我做一個Twitter情感分析，只有正在處理

Question

R檔從Twitter的我再換一個：

# Install and Activate Packages 
# install.packages("twitteR", "RCurl", "RJSONIO", "stringr", "ROAuth") 

library(twitteR) 
library(RCurl) 
library(RJSONIO) 
library(stringr) 
library(ROAuth) 
library(plyr) 

# Declare Twitter API Credentials 
api_key <- "MLTS91hQlJaPOPpdDTOeNAaHU" # From dev.twitter.com 
api_secret <- "bUdEdspvjOfQ192INw48K84lZulRBLgOMqu0ukr6FRyysaW95f" # From dev.twitter.com 
token <- "336148817-VQgCSg9KDng71zNOpsbkFhHSPImaO1nFvJTd4HmK" # From dev.twitter.com 
token_secret <- "WAGmLbICos6M2QQaWJWGyhKPKCPqyE5pVV1FPNLMg5tkI" # From dev.twitter.com 

# Create Twitter Connection 
setup_twitter_oauth(api_key, api_secret, token, token_secret) 

delta.tweets = searchTwitter('@delta', n=1500) 
#delta.tweets 
americanair.tweets = searchTwitter('@americanair', n=1500) 
#americanair.tweets 
#JetBlueAirlines.tweets = searchTwitter('@jetblueairlines', n=1500) 
SouthwestAir.tweets = searchTwitter('@SouthwestAir', n=1500) 
united.tweets = searchTwitter('@united', n=1500) 
USAirways.tweets = searchTwitter('@USAirways', n=1500) 

#loading opinion lexicon 
setwd('C:/Users/SRC/Downloads') 
hu.liu.pos <- scan('positive-words.txt', 
        what='character', comment.char=';') 
hu.liu.neg = scan('negative-words.txt', 
        what='character', comment.char=';') 
pos.words = c(hu.liu.pos, 'upgrade') 
neg.words = c(hu.liu.neg, 'wtf', 'wait', 
       'waiting', 'epicfail', 'mechanical') 
hu.liu.pos 
hu.liu.neg 

#Sentiment analysis code 
score.sentiment = function(sentences, pos.words, neg.words, .progress='none') 
{ 
    require(plyr) 
    require(stringr) 
    # we got a vector of sentences. plyr will handle a list or a vector as an "l" for us 
    # we want a simple array of scores back, so we use "l" + "a" + "ply" = laply: 
    scores = laply(sentences, function(sentence, pos.words, neg.words) { 

    # clean up sentences with R's regex-driven global substitute, gsub(): 
    sentence = gsub('[[:punct:]]', '', sentence) 
    sentence = gsub('[[:cntrl:]]', '', sentence) 
    sentence = gsub('\\d+', '', sentence) 
    # and convert to lower case: 
    sentence = tolower(sentence) 
    # split into words. str_split is in the stringr package 
    word.list = str_split(sentence, '\\s+') 
    # sometimes a list() is one level of hierarchy too much 
    words = unlist(word.list) 
    # compare our words to the dictionaries of positive & negative terms 
    pos.matches = match(words, pos.words) 
    neg.matches = match(words, neg.words) 
    # match() returns the position of the matched term or NA 
    # we just want a TRUE/FALSE: 
    pos.matches = !is.na(pos.matches) 
    neg.matches = !is.na(neg.matches) 
    # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum(): 
    score = sum(pos.matches) - sum(neg.matches) 
    return(score) 
    }, pos.words, neg.words, .progress=.progress) 
    scores.df = data.frame(score=scores, text=sentences) 
    return(scores.df) 
} 


delta.scores = score.sentiment(delta.text, pos.words, 
           neg.words, .progress='text') 

hist(delta.scores$score) 

americainair.scores = score.sentiment(americanair.text, pos.words, 
           neg.words, .progress='text') 

Southwestair.scores = score.sentiment(Southwestair.text, pos.words, 
           neg.words, .progress='text') 

united.scores = score.sentiment(united.text, pos.words, 
           neg.words, .progress='text') 

USAirways.scores = score.sentiment(USAirways.text, pos.words, 
           neg.words, .progress='text') 

RStudio控制檯：

> delta.scores = score.sentiment(delta.text, pos.words, 
+ neg.words, .progress='text') 
    |======================================================================================================| 100% 
> hist(delta.scores$score) 
> americainair.scores = score.sentiment(americanair.text, pos.words, 
+ neg.words, .progress='text') 

顯示回溯

重新運行調試 繼承錯誤（.data，「split」）：object'americanair.text'not found> Southwestair.scores = score.sentiment（Southwestair.text，pos.words， 個+ neg.words，.progress =在繼承 '文本'）

顯示回溯

重新運行與調試 誤差（。數據， 「分裂」）：對象 'Southwestair.text' 未找到>聯合起來。分數= score.sentiment（united.text，pos.words， + neg.words，.progress = '文本'） 顯示回溯

重新運行在繼承調試 誤差（。數據， 「分裂」）：對象'united.text'找不到> USAirways.scores = score.sentiment（USAirways.text，pos.words， + neg.words，.progress ='text'） Show Traceback

重新運行與調試 錯誤在繼承（。數據，「分裂」）：對象「USAirways.text」未找到

Answer 1

想通了，愚蠢的錯誤具有可變的字體的情況下