У меня большой массив данных, состоящий из твитов и словарей ключевых слов, загруженных в виде значений, слова которых связаны с моралью (kw_Moral) и эмоциями (kw_Emo).В прошлом я использовал словари ключевых слов для поднабора фрейма данных, чтобы получать только те твиты, в которых присутствует одно или несколько ключевых слов.
Например, чтобы создать подмножество только с теми твитами, которые содержат эмоциональные ключевые слова, я загрузил в свой словарь ключевых слов ...
kw_Emo <- c("abusi*", "accept", "accepta*", "accepted",
"accepting", "accepts", "ache*", "aching", "active*", "admir*",
"ador*", "advantag*", "adventur*", "advers*", "affection*", "afraid",
"aggravat*", "aggress*", "agoniz*", "agony", "agree", "agreeab*",
"agreed", "agreeing", "agreement*", "agrees", "alarm*", "alone",
"alright*", "amaz*", "amor*", "amus*", "anger*", "angr*", "anguish*",
"annoy*", "antagoni*", "anxi*", "aok", "apath*", "appall*", "appreciat*",
"apprehens*", "argh*", "argu*", "arrogan*", "asham*", "assault*",
"asshole*", "assur*", "attachment*", "attract*", "aversi*", "avoid*",
"award*", "awesome", "awful", "awkward*", "bashful*", "bastard*",
"battl*", "beaten", "beaut*", "beloved", "benefic*", "benevolen*",
"benign*", "best", "better", "bitch*", "bitter*", "blam*", "bless*",
"bold*", "bonus*", "bore*", "boring", "bother*", "brave*", "bright*",
"brillian*", "broke", "burden*", "calm*", "cared", "carefree",
"careful*", "careless*", "cares", "casual", "casually", "certain*",
"challeng*", "champ*", "charit*", "charm*", "cheer*", "cherish*",
"chuckl*", "clever*", "comed*", "comfort*", "commitment*", "complain*",
"compliment*", "concerned", "confidence", "confident", "confidently",
"confront*", "confus*", "considerate", "contempt*", "contented*",
"contentment", "contradic*", "convinc*", "cool", "courag*", "crap",
"crappy", "craz*", "create*", "creati*", "credit*", "cried",
"cries", "critical", "critici*", "crude*", "cry", "crying", "cunt*",
"cut", "cute*", "cutie*", "cynic", "danger*", "daring", "darlin*",
"daze*", "dear*", "decay*", "defeat*", "defect*", "definite",
"definitely", "degrad*", "delectabl*", "delicate*", "delicious*",
"deligh*", "depress*", "depriv*", "despair*", "desperat*", "despis*",
"destruct*", "determina*", "determined", "devastat*", "difficult*",
"digni*", "disadvantage*", "disagree*", "disappoint*", "disaster*",
"discomfort*", "discourag*", "dishearten*", "disillusion*", "dislike",
"disliked", "dislikes", "disliking", "dismay*", "dissatisf*",
"distract*", "distraught", "distress*", "distrust*", "disturb*",
"divin*", "domina*", "doom*", "dork*", "doubt*", "dread*", "dull*",
"dumb*", "dump*", "dwell*", "dynam*", "eager*", "ease*", "easie*",
"easily", "easiness", "easing", "easy*", "ecsta*", "efficien*",
"egotis*", "elegan*", "embarrass*", "emotion", "emotional", "empt*",
"encourag*", "energ*", "engag*", "enjoy*", "enrag*", "entertain*",
"enthus*", "envie*", "envious", "excel*", "excit*", "excruciat*",
"exhaust*", "fab", "fabulous*", "fail*", "fake", "fantastic*",
"fatal*", "fatigu*", "favor*", "favour*", "fear", "feared", "fearful*",
"fearing", "fearless*", "fears", "feroc*", "festiv*", "feud*",
"fiery", "fiesta*", "fine", "fired", "flatter*", "flawless*",
