Chapter 4: Classification using Naive Bayes
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Example: Filtering spam SMS messages
Step 2: Exploring and preparing the data —-
# read the sms data into the sms data frame
sms_raw <- read.csv("sms_spam.csv", stringsAsFactors = FALSE)
# examine the structure of the sms data
str(sms_raw)'data.frame': 5559 obs. of 2 variables:
$ type: chr "ham" "ham" "ham" "spam" ...
$ text: chr "Hope you are having a good week. Just checking in" "K..give back my thanks." "Am also doing in cbe only. But have to pay." "complimentary 4 STAR Ibiza Holiday or £10,000 cash needs your URGENT collection. 09066364349 NOW from Landline not to lose out!"| __truncated__ ...# convert spam/ham to factor.
sms_raw$type <- factor(sms_raw$type)
# examine the type variable more carefully
str(sms_raw$type) Factor w/ 2 levels "ham","spam": 1 1 1 2 2 1 1 1 2 1 ...table(sms_raw$type)
ham spam
4812 747 # build a corpus using the text mining (tm) package
library(tm)Loading required package: NLPsms_corpus <- VCorpus(VectorSource(sms_raw$text))
# examine the sms corpus
print(sms_corpus)<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 5559inspect(sms_corpus[1:2])<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 2
[[1]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 49
[[2]]
<<PlainTextDocument>>
Metadata: 7
Content: chars: 23as.character(sms_corpus[[1]])[1] "Hope you are having a good week. Just checking in"lapply(sms_corpus[1:2], as.character)$`1`
[1] "Hope you are having a good week. Just checking in"
$`2`
[1] "K..give back my thanks."# clean up the corpus using tm_map()
sms_corpus_clean <- tm_map(sms_corpus, content_transformer(tolower))
# show the difference between sms_corpus and corpus_clean
as.character(sms_corpus[[1]])[1] "Hope you are having a good week. Just checking in"as.character(sms_corpus_clean[[1]])[1] "hope you are having a good week. just checking in"sms_corpus_clean <- tm_map(sms_corpus_clean, removeNumbers) # remove numbers
sms_corpus_clean <- tm_map(sms_corpus_clean, removeWords, stopwords()) # remove stop words
sms_corpus_clean <- tm_map(sms_corpus_clean, removePunctuation) # remove punctuation# tip: create a custom function to replace (rather than remove) punctuation
removePunctuation("hello...world")[1] "helloworld"replacePunctuation <- function(x) { gsub("[[:punct:]]+", " ", x) }
replacePunctuation("hello...world")[1] "hello world"# illustration of word stemming
library(SnowballC)
wordStem(c("learn", "learned", "learning", "learns"))[1] "learn" "learn" "learn" "learn"sms_corpus_clean <- tm_map(sms_corpus_clean, stemDocument)
sms_corpus_clean <- tm_map(sms_corpus_clean, stripWhitespace) # eliminate unneeded whitespace
# examine the final clean corpus
lapply(sms_corpus[1:3], as.character)$`1`
[1] "Hope you are having a good week. Just checking in"
$`2`
[1] "K..give back my thanks."
$`3`
[1] "Am also doing in cbe only. But have to pay."lapply(sms_corpus_clean[1:3], as.character)$`1`
[1] "hope good week just check"
$`2`
[1] "kgive back thank"
$`3`
[1] " also cbe pay"# create a document-term sparse matrix
sms_dtm <- DocumentTermMatrix(sms_corpus_clean)
# alternative solution: create a document-term sparse matrix directly from the SMS corpus
sms_dtm2 <- DocumentTermMatrix(sms_corpus, control = list(
tolower = TRUE,
removeNumbers = TRUE,
stopwords = TRUE,
removePunctuation = TRUE,
stemming = TRUE
))
# alternative solution: using custom stop words function ensures identical result
sms_dtm3 <- DocumentTermMatrix(sms_corpus, control = list(
