieSanders to negotiate
## 67 BernieSanders negotiate better
## 68 BernieSanders better pay
## 69 BernieSanders pay and
## 70 BernieSanders and benefits
## 71 BernieSanders benefits training
## 72 BernieSanders training and
## 73 BernieSanders and high
## 74 BernieSanders high quality
## 75 BernieSanders quality care
## 76 BernieSanders care for
## 77 BernieSanders for children
## 78 BernieSanders children congratulations
## 79 BernieSanders congratulations to
## 80 BernieSanders to all
## 81 BernieSanders all who
## 82 BernieSanders who organized
## 83 BernieSanders organized to
## 84 BernieSanders to make
## 85 BernieSanders make this
## 86 BernieSanders this possible
## 87 BernieSanders possible https
## 88 BernieSanders https t.co
## 89 BernieSanders t.co obizv6h2pq
## 90 BernieSanders obizv6h2pq this
## 91 BernieSanders this is
## 92 BernieSanders is what
## 93 BernieSanders what i
## 94 BernieSanders i mean
## 95 BernieSanders mean when
## 96 BernieSanders when i
## 97 BernieSanders i say
## 98 BernieSanders say trump
## 99 BernieSanders trump is
## 100 BernieSanders is a
## 101 BernieSanders a racist
## 102 BernieSanders racist and
## 103 BernieSanders and a
## 104 BernieSanders a demagogue
## 105 BernieSanders demagogue under
## 106 BernieSanders under my
## 107 BernieSanders my administration
## 108 BernieSanders administration no
## 109 BernieSanders no government
## 110 BernieSanders government agent
## 111 BernieSanders agent will
## 112 BernieSanders will ever
## 113 BernieSanders ever tear
## 114 BernieSanders tear a
## 115 BernieSanders a baby
## 116 BernieSanders baby away
## 117 BernieSanders away from
## 118 BernieSanders from the
## 119 BernieSanders the arms
## 120 BernieSanders arms of
## 121 BernieSanders of a
## 122 BernieSanders a mother
## 123 BernieSanders mother we
## 124 BernieSanders we are
## 125 BernieSanders are going
## 126 BernieSanders going to
## 127 BernieSanders to end
## 128 BernieSanders end the
## 129 BernieSanders the demonization
## 130 BernieSanders demonization of
## 131 BernieSanders of immigrants
## 132 BernieSanders immigrants and
## 133 BernieSanders and create
## 134 BernieSanders create a
## 135 BernieSanders a just
## 136 BernieSanders just and
## 137 BernieSanders and fair
## 138 BernieSanders fair immigration
## 139 BernieSanders immigration system
## 140 BernieSanders system https
## 141 BernieSanders https t.co
## 142 BernieSanders t.co fhvu2mltht
## 143 BernieSanders fhvu2mltht yesterday
## 144 BernieSanders yesterday i
## 145 BernieSanders i stood
## 146 BernieSanders stood in
## 147 BernieSanders in solidarity
## 148 BernieSanders solidarity with
## 149 BernieSanders with seiu1984
## 150 BernieSanders seiu1984 seiu
## 151 BernieSanders seiu and
## 152 BernieSanders and workers
## 153 BernieSanders workers from
## 154 BernieSanders from rockingham
## 155 BernieSanders rockingham county
## 156 BernieSanders county nursing
## 157 BernieSanders nursing home
## 158 BernieSanders home who
## 159 BernieSanders who are
## 160 BernieSanders are fighting
## 161 BernieSanders fighting for
## 162 BernieSanders for their
## 163 BernieSanders their right
## 164 BernieSanders right to
## 165 BernieSanders to form
## 166 BernieSanders form a
## 167 BernieSanders a union
## 168 BernieSanders union they
## 169 BernieSanders they deserve
## 170 BernieSanders deserve a
## 171 BernieSanders a voice
## 172 BernieSanders voice for
## 173 BernieSanders for themselves
## 174 BernieSanders themselves and
## 175 BernieSanders and the
## 176 BernieSanders the people
## 177 BernieSanders people they
## 178 BernieSanders they serve
## 179 BernieSanders serve https
## 180 BernieSanders https t.co
## 181 BernieSanders t.co pjfzrrfxxt
## 182 BernieSanders pjfzrrfxxt it's
## 183 BernieSanders it's simple
## 184 BernieSanders simple people
## 185 BernieSanders people trust
## 186 BernieSanders trust berniesanders
## 187 