"flexib*", "flirt*", "flunk*", "foe*", "fond", "fondly", "fondness",
"fool*", "forgave", "forgiv*", "fought", "frantic*", "freak*",
"free", "freeb*", "freed*", "freeing", "freely", "freeness",
"freer", "frees*", "friend*", "fright*", "frustrat*", "fuck",
"fucked*", "fucker*", "fuckin*", "fucks", "fume*", "fuming",
"fun", "funn*", "furious*", "fury", "geek*", "genero*", "gentle",
"gentler", "gentlest", "gently", "giggl*", "giver*", "giving",
"glad", "gladly", "glamor*", "glamour*", "gloom*", "glori*",
"glory", "goddam*", "gorgeous*", "gossip*", "grace", "graced",
"graceful*", "graces", "graci*", "grand", "grande*", "gratef*",
"grati*", "grave*", "great", "grief", "griev*", "grim*", "grin",
"grinn*", "grins", "grouch*", "grr*", "guilt*", "ha", "haha*",
"handsom*", "happi*", "happy", "harass*", "hated", "hateful*",
"hater*", "hates", "hating", "hatred", "hazy", "heartbreak*",
"heartbroke*", "heartfelt", "heartless*", "heartwarm*", "heh*",
"hellish", "helper*", "helpful*", "helping", "helpless*", "helps",
"hesita*", "hilarious", "hoho*", "homesick*", "honour*", "hope",
"hoped", "hopeful", "hopefully", "hopefulness", "hopeless*",
"hopes", "hoping", "horr*", "hostil*", "hug", "hugg*", "hugs",
"humiliat*", "humor*", "humour*", "hurra*", "idiot", "ignor*",
"impatien*", "impersonal", "impolite*", "importan*", "impress*",
"improve*", "improving", "inadequa*", "incentive*", "indecis*",
"ineffect*", "inferior*", "inhib*", "innocen*", "insecur*", "insincer*",
"inspir*", "insult*", "intell*", "interest*", "interrup*", "intimidat*",
"invigor*", "irrational*", "irrita*", "isolat*", "jaded", "jealous*",
"jerk", "jerked", "jerks", "joke*", "joking", "joll*", "joy*",
"keen*", "kidding", "kind", "kindly", "kindn*", "kiss*", "laidback",
"lame*", "laugh*", "lazie*", "lazy", "liabilit*", "libert*",
"lied", "lies", "like", "likeab*", "liked", "likes", "liking",
"livel*", "LMAO", "LOL", "lone*", "longing*", "lose", "loser*",
"loses", "losing", "loss*", "lost", "lous*", "love", "loved",
"lovely", "lover*", "loves", "loving*", "low*", "luck", "lucked",
"lucki*", "luckless*", "lucks", "lucky", "ludicrous*", "lying",
"mad", "maddening", "madder", "maddest", "madly", "magnific*",
"maniac*", "masochis*", "melanchol*", "merit*", "merr*", "mess",
"messy", "miser*", "miss", "missed", "misses", "missing", "mistak*",
"mock", "mocked", "mocker*", "mocking", "mocks", "molest*", "mooch*",
"mood", "moodi*", "moods", "moody", "moron*", "mourn*", "nag*",
"nast*", "neat*", "needy", "neglect*", "nerd*", "nervous*", "neurotic*",
"nice*", "numb*", "nurtur*", "obnoxious*", "obsess*", "offence*",
"offens*", "ok", "okay", "okays", "oks", "openminded*", "openness",
"opportun*", "optimal*", "optimi*", "original", "outgoing", "outrag*",
"overwhelm*", "pained", "painf*", "paining", "painl*", "pains",
"palatabl*", "panic*", "paradise", "paranoi*", "partie*", "party*",
"passion*", "pathetic*", "peculiar*", "perfect*", "personal",
"perver*", "pessimis*", "petrif*", "pettie*", "petty*", "phobi*",
"piss*", "piti*", "pity*", "play", "played", "playful*", "playing",
"plays", "pleasant*", "please*", "pleasing", "pleasur*", "poison*",
"popular*", "positiv*", "prais*", "precious*", "pressur*", "prettie*",
"pretty", "prick*", "pride", "privileg*", "prize*", "problem*",