tolower = TRUE,
removeNumbers = TRUE,
stopwords = function(x) { removeWords(x, stopwords()) },
removePunctuation = TRUE,
stemming = TRUE
))
# compare the result
sms_dtm<<DocumentTermMatrix (documents: 5559, terms: 6518)>>
Non-/sparse entries: 42113/36191449
Sparsity : 100%
Maximal term length: 40
Weighting : term frequency (tf)sms_dtm2<<DocumentTermMatrix (documents: 5559, terms: 6909)>>
Non-/sparse entries: 43192/38363939
Sparsity : 100%
Maximal term length: 40
Weighting : term frequency (tf)sms_dtm3<<DocumentTermMatrix (documents: 5559, terms: 6518)>>
Non-/sparse entries: 42113/36191449
Sparsity : 100%
Maximal term length: 40
Weighting : term frequency (tf)# creating training and test datasets
sms_dtm_train <- sms_dtm[1:4169, ]
sms_dtm_test <- sms_dtm[4170:5559, ]
# also save the labels
sms_train_labels <- sms_raw[1:4169, ]$type
sms_test_labels <- sms_raw[4170:5559, ]$type
# check that the proportion of spam is similar
prop.table(table(sms_train_labels))sms_train_labels
ham spam
0.8647158 0.1352842 prop.table(table(sms_test_labels))sms_test_labels
ham spam
0.8683453 0.1316547 # word cloud visualization
library(wordcloud)Loading required package: RColorBrewerwordcloud(sms_corpus_clean, min.freq = 50, random.order = FALSE)
# subset the training data into spam and ham groups
spam <- subset(sms_raw, type == "spam")
ham <- subset(sms_raw, type == "ham")
wordcloud(spam$text, max.words = 40, scale = c(3, 0.5))
wordcloud(ham$text, max.words = 40, scale = c(3, 0.5))
sms_dtm_freq_train <- removeSparseTerms(sms_dtm_train, 0.999)
sms_dtm_freq_train<<DocumentTermMatrix (documents: 4169, terms: 1101)>>
Non-/sparse entries: 24834/4565235
Sparsity : 99%
Maximal term length: 19
Weighting : term frequency (tf)# indicator features for frequent words
findFreqTerms(sms_dtm_train, 5) [1] "abiola" "abl" "abt"
[4] "accept" "access" "account"
[7] "across" "act" "activ"
[10] "actual" "add" "address"
[13] "admir" "adult" "advanc"
[16] "aft" "afternoon" "age"
[19] "ago" "aha" "ahead"
[22] "aight" "aint" "air"
[25] "aiyo" "alex" "almost"
[28] "alon" "alreadi" "alright"
[31] "also" "alway" "angri"
[34] "announc" "anoth" "answer"
[37] "anymor" "anyon" "anyth"
[40] "anytim" "anyway" "apart"
[43] "app" "appli" "appreci"
[46] "arcad" "ard" "area"
[49] "argu" "argument" "armand"
[52] "around" "arrang" "arriv"
[55] "asap" "ask" "askd"
[58] "attempt" "auction" "avail"
[61] "ave" "avoid" "await"
[64] "awak" "award" "away"
[67] "awesom" "babe" "babi"
[70] "back" "bad" "bag"
[73] "bank" "bare" "basic"
[76] "bath" "batteri" "bcoz"
[79] "bday" "beauti" "becom"
[82] "bed" "bedroom" "beer"
[85] "begin" "believ" "best"
[88] "better" "bid" "big"
[91] "bill" "bird" "birthday"
[94] "bit" "black" "blank"
[97] "bless" "blue" "bluetooth"
[100] "bold" "bonus" "boo"
[103] "book" "boost" "bore"
[106] "boss" "bother" "bout"
[109] "box" "boy" "boytoy"
[112] "break" "breath" "bring"
[115] "brother" "bslvyl" "btnationalr"
[118] "buck" "bus" "busi"
[121] "buy" "cabin" "call"
[124] "caller" "callertun" "camcord"
[127] "came" "camera" "campus"
[130] "can" "cancel" "cancer"
[133] "cant" "car" "card"
[136] "care" "carlo" "case"
[139] "cash" "cashbal" "catch"
[142] "caus" "celebr" "cell"
[145] "centr" "chanc" "chang"
[148] "charg" "chat" "cheap"
[151] "cheaper" "check" "cheer"
[154] "chennai" "chikku" "childish"
[157] "children" "choic" "choos"
[160] "christma" "claim" "class"
[163] "clean" "clear" "close"
[166] "club" "code" "coffe"
[169] "cold" "colleagu" "collect"
[172] "colleg" "colour" "come"