BernieSanders berniesanders to
## 188 BernieSanders to fight
## 189 BernieSanders fight for
## 190 BernieSanders for them
## 191 BernieSanders them because
## 192 BernieSanders because he
## 193 BernieSanders he always
## 194 BernieSanders always has
## 195 BernieSanders has together
## 196 BernieSanders together we're
## 197 BernieSanders we're working
## 198 BernieSanders working to
## 199 BernieSanders to rebuild
## 200 BernieSanders rebuild the
## 201 BernieSanders the middle
## 202 BernieSanders middle class
## 203 BernieSanders class and
## 204 BernieSanders and create
## 205 BernieSanders create an
## 206 BernieSanders an economy
## 207 BernieSanders economy and
## 208 BernieSanders and a
## 209 BernieSanders a government
## 210 BernieSanders government that
## 211 BernieSanders that works
## 212 BernieSanders works for
## 213 BernieSanders for all
## 214 BernieSanders all people
## 215 BernieSanders people join
## 216 BernieSanders join the
## 217 BernieSanders the fight
## 218 BernieSanders fight https
## 219 BernieSanders https t.co
## 220 BernieSanders t.co m8pqdzd8g4
## 221 BernieSanders m8pqdzd8g4 https
## 222 BernieSanders https t.co
## 223 BernieSanders t.co mbs78uhunl
## 224 BernieSanders mbs78uhunl two
## 225 BernieSanders two years
## 226 BernieSanders years ago
## 227 BernieSanders ago the
## 228 BernieSanders the deadliest
## 229 BernieSanders deadliest mass
## 230 BernieSanders mass shooting
## 231 BernieSanders shooting in
## 232 BernieSanders in our
## 233 BernieSanders our modern
## 234 BernieSanders modern history
## 235 BernieSanders history ended
## 236 BernieSanders ended 58
## 237 BernieSanders 58 lives
## 238 BernieSanders lives in
## 239 BernieSanders in las
## 240 BernieSanders las vegas
## 241 BernieSanders vegas since
## 242 BernieSanders since then
## 243 BernieSanders then thousands
## 244 BernieSanders thousands more
## 245 BernieSanders more lives
## 246 BernieSanders lives have
## 247 BernieSanders have been
## 248 BernieSanders been lost
## 249 BernieSanders lost to
## 250 BernieSanders to gun
## 251 BernieSanders gun violence
## 252 BernieSanders violence our
## 253 BernieSanders our job
## 254 BernieSanders job stand
## 255 BernieSanders stand up
## 256 BernieSanders up to
## 257 BernieSanders to the
## 258 BernieSanders the nra
## 259 BernieSanders nra pass
## 260 BernieSanders pass real
## 261 BernieSanders real gun
## 262 BernieSanders gun safety
## 263 BernieSanders safety legislation
## 264 BernieSanders legislation and
## 265 BernieSanders and ban
## 266 BernieSanders ban the
## 267 BernieSanders the sale
## 268 BernieSanders sale and
## 269 BernieSanders and distribution
## 270 BernieSanders distribution of
## 271 BernieSanders of assault
## 272 BernieSanders assault weapons
## 273 BernieSanders weapons people
## 274 BernieSanders people don’t
## 275 BernieSanders don’t want
## 276 BernieSanders want to
## 277 BernieSanders to keep
## 278 BernieSanders keep their
## 279 BernieSanders their insurance
## 280 BernieSanders insurance plans
## 281 BernieSanders plans they
## 282 BernieSanders they want
## 283 BernieSanders want to
## 284 BernieSanders to keep
## 285 BernieSanders keep their
## 286 BernieSanders their doctors
## 287 BernieSanders doctors for
## 288 BernieSanders for profit
## 289 BernieSanders profit insurance
## 290 BernieSanders insurance plans
## 291 BernieSanders plans are
## 292 BernieSanders are unaffordable
## 293 BernieSanders unaffordable and
## 294 BernieSanders and unsustainable
## 295 BernieSanders unsustainable we
## 296 BernieSanders we need
## 297 BernieSanders need a
## 298 BernieSanders a system
## 299 BernieSanders system that
## 300 BernieSanders that works
## 301 BernieSanders works for
## 302 BernieSanders for everyone
## 303 BernieSanders everyone we
## 304 BernieSanders we need
## 305 BernieSanders need medicareforall
## 306 BernieSanders medicareforall https
## 