"profit*", "promis*", "protested", "protesting", "proud*", "puk*",
"radian*", "rage*", "raging", "rancid*", "rape*", "raping", "rapist*",
"readiness", "ready", "reassur*", "reek*", "regret*", "reject*",
"relax*", "relief", "reliev*", "reluctan*", "remorse*", "repress*",
"resent*", "resign*", "resolv*", "restless*", "revigor*", "reward*",
"rich*", "ridicul*", "rigid*", "risk*", "ROFL", "romanc*", "romantic*",
"rotten", "rude*", "sad", "sadde*", "sadly", "sadness", "sarcas*",
"satisf*", "savage*", "scare*", "scaring", "scary", "sceptic*",
"scream*", "screw*", "selfish*", "sentimental*", "serious", "seriously",
"seriousness", "severe*", "shake*", "shaki*", "shaky", "share",
"shared", "shares", "sharing", "shit*", "shock*", "shook", "shy*",
"sigh", "sighed", "sighing", "sighs", "silli*", "silly", "sincer*",
"skeptic*", "smart*", "smil*", "smother*", "smug*", "snob*",
"sob", "sobbed", "sobbing", "sobs", "sociab*", "solemn*", "sorrow*",
"sorry", "soulmate*", "special", "splend*", "stammer*", "stank",
"startl*", "stink*", "strain*", "strange", "strength*", "stress*",
"strong*", "struggl*", "stubborn*", "stunk", "stunned", "stuns",
"stupid*", "stutter*", "succeed*", "success*", "suck", "sucked",
"sucker*", "sucks", "sucky", "sunnier", "sunniest", "sunny",
"sunshin*", "super", "superior*", "support", "supported", "supporter*",
"supporting", "supportive*", "supports", "suprem*", "sure*",
"surpris*", "suspicio*", "sweet", "sweetheart*", "sweetie*",
"sweetly", "sweetness*", "sweets", "talent*", "tantrum*", "tears",
"teas*", "tehe", "temper", "tempers", "tender*", "tense*", "tensing",
"tension*", "terribl*", "terrific*", "terrified", "terrifies",
"terrify", "terrifying", "terror*", "thank", "thanked", "thankf*",
"thanks", "thief", "thieve*", "thoughtful*", "threat*", "thrill*",
"ticked", "timid*", "toleran*", "tortur*", "tough*", "traged*",
"tragic*", "tranquil*", "trauma*", "treasur*", "treat", "trembl*",
"trick*", "trite", "triumph*", "trivi*", "troubl*", "TRUE", "trueness",
"truer", "truest", "truly", "trust*", "truth*", "turmoil", "ugh",
"ugl*", "unattractive", "uncertain*", "uncomfortabl*", "uncontrol*",
"uneas*", "unfortunate*", "unfriendly", "ungrateful*", "unhapp*",
"unimportant", "unimpress*", "unkind", "unlov*", "unpleasant",
"unprotected", "unsavo*", "unsuccessful*", "unsure*", "unwelcom*",
"upset*", "uptight*", "useful*", "useless*", "vain", "valuabl*",
"valuing", "vanity", "vicious*", "vigor*", "vigour*", "villain*",
"violat*", "virtuo*", "vital*", "vulnerab*", "vulture*", "warfare*",
"warm*", "warred", "weak*", "wealth*", "weapon*", "weep*", "weird*",
"welcom*", "well*", "wept", "whine*", "whining", "willing", "wimp*",
"win", "winn*", "wins", "wisdom", "wise*", "witch", "woe*", "won",
"wonderf*", "worr*", "worse*", "worship*", "worst", "wow*", "yay",
"yays","yearn*","stench*") %>% paste0(collapse="|")and then filtered my dataframe with the keywords...
tweets_E <- tweets[with(tweets, grepl(paste0("\\b(?:",paste(kw_Emo, collapse="|"),")\\b"), text)),]
Как мне расширить этот процесс, чтобы точно подсчитать, сколько слов из словаря появляется в каждом твите? Другими словами, я хочу добавить вектор к кадру данных, скажем EmoWordCount, который покажет числоэмоциональных слов появляются в каждом твите.