[175] "comin" "comp" "compani"
[178] "competit" "complet" "complimentari"
[181] "comput" "condit" "confirm"
[184] "congrat" "congratul" "connect"
[187] "contact" "content" "contract"
[190] "cook" "cool" "copi"
[193] "correct" "cos" "cost"
[196] "costa" "costpm" "coupl"
[199] "cours" "cover" "coz"
[202] "crave" "crazi" "creat"
[205] "credit" "cri" "cross"
[208] "cuddl" "cum" "cup"
[211] "current" "custcar" "custom"
[214] "cut" "cute" "cuz"
[217] "dad" "daddi" "darl"
[220] "darlin" "darren" "dat"
[223] "date" "day" "dead"
[226] "deal" "dear" "decid"
[229] "decim" "decis" "deep"
[232] "definit" "del" "deliv"
[235] "deliveri" "den" "depend"
[238] "detail" "didnt" "die"
[241] "diet" "differ" "difficult"
[244] "digit" "din" "dinner"
[247] "direct" "dis" "discount"
[250] "discuss" "disturb" "dnt"
[253] "doc" "doctor" "doesnt"
[256] "dog" "doin" "don"
[259] "done" "dont" "door"
[262] "doubl" "download" "draw"
[265] "dream" "drink" "drive"
[268] "drop" "drug" "dude"
[271] "due" "dun" "dunno"
[274] "dvd" "earli" "earlier"
[277] "earth" "easi" "eat"
[280] "eatin" "egg" "either"
[283] "els" "email" "embarass"
[286] "end" "energi" "england"
[289] "enjoy" "enough" "enter"
[292] "entitl" "entri" "envelop"
[295] "etc" "euro" "eve"
[298] "even" "ever" "everi"
[301] "everybodi" "everyon" "everyth"
[304] "exact" "exam" "excel"
[307] "excit" "excus" "expect"
[310] "experi" "expir" "extra"
[313] "eye" "face" "facebook"
[316] "fact" "fall" "famili"
[319] "fanci" "fantasi" "fantast"
[322] "far" "fast" "fat"
[325] "father" "fault" "feb"
[328] "feel" "felt" "fetch"
[331] "fight" "figur" "file"
[334] "fill" "film" "final"
[337] "find" "fine" "finger"
[340] "finish" "first" "fix"
[343] "flag" "flat" "flight"
[346] "flower" "follow" "fone"
[349] "food" "forev" "forget"
[352] "forgot" "forward" "found"
[355] "freak" "free" "freemsg"
[358] "freephon" "fren" "fri"
[361] "friday" "friend" "friendship"
[364] "frm" "frnd" "frnds"
[367] "full" "fullonsmscom" "fun"
[370] "funni" "futur" "gal"
[373] "game" "gap" "gas"
[376] "gave" "gay" "gentl"
[379] "get" "gettin" "gift"
[382] "girl" "girlfrnd" "give"
[385] "glad" "god" "goe"
[388] "goin" "gone" "gonna"
[391] "good" "goodmorn" "goodnight"
[394] "got" "goto" "gotta"
[397] "great" "grin" "guarante"
[400] "gud" "guess" "guy"
[403] "gym" "haf" "haha"
[406] "hai" "hair" "half"
[409] "hand" "handset" "hang"
[412] "happen" "happi" "hard"
[415] "hate" "hav" "havent"
[418] "head" "hear" "heard"
[421] "heart" "heavi" "hee"
[424] "hell" "hello" "help"
[427] "hey" "hgsuiteland" "hit"
[430] "hiya" "hmm" "hmmm"
[433] "hmv" "hol" "hold"
[436] "holder" "holiday" "home"
[439] "hook" "hop" "hope"
[442] "horni" "hospit" "hot"
[445] "hotel" "hour" "hous"
[448] "how" "howev" "howz"
[451] "hrs" "httpwwwurawinnercom" "hug"
[454] "huh" "hungri" "hurri"
[457] "hurt" "ice" "idea"
[460] "identifi" "ignor" "ill"
[463] "immedi" "import" "inc"
[466] "includ" "india" "info"
[469] "inform" "insid" "instead"
[472] "interest" "invit" "ipod"
[475] "irrit" "ish" "island"
[478] "issu" "ive" "izzit"
[481] "januari" "jay" "job"
[484] "john" "join" "joke"
[487] "joy" "jst" "jus"
[490] "just" "juz" "kate"
[493] "keep" "kept" "kick"
[496] "kid" "kill" "kind"
[499] "kinda" "king" "kiss"
[502] "knew" "know" "knw"
[505] "ladi" "land" "landlin"
[508] "laptop" "lar" "last"
[511] "late" "later" "latest"
[514] "laugh" "lazi" "ldn"
[517] "lead" "learn" "least"
[520] "leav" "lect" "left"
[523] "leh" "lei" "less"
[526] "lesson" "let" "letter"
[529] "liao" "librari" "lie"
[532] "life" "lift" "light"