307 BernieSanders https t.co
## 308 BernieSanders t.co uripaaleaw
## 309 BernieSanders uripaaleaw the
## 310 BernieSanders the billionaire
## 311 BernieSanders billionaire owners
## 312 BernieSanders owners of
## 313 BernieSanders of forever
## 314 BernieSanders forever 21
## 315 BernieSanders 21 won't
## 316 BernieSanders won't suffer
## 317 BernieSanders suffer as
## 318 BernieSanders as a
## 319 BernieSanders a result
## 320 BernieSanders result of
## 321 BernieSanders of filing
## 322 BernieSanders filing for
## 323 BernieSanders for bankruptcy
## 324 BernieSanders bankruptcy the
## 325 BernieSanders the thousands
## 326 BernieSanders thousands of
## 327 BernieSanders of workers
## 328 BernieSanders workers losing
## 329 BernieSanders losing their
## 330 BernieSanders their jobs
## 331 BernieSanders jobs will
## 332 BernieSanders will we
## 333 BernieSanders we must
## 334 BernieSanders must stand
## 335 BernieSanders stand workers
## 336 BernieSanders workers and
## 337 BernieSanders and against
## 338 BernieSanders against the
## 339 BernieSanders the greed
## 340 BernieSanders greed of
## 341 BernieSanders of wall
## 342 BernieSanders wall street
## 343 BernieSanders street and
## 344 BernieSanders and the
## 345 BernieSanders the billionaire
## 346 BernieSanders billionaire class
## 347 BernieSanders class all
## 348 BernieSanders all forever
## 349 BernieSanders forever 21
## 350 BernieSanders 21 workers
## 351 BernieSanders workers must
## 352 BernieSanders must be
## 353 BernieSanders be paid
## 354 BernieSanders paid their
## 355 BernieSanders their fair
## 356 BernieSanders fair share
## 357 BernieSanders share https
## 358 BernieSanders https t.co
## 359 BernieSanders t.co tej53ojm0f
## 360 BernieSanders tej53ojm0f this
## 361 BernieSanders this stuff
## 362 BernieSanders stuff is
## 363 BernieSanders is all
## 364 BernieSanders all true
## 365 BernieSanders true https
## 366 BernieSanders https t.co
## 367 BernieSanders t.co 1pv6ibdydt
## 368 BernieSanders 1pv6ibdydt an
## 369 BernieSanders an ad
## 370 BernieSanders ad about
## 371 BernieSanders about bernie’s
## 372 BernieSanders bernie’s history
## 373 BernieSanders history leading
## 374 BernieSanders leading movements
## 375 BernieSanders movements to
## 376 BernieSanders to lift
## 377 BernieSanders lift up
## 378 BernieSanders up working
## 379 BernieSanders working people
## 380 BernieSanders people fittingly
## 381 BernieSanders fittingly funded
## 382 BernieSanders funded exclusively
## 383 BernieSanders exclusively from
## 384 BernieSanders from grassroots
## 385 BernieSanders grassroots donations
## 386 BernieSanders donations made
## 387 BernieSanders made by
## 388 BernieSanders by working
## 389 BernieSanders working people
## 390 BernieSanders people who
## 391 BernieSanders who stand
## 392 BernieSanders stand with
## 393 BernieSanders with him
## 394 BernieSanders him in
## 395 BernieSanders in this
## 396 BernieSanders this campaign
## 397 BernieSanders campaign https
## 398 BernieSanders https t.co
## 399 BernieSanders t.co uxntlpyycs
## 400 BernieSanders uxntlpyycs we
## 401 BernieSanders we know
## 402 BernieSanders know berniesanders
## 403 BernieSanders berniesanders will
## 404 BernieSanders will fight
## 405 BernieSanders fight for
## 406 BernieSanders for the
## 407 BernieSanders the working
## 408 BernieSanders working class
## 409 BernieSanders class as
## 410 BernieSanders as president
## 411 BernieSanders president because
## 412 BernieSanders because he
## 413 BernieSanders he always
## 414 BernieSanders always has
## 415 BernieSanders has https
## 416 BernieSanders https t.co
## 417 BernieSanders t.co wids2qefc1
## 418 BernieSanders wids2qefc1 new
## 419 BernieSanders new ad
## 420 BernieSanders ad https
## 421 BernieSanders https t.co
## 422 BernieSanders t.co xarzvbjz7c
## 423 BernieSanders xarzvbjz7c let's
## 424 BernieSanders let's be
## 425 BernieSanders be clear
## 426 BernieSanders clear berniesanders
## 427 BernieSanders berniesanders is
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## 49421 ewarren of corporations