Вот воспроизводимый образец моих данных:
dput(droplevels(head(TestTweets, 20)))
structure(list(Time = c("24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:03", "24/06/2016 10:55:03"
), clean_text = c("mayagoodfellow as always making sense of it all for us ive never felt less welcome in this country brexit httpstcoiai5xa9ywv",
"never underestimate power of stupid people in a democracy brexit",
"a quick guide to brexit and beyond after britain votes to quit eu httpstcos1xkzrumvg httpstcocniutojkt0",
"this selfinflicted wound will be his legacy cameron falls on sword after brexit euref httpstcoegph3qonbj httpstcohbyhxodeda",
"so the uk is out cameron resigned scotland wants to leave great britain sinn fein plans to unify ireland and its o",
"this is a very good summary no biasspinagenda of the legal ramifications of the leave result brexit httpstcolobtyo48ng",
"you cant make this up cornwall votes out immediately pleads to keep eu cash this was never a rehearsal httpstco",
"no matter the outcome brexit polls demonstrate how quickly half of any population can be convinced to vote against itself q",
"i wouldnt mind so much but the result is based on a pack of lies and unaccountable promises democracy didnt win brexit pro",
"so the uk is out cameron resigned scotland wants to leave great britain sinn fein plans to unify ireland and its o",
"absolutely brilliant poll on brexit by yougov httpstcoepevg1moaw",
"retweeted mikhail golub golub\r\n\r\nbrexit to be followed by grexit departugal italeave fruckoff czechout httpstcoavkpfesddz",
"think the brexit campaign relies on the same sort of logic that drpepper does whats the worst that can happen thingsthatarewellbrexit",
"am baffled by nigel farages claim that brexit is a victory for real people as if the 47 voting remain are fucking smu",
"not one of the uks problems has been solved by brexit vote migration inequality the uks centurylong decline as",
"scotland should never leave eu calls for new independence vote grow httpstcorudiyvthia brexit",
"the most articulate take on brexit is actually this ft reader comment today httpstco98b4dwsrtv",
"65 million refugees half of them are children maybe instead of fighting each other we should be working hand in hand ",
"im laughing at people who voted for brexit but are complaining about the exchange rate affecting their holiday\r\nremain",
"life is too short to wear boring shoes brexit")), .Names = c("Time",
"clean_text"), row.names = c(NA, 20L), class = c("tbl_df", "tbl",
"data.frame"))
Вот кодЯ использовал из Франциско:
library(stringr)
for (x in 1:length(kw_Emo)) {
if (grepl("[*]", kw_Emo[x]) == TRUE) {
kw_Emo[x] <- substr(kw_Emo[x],1,nchar(kw_Emo[x])-1)
}
}
for (x in 1:length(kw_Emo)) {
TestTweets[, kw_Emo[x]] <- 0
}
for (x in 1:nrow(TestTweets)) {
partials <- data.frame(str_split(TestTweets[x,2], " "), stringsAsFactors=FALSE)
partials <- partials[partials[] != ""]
for(y in 1:length(partials)) {
for (z in 1:length(kw_Emo)) {
if (kw_Emo[z] == partials[y]) {
TestTweets[x, kw_Emo[z]] <- TestTweets[x, kw_Emo[z]] + 1
}
}
}
}
Вот результат решения Франциско ниже (я переименовал новый столбец EmoWordCount):
structure(list(Time = c("24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:04", "24/06/2016 10:55:04",
"24/06/2016 10:55:04", "24/06/2016 10:55:03", "24/06/2016 10:55:03"
), clean_text = c("mayagoodfellow as always making sense of it all for us ive never felt less welcome in this country brexit httpstcoiai5xa9ywv",
"never underestimate power of stupid people in a democracy brexit",
"a quick guide to brexit and beyond after britain votes to quit eu httpstcos1xkzrumvg httpstcocniutojkt0",
"this selfinflicted wound will be his legacy cameron falls on sword after brexit euref httpstcoegph3qonbj httpstcohbyhxodeda",
"so the uk is out cameron resigned scotland wants to leave great britain sinn fein plans to unify ireland and its o",
"this is a very good summary no biasspinagenda of the legal ramifications of the leave result brexit httpstcolobtyo48ng",
"you cant make this up cornwall votes out immediately pleads to keep eu cash this was never a rehearsal httpstco",
"no matter the outcome brexit polls demonstrate how quickly half of any population can be convinced to vote against itself q",
"i wouldnt mind so much but the result is based on a pack of lies and unaccountable promises democracy didnt win brexit pro",
"so the uk is out cameron resigned scotland wants to leave great britain sinn fein plans to unify ireland and its o",
"absolutely brilliant poll on brexit by yougov httpstcoepevg1moaw",
"retweeted mikhail golub golub\r\n\r\n\r\n\r\nbrexit to be followed by grexit departugal italeave fruckoff czechout httpstcoavkpfesddz",
"think the brexit campaign relies on the same sort of logic that drpepper does whats the worst that can happen thingsthatarewellbrexit",
"am baffled by nigel farages claim that brexit is a victory for real people as if the 47 voting remain are fucking smu",
"not one of the uks problems has been solved by brexit vote migration inequality the uks centurylong decline as",
"scotland should never leave eu calls for new independence vote grow httpstcorudiyvthia brexit",
"the most articulate take on brexit is actually this ft reader comment today httpstco98b4dwsrtv",
"65 million refugees half of them are children maybe instead of fighting each other we should be working hand in hand ",
"im laughing at people who voted for brexit but are complaining about the exchange rate affecting their holiday\r\n\r\nremain",
"life is too short to wear boring shoes brexit"), EmoWordCount = c(3,
2, 0, 3, 5, 4, 3, 5, 7, 5, 2, 5, 11, 6, 6, 5, 1, 7, 6, 4)), .Names = c("Time",
"clean_text", "EmoWordCount"), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"))