[535] "like" "line" "link"
[538] "list" "listen" "littl"
[541] "live" "lmao" "load"
[544] "loan" "local" "locat"
[547] "log" "lol" "london"
[550] "long" "longer" "look"
[553] "lookin" "lor" "lose"
[556] "lost" "lot" "lovabl"
[559] "love" "lover" "loyalti"
[562] "ltd" "luck" "lucki"
[565] "lunch" "luv" "mad"
[568] "made" "mah" "mail"
[571] "make" "malaria" "man"
[574] "mani" "march" "mark"
[577] "marri" "match" "mate"
[580] "matter" "maxim" "maxmin"
[583] "may" "mayb" "meal"
[586] "mean" "meant" "med"
[589] "medic" "meet" "meetin"
[592] "meh" "member" "men"
[595] "merri" "messag" "met"
[598] "mid" "midnight" "might"
[601] "min" "mind" "mine"
[604] "minut" "miracl" "miss"
[607] "mistak" "moan" "mob"
[610] "mobil" "mobileupd" "mode"
[613] "mom" "moment" "mon"
[616] "monday" "money" "month"
[619] "morn" "mother" "motorola"
[622] "move" "movi" "mrng"
[625] "mrt" "mrw" "msg"
[628] "msgs" "mths" "much"
[631] "mum" "murder" "music"
[634] "must" "muz" "nah"
[637] "nake" "name" "nation"
[640] "natur" "naughti" "near"
[643] "need" "net" "network"
[646] "neva" "never" "new"
[649] "news" "next" "nice"
[652] "nigeria" "night" "nite"
[655] "nobodi" "noe" "nokia"
[658] "noon" "nope" "normal"
[661] "normpton" "noth" "notic"
[664] "now" "num" "number"
[667] "nyt" "obvious" "offer"
[670] "offic" "offici" "okay"
[673] "oki" "old" "omg"
[676] "one" "onlin" "onto"
[679] "oop" "open" "oper"
[682] "opinion" "opt" "optout"
[685] "orang" "orchard" "order"
[688] "oredi" "oso" "other"
[691] "otherwis" "outsid" "pack"
[694] "page" "paid" "pain"
[697] "paper" "parent" "park"
[700] "part" "parti" "partner"
[703] "pass" "passion" "password"
[706] "past" "pay" "peopl"
[709] "per" "person" "pete"
[712] "phone" "photo" "pic"
[715] "pick" "pictur" "pin"
[718] "piss" "pix" "pizza"
[721] "place" "plan" "play"
[724] "player" "pleas" "pleasur"
[727] "plenti" "pls" "plus"
[730] "plz" "pmin" "pmsg"
[733] "pobox" "point" "poli"
[736] "polic" "poor" "pop"
[739] "possess" "possibl" "post"
[742] "pound" "power" "ppm"
[745] "pray" "present" "press"
[748] "pretti" "previous" "price"
[751] "princess" "privat" "prize"
[754] "prob" "probabl" "problem"
[757] "project" "promis" "pub"
[760] "put" "qualiti" "question"
[763] "quick" "quit" "quiz"
[766] "quot" "rain" "random"
[769] "rang" "rate" "rather"
[772] "rcvd" "reach" "read"
[775] "readi" "real" "reali"
[778] "realli" "reason" "receipt"
[781] "receiv" "recent" "record"
[784] "refer" "regard" "regist"
[787] "relat" "relax" "remain"
[790] "rememb" "remind" "remov"
[793] "rent" "rental" "repli"
[796] "repres" "request" "respond"
[799] "respons" "rest" "result"
[802] "return" "reveal" "review"
[805] "reward" "right" "ring"
[808] "rington" "rite" "road"
[811] "rock" "role" "room"
[814] "roommat" "rose" "round"
[817] "rowwjhl" "rpli" "rreveal"
[820] "run" "rush" "sad"
[823] "sae" "safe" "said"
[826] "sale" "sat" "saturday"
[829] "savamob" "save" "saw"
[832] "say" "sch" "school"
[835] "scream" "sea" "search"
[838] "sec" "second" "secret"
[841] "see" "seem" "seen"
[844] "select" "self" "sell"
[847] "semest" "send" "sens"
[850] "sent" "serious" "servic"
[853] "set" "settl" "sex"
[856] "sexi" "shall" "share"
[859] "shd" "ship" "shirt"
[862] "shop" "short" "show"
[865] "shower" "sick" "side"
[868] "sigh" "sight" "sign"
[871] "silent" "simpl" "sinc"
[874] "singl" "sipix" "sir"
[877] "sis" "sister" "sit"
[880] "situat" "skxh" "skype"
[883] "slave" "sleep" "slept"
[886] "slow" "slowli" "small"
[889] "smile" "smoke" "sms"
[892] "smth" "snow" "sofa"
[895] "sol" "somebodi" "someon"