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## 49425 ewarren interests shouldn’t
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## 49508 ewarren in the
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## 49849 ewarren shc9tc75i7 massachusetts
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## [ reached getOption("max.print") -- omitted 113288 rows ]
Counts of bigrams
tweet_bigrams %>% count(bigram, sort = T)
## # A tibble: 72,270 x 2 ## bigram n ## <chr> <int> ## 1 https t.co 3555 ## 2 of the 480 ## 3 in the 459 ## 4 we need 372 ## 5 to the 329 ## 6 health care 319 ## 7 for the 309 ## 8 on the 299 ## 9 for all 278 ## 10 we are 278 ## # … with 72,260 more rows
Remove custom stop_words
my_stop_words <- tibble(word = c("http", "https"), lexicon = c("custom"))
custom_stop_words <- bind_rows(my_stop_words, stop_words)
bigrams_sep <- tweet_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_sep %>%
filter(!word1 %in% custom_stop_words$word,
!word1 %in% str_remove_all(custom_stop_words$word, "'"),
str_detect(word1, "[a-z]"),
!word2 %in% custom_stop_words$word,
!word2 %in% str_remove_all(custom_stop_words$word, "'"),
str_detect(word2, "[a-z]"))
## # A tibble: 21,107 x 3 ## word1 word2 n ## <chr> <chr> <int> ## 1 health care 319 ## 2 donald trump 170 ## 3 gun violence 147 ## 4 climate change 144 ## 5 middle class 104 ## 6 it’s time 97 ## 7 white house 94 ## 8 climate crisis 75 ## 9 structural change 75 ## 10 fossil fuel 73 ## # … with 21,097 more rows
Words within a single tweet
- We may be interested in which words tend to appear in a single tweet (not necessarily consecutive pairs)
- For each candidate, we create a new variable called
rankto indicate the order in which the tweets were published - Then we count the number of word pairs that occur within a single tweet
Word pairs
word_pairs <- tweet_section_words %>% pairwise_count(word, rank, sort= T ) word_pairs
## # A tibble: 2,390,560 x 3 ## item1 item2 n ## <chr> <chr> <dbl> ## 1 care health 267 ## 2 health care 267 ## 3 trump president 219 ## 4 president trump 219 ## 5 people president 211 ## 6 president people 211 ## 7 people country 180 ## 8 country people 180 ## 9 people trump 172 ## 10 trump people 172 ## # … with 2,390,550 more rows
Pairwise correlations
- We see that "health" and "care" are the two most common co-occurring words within a single tweet, and "country" and "people" also commonly co-occur.
- However, you may recall that those words are also among the most common individual unigrams
- Therefore, it may be more meaningful to measure how often pairs of words are used together relative to how often they are used individually
- This is the correlation among words
Phi coefficient
- We will use the phi coefficient, which focuses on how much more likely it is that either both/neither of two words appear, versus only one of the two appearing
- A correlation close to 0 means that the two words are not likely to co-occur, and a correlation close to \(\pm 1\) means that the two are both likely to simultanegously occur or not occur
- We will focus on the most common individual words
word_cors <- tweet_section_words %>% group_by(word) %>% filter(n() >= 75) %>% pairwise_cor(word, rank, sort = T)
Most positively correlated pairs
- The most positively correlated pairs of words are common two-word terms: health care, student debt, etc.
word_cors %>% slice(1:10)
## # A tibble: 10 x 3 ## item1 item2 correlation ## <chr> <chr> <dbl> ## 1 fossil fuel 0.911 ## 2 fuel fossil 0.911 ## 3 care health 0.766 ## 4 health care 0.766 ## 5 class middle 0.715 ## 6 middle class 0.715 ## 7 debt student 0.649 ## 8 student debt 0.649 ## 9 violence gun 0.619 ## 10 gun violence 0.619
Pairwise correlation cont.
- When we looked at pairwise counts, "people" and "country" occurred frequently in a single tweet. But how correlated are they?
word_cors %>% filter(item1 == "people" & item2 == "country")
## # A tibble: 1 x 3 ## item1 item2 correlation ## <chr> <chr> <dbl> ## 1 people country 0.0817
- So even though "people" and "country" co-occur very often, they are not very associated.
- That is, these two words often occur together, but many tweets use one word and not the other
Graph of highly correlated words
- Lastly, we can visualize the correlations and clusters of high, positively correlated word pairs
word_cors %>% filter(correlation > .15) %>% graph_from_data_frame() %>% ggraph(layout = "fr") + geom_edge_link(aes(edge_alpha = correlation), show.legend = FALSE) + geom_node_point(color = "lightblue", size = 5) + geom_node_text(aes(label = name), repel = TRUE) + theme_void()
Classification
Classification - Bag of Words
- We have been generally treating text as a bag of words. The order does not really matter (maybe with n-grams there is some dependence, but not much).
- In this case, a corpus can be represented as a document-term matrix. Each row is a document, each column is a word, and the \(ij\)-th entry is the number of times word \(j\) appears in document \(i\).
- These matrices are extremely large but also extremely sparse. Most entries are 0. Special tools have been made to represent them in a memory-saving manner.
Document-Term Example
text
## [1] "We typically think of text as a (very) long string, but this format is quite hard to work with." ## [2] "Specifically, we will work in the 'tidy text format': a table with one token per row"
dfm(text) %>% convert(to = "matrix") %>% {.[,1:10]}
## features ## docs we typically think of text as a ( very ) ## text1 1 1 1 1 1 1 1 1 1 1 ## text2 1 0 0 0 1 0 1 0 0 0
Example Speeches
- The data we will use for this section are the State of the Union speeches from 1970 onward.
sou_data %>% slice(1:3)
## # A tibble: 3 x 5 ## file text year pres Party ## <chr> <chr> <chr> <chr> <chr> ## 1 1970nixon… Mr. Speaker, Mr. President, my colleagues… 1970 nixon Republ… ## 2 1970nixon… To address a joint session of the Congres… 1970 nixon Republ… ## 3 1970nixon… The State of the Union Address is traditi… 1970 nixon Republ…
DFM objects
sou_data_dfm <-
dfm(sou_data$text,
remove = c(stop_words$word,"laughter","applause"),
remove_punct = T)
sou_data_dfm
## Document-feature matrix of: 4,846 documents, 12,107 features (99.8% sparse).
object.size(sou_data_dfm) %>% format(units = "Mb")
## [1] "2.7 Mb"
convert(sou_data_dfm,to = "matrix") %>% object.size() %>% format(units = "Mb")
## [1] "448.7 Mb"
sou_data_dfm@docvars <- sou_data %>% select(-text) %>% as.data.frame()
Logistic regression
- The classification task we will focus on is recovering political party from the paragraphs of a speech.
- We have 4846 documents but 12107 unique words! Logistic regression can only work with fewer features than observations (\(p < n\)).
- Need some way to reduce the dimensions
Logistic regression - sentiment analysis
One way we discussed before is mapping each word to a "sentiment". We can reduce the columns of the dfm to counts of the sentiment words of each type. For each row (paragraph), sum all the counts of words for each sentiment. The output is a count matrix with the \(ij\) entry showing the number of words in row \(i\) with sentiment \(j\).
## # A tibble: 5 x 5 ## trust fear negative sadness anger ## <int> <int> <int> <int> <int> ## 1 3 0 0 0 0 ## 2 4 0 1 0 0 ## 3 3 0 1 1 0 ## 4 0 0 0 0 0 ## 5 0 0 1 0 1
Logistic regression - sentiment analysis
logit_sentiment <- glm(party == "Democrat" ~ . -n,
data = sou_sentiment_dfm_std,
family = binomial(link = "logit"))
#accuracy rate
sou_sentiment_dfm_std$pred_party <-
if_else(predict(logit_sentiment,type = "response")>.5,
"Democrat","Republican")
mean(sou_sentiment_dfm_std$party ==
sou_sentiment_dfm_std$pred_party)
## [1] 0.6295914
Logistic regression - sentiment analysis
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | -0.606 | 0.057 | -10.639 | 0.000 |
| trust | 0.008 | 0.021 | 0.391 | 0.695 |
| fear | -0.081 | 0.029 | -2.741 | 0.006 |
| negative | 0.025 | 0.028 | 0.911 | 0.362 |
| sadness | 0.059 | 0.037 | 1.594 | 0.111 |
| anger | 0.022 | 0.038 | 0.587 | 0.557 |
| surprise | 0.036 | 0.044 | 0.823 | 0.410 |
| positive | 0.028 | 0.017 | 1.658 | 0.097 |
| disgust | -0.099 | 0.042 | -2.345 | 0.019 |
| joy | -0.098 | 0.031 | -3.135 | 0.002 |
| anticipation | 0.082 | 0.027 | 3.071 | 0.002 |
| no_sentiment_words | -0.730 | 0.161 | -4.545 | 0.000 |
What if we do it by speeches instead?