[898] "someth" "sometim" "somewher"
[901] "song" "soni" "sonyericsson"
[904] "soon" "sorri" "sort"
[907] "sound" "south" "space"
[910] "speak" "special" "specialcal"
[913] "spend" "spent" "spoke"
[916] "spree" "stand" "start"
[919] "statement" "station" "stay"
[922] "std" "step" "still"
[925] "stockport" "stone" "stop"
[928] "store" "stori" "street"
[931] "student" "studi" "stuff"
[934] "stupid" "style" "sub"
[937] "subscrib" "success" "suck"
[940] "suit" "summer" "sun"
[943] "sunday" "sunshin" "sup"
[946] "support" "suppos" "sure"
[949] "surf" "surpris" "sweet"
[952] "swing" "system" "take"
[955] "talk" "tampa" "tariff"
[958] "tcs" "tea" "teach"
[961] "tear" "teas" "tel"
[964] "tell" "ten" "tenerif"
[967] "term" "test" "text"
[970] "thank" "thanx" "that"
[973] "thing" "think" "thinkin"
[976] "thk" "tho" "though"
[979] "thought" "throw" "thru"
[982] "tht" "thur" "tick"
[985] "ticket" "til" "till"
[988] "time" "tire" "titl"
[991] "tmr" "toclaim" "today"
[994] "togeth" "told" "tomo"
[997] "tomorrow" "tone" "tonight"
[1000] "tonit"
[ reached getOption("max.print") -- omitted 136 entries ]# save frequently-appearing terms to a character vector
sms_freq_words <- findFreqTerms(sms_dtm_train, 5)
str(sms_freq_words) chr [1:1136] "abiola" "abl" "abt" "accept" "access" "account" ...# create DTMs with only the frequent terms
sms_dtm_freq_train <- sms_dtm_train[ , sms_freq_words]
sms_dtm_freq_test <- sms_dtm_test[ , sms_freq_words]
# convert counts to a factor
convert_counts <- function(x) {
x <- ifelse(x > 0, "Yes", "No")
}
# apply() convert_counts() to columns of train/test data
sms_train <- apply(sms_dtm_freq_train, MARGIN = 2, convert_counts)
sms_test <- apply(sms_dtm_freq_test, MARGIN = 2, convert_counts)Step 3: Training a model on the data —-
library(e1071)
sms_classifier <- naiveBayes(sms_train, sms_train_labels)Step 4: Evaluating model performance —-
sms_test_pred <- predict(sms_classifier, sms_test)
head(sms_test_pred)[1] ham ham ham ham spam ham
Levels: ham spamlibrary(gmodels)
CrossTable(sms_test_pred, sms_test_labels,
prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
dnn = c('predicted', 'actual'))
Cell Contents
|-------------------------|
| N |
| N / Col Total |
|-------------------------|
Total Observations in Table: 1390
| actual
predicted | ham | spam | Row Total |
-------------|-----------|-----------|-----------|
ham | 1201 | 30 | 1231 |
| 0.995 | 0.164 | |
-------------|-----------|-----------|-----------|
spam | 6 | 153 | 159 |
| 0.005 | 0.836 | |
-------------|-----------|-----------|-----------|
Column Total | 1207 | 183 | 1390 |
| 0.868 | 0.132 | |
-------------|-----------|-----------|-----------|
Step 5: Improving model performance —-
sms_classifier2 <- naiveBayes(sms_train, sms_train_labels, laplace = 1)
sms_test_pred2 <- predict(sms_classifier2, sms_test)
CrossTable(sms_test_pred2, sms_test_labels,
prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
dnn = c('predicted', 'actual'))
Cell Contents
|-------------------------|
| N |
| N / Col Total |
|-------------------------|
Total Observations in Table: 1390
| actual
predicted | ham | spam | Row Total |
-------------|-----------|-----------|-----------|
ham | 1202 | 28 | 1230 |
| 0.996 | 0.153 | |
-------------|-----------|-----------|-----------|
spam | 5 | 155 | 160 |
| 0.004 | 0.847 | |
-------------|-----------|-----------|-----------|
Column Total | 1207 | 183 | 1390 |
| 0.868 | 0.132 | |
-------------|-----------|-----------|-----------|
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