sou_sentiment_dfm_speech <- sou_sentiment_dfm %>%
as_tibble %>%
mutate(year = sou_data$year,
party = sou_data$Party,
pres = sou_data$pres) %>%
group_by(year,party,pres) %>%
summarise_all(sum)
logit_sentiment_by_speech <- glm(party == "Democrat" ~ . - year - pres,
data = sou_sentiment_dfm_speech,
family = binomial(link = "logit"))
How well does the classifier do by President?
sou_sentiment_dfm_speech$pred_party <- ifelse(predict(logit_sentiment_by_speech) > 0,"Democrat","Republican") sou_sentiment_dfm_speech %>% group_by(pres,party) %>% summarise(n_speech = n(),pred_dem_prop = mean(pred_party == "Democrat")) %>% arrange(party,pred_dem_prop)
## # A tibble: 9 x 4 ## # Groups: pres [9] ## pres party n_speech pred_dem_prop ## <chr> <chr> <int> <dbl> ## 1 carter Democrat 3 0 ## 2 clinton Democrat 8 0.5 ## 3 obama Democrat 8 0.75 ## 4 ford Republican 3 0 ## 5 nixon Republican 5 0 ## 6 trump Republican 3 0 ## 7 reagan Republican 8 0.125 ## 8 wbush Republican 8 0.125 ## 9 hbush Republican 4 0.5
Generative Models
- Sentiment is useful for some ad-hoc methods or high-level summaries, but it lacks interpretability as a generative model.
- Some simple bag-of-words generative models can be used for classification.
Generative Models
- Let each document be represented by a vector of word counts \(W_d\).
- Political parties differ by the probabilities that they choose each word. \(\theta_d\) and \(\theta_r\) give the probabilities that each word is chosen by Democrats and Republicans respectively.
- Then the observed word counts for a president of party \(p\) conditional on \(\theta_p\) is thus a multinomial likelihood: \(W_d | \theta_p \sim \text{Multinomial}(n_d,\theta_p)\).
Multinomial Distribution
- What is a multinomial distribution? Just like a Binomial, there are \(n\) independent trials, but each trial can have \(k\) unique, discrete outcomes. Say you ask \(n=10\) people what their favorite color is and tabulate the responses. The responses plausibly have a multinomial distribution.
- So if \(\boldsymbol{w} | \boldsymbol{\theta} \sim \text{Multinomial}(n,\boldsymbol{\theta})\), then the pmf is \(p(\boldsymbol{w}) = \dfrac{n!}{w_1! \cdots w_k!}\theta_1^{w_1}\cdots \theta_k^{w_k}\), where \(\sum_i^k w_i = n\) and \(\sum_i^k \theta_i = 1\).
Generative Models: Conjugate priors
- Of course, \(\theta_r\) and \(\theta_d\) are unknown and we would like to estimate them from the data. Sounds like posterior inference!
- Luckily there is a nice conjugate prior for multinomial data!
- Dirichlet distribution: \(\theta_p \sim \text{Dirichlet}(\alpha)\).
Dirichlet Distribution
- The Dirichlet distribution is a multivariate Beta distribution, and is conjugate with the multinomial likelihood, similar to the Beta and Binomial conjugacy.
- The Dirichlet is just like a Beta distribution, but with \(k\) parameters rather than two. \(\boldsymbol{\theta} = (\theta_1, \ldots,\theta_k) \sim \text{Dir}(\mathbf{z})\). The pdf is proportional to \(p(\boldsymbol{\theta}) \propto \prod_{i=1}^k \theta_i^{z_i-1}\). This implies that \(\sum_i^k \theta_i = 1\) and \(\theta_i \in (0,1)\).
- \(E[\theta_i] = \frac{z_i}{\sum_i^k z_i}\). As the sum \(\sum_i^k z_i\) increases (often called the "concentration" of the Dirichlet), the distribution gets more and more concentrated around the means. In priors, the parameters are often all set to the same value and we write \(\theta \sim \text{Dir}(\alpha)\) to imply that \(E[\theta_i] = 1/k\) with concentration \(k\alpha\).
Generative Models - Estimation
Let \(W_d\) be a k-dimensional count vector with conditional distribution \(W_d|\theta_p \sim \text{Multi}(n,\theta_p)\) where \(n = \sum_{i=1}^k W_{di}\). Let the prior on \(\theta_p\) be Dirichlet(\(\alpha\)): \(p(\theta_p) \propto \prod_{i=1}^k \theta_{pi}^{\alpha-1}\).
\[ \begin{align} p(\theta_p|W_d) &\propto p(W_d|\theta_p)p(\theta_p) \\ &\propto \left(\prod_{i=1}^k \theta_{pi}^{W_{di}} \right) \left(\prod_{i=1}^k \theta_{pi}^{\alpha - 1} \right) \\ &\propto \prod_{i=1}^k \theta_{pi}^{W_{di} + \alpha - 1} \\ &\propto \text{Dir}(W_d + \alpha) \\ \end{align} \]
Generative Models - Classification
Thus, given party-labeled speeches, we can quickly update a prior distribution on the party-specific word probabilities. How can we use this for classification?
Given a piece of text with unknown authorship, we can compute the posterior-predictive probability that it was generated by either a Democrat or a Republican.
Generative Models - Classification
What we are interested in is \(P(\text{Dem}|W_{new},W_d,W_r)\), where \(W_{new}\) is the new document, and \(W_d\) and \(W_r\) are word counts from documents authored by Democrats and Republicans.
From Bayes rule, we know \(P(\text{Dem}|W_{new},W_d,W_r) \propto P(W_{new}|\text{Dem},W_d,W_r)P(\text{Dem})\). The prior \(P(\text{Dem})\) is easy, we choose it ourselves. But how do we compute the other term?
Generative Models - Classification
Let \(W_d\) be the word counts for Democratic speeches and \(W_r\) be the word counts for Republican speeches. \(W_{new}\) is the word count vector of the new document. \(n_{new}\), \(n_d\), and \(n_r\) are the number of new, Democratic, and Republican words.
\[ \begin{align} p_{D} &= P(\text{Dem}|W_{new},W_d,W_r) \\ &\propto P(W_{new}|\text{Dem},W_d,W_r) \\ &= \int P(W_{new}|\text{Dem},\theta_d)p(\theta_d|W_d) \ d\theta_d \\ &= \frac{n_{new}! \ \Gamma(n_d + k\alpha)}{\Gamma(n_{new} + n_d + k\alpha)}\prod_{i=1}^k\frac{W_{new,i} + W_{d,i} + \alpha}{W_{new,i}! \ \Gamma(W_{d,i} + \alpha)}\\ \end{align} \]
\(P(W_{new}|\text{Dem},\theta_d)\) is a multinomial likelihood, and the posterior is Dirichlet, so this integral is analytically computable. It is actually a known distribution, the Dirichlet-Multinomial.
Generative Models - Classification
A Dirichlet-Multinomial distribution is the result of \(n\) independent, discrete-outcomed trials (same as in the multinomial) where the success probability is drawn from a Dirichlet distribution with specified parameters. This is the same form as the posterior-predictive for our example. Thus,
\[p_D \propto \text{Dir-Multi}(W_{new}|W_d + \alpha)\] \[p_R \propto \text{Dir-Multi}(W_{new}|W_r + \alpha)\] \[p_D = \frac{\text{Dir-Multi}(W_{new}|W_d + \alpha)}{\text{Dir-Multi}(W_{new}|W_d + \alpha) + \text{Dir-Multi}(W_{new}|W_r + \alpha)}\]
Generative Models - Classification Code
lddirmult <- function(x,alpha){
n <- sum(x)
lc <- lgamma(n+1) + lgamma(sum(alpha)) - lgamma(n + sum(alpha))
lkernel <- sum(lgamma(x + alpha) - lgamma(x + 1) - lgamma(alpha))
lc + lkernel
}
loo_classify <- function(docs,alpha,truth){
lapply(1:nrow(docs),function(d){
w <- docs[d,]
#remove from current counts
full_counts_new <- docs %>% dfm_subset(1:nrow(docs) != d) %>% dfm_group(truth[-d])
#compute log probabilities
lprobs <- sapply(unique(truth),
function(t) lddirmult(convert(w,"matrix"),
convert(full_counts_new[t,],"matrix") + alpha))
#return to raw scale
probs <- lprobs %>% {. - max(.)} %>% exp %>% {./sum(.)}
return(c(round(probs,3),unique(truth)[which.max(lprobs)]))
})
}
sou_dfm_by_speech <-
sou_data_dfm %>%
dfm_group("year")
loo_results <- loo_classify(sou_dfm_by_speech,
alpha = .01,
truth = sou_dfm_by_speech@docvars$Party) %>%
do.call(what = rbind)
table(truth = sou_dfm_by_speech@docvars$Party,
guess = loo_results[,ncol(loo_results)])
## guess ## truth Democrat Republican ## Democrat 16 3 ## Republican 0 31
Genertive Models - Other Applications
- These methods are useful anytime you have very high dimensional discrete data and have many labeled observations:
- Identifying species by genetic markers
- Authorship identification
- Measures of separation
Topic Models
Topic Models - Motivation
The previous method is useful for labeled data and classification. But the classification does not tell you what the text is about and not all data have meaningful labels. Topic models generally do unsupervised clustering of documents.
Topic Models - Motivation
The results of LDA applied to the State of the Union speeches are shown below. We identified 5 themes/topics. The 10 most common words in each topic are shown below.
topicmodels::terms(lda_speech,10)
## Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 ## [1,] "children" "country" "federal" "jobs" "world" ## [2,] "century" "world" "programs" "country" "peace" ## [3,] "support" "nation" "program" "economy" "freedom" ## [4,] "world" "united" "local" "health" "future" ## [5,] "security" "iraq" "billion" "tax" "budget" ## [6,] "care" "terrorists" "system" "care" "nation" ## [7,] "budget" "security" "percent" "world" "free" ## [8,] "21st" "health" "energy" "energy" "security" ## [9,] "challenge" "citizens" "economic" "families" "economic" ## [10,] "social" "economy" "act" "plan" "growth"
Topic Models - Motivation
The proportion of times each president spoke about each toipc is shown below
## # A tibble: 9 x 6 ## pres `Topic 1` `Topic 2` `Topic 3` `Topic 4` `Topic 5` ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 nixon 0 0 0.82 0 0.18 ## 2 ford 0 0 0.74 0.02 0.24 ## 3 carter 0 0 0.290 0.01 0.7 ## 4 reagan 0 0 0.2 0.04 0.76 ## 5 hbush 0 0 0.06 0.09 0.86 ## 6 clinton 0.78 0 0.02 0.2 0.01 ## 7 wbush 0.05 0.68 0.02 0.04 0.22 ## 8 obama 0 0.05 0 0.94 0 ## 9 trump 0.27 0.72 0 0.01 0
Topic Models - LDA
LDA, the Latent Dirichlet Allocation, is the most common topic model. In LDA, each document is modeled as a mixture of \(K\) substantive topics. \(\theta_d\) gives the proportion of words in document \(d\) about each topic, i.e. if \(\theta_{d} = <.25,.50,.25>\), then one quarter of the words are from topic 1, one half from topic 2, and one quarter from topic 3.
A topic is a probability distribution over words. Given that a word is from topic \(k\), the probability of drawing a specific word \(w\) is given by \(\beta_{kw}\). Each topic gives a different probability for drawing that same word \(w\).
Independent Dir(\(\phi\)) priors are placed on each \(\beta_k\) and Dir(\(\alpha\)) priors on \(\theta_d\).
Topic Models - LDA
Most topic modeling packages in R have built in estimation methods that are very fast.
Results are often characterized not by inference on a specific parameter, but by visualizing topics and comparing documents by what toipcs they discus.
Sometimes the document-topic proportions are passed to a second algorithm as a dimension reduction tool to learn something about the documents (similar to how we used sentiment scores for logistic regression).