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R Twitter patch

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Apr 21st, 2013
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  1. commit 3460700d98c26b1e0e255a54e10be722489b9391
  2. Author: unknown <dwmcqueen@gmail.com>
  3. Date: Sun Apr 21 12:41:01 2013 -0500
  4.  
  5. Updated Twitter to use OAuth and fixed UTF conversion
  6.  
  7. diff --git a/R/2_run.R b/R/2_run.R
  8. index 164c0cf..0f7e3cc 100644
  9. --- a/R/2_run.R
  10. +++ b/R/2_run.R
  11. @@ -9,20 +9,18 @@ if (VERBOSE)
  12. # we do end up with lots of objects in memory to play with (it _is_
  13. # a tutorial, after all :)
  14.  
  15. -american.text = laply(american.tweets, function(t) t$getText() )
  16. -delta.text = laply(delta.tweets, function(t) t$getText() )
  17. -jetblue.text = laply(jetblue.tweets, function(t) t$getText() )
  18. -southwest.text = laply(southwest.tweets, function(t) t$getText() )
  19. -united.text = laply(united.tweets, function(t) t$getText() )
  20. -us.text = laply(us.tweets, function(t) t$getText() )
  21. -
  22. +american.text = laply(american.tweets, function(t) iconv(t$getText(), to="UTF8"))
  23. +delta.text = laply(delta.tweets, function(t) iconv(t$getText(), to="UTF8") )
  24. +jetblue.text = laply(jetblue.tweets, function(t) iconv(t$getText(), to="UTF8") )
  25. +southwest.text = laply(southwest.tweets, function(t) iconv(t$getText(), to="UTF8") )
  26. +united.text = laply(united.tweets, function(t) iconv(t$getText(), to="UTF8") )
  27. +us.text = laply(us.tweets, function(t) iconv(t$getText(), to="UTF8") )
  28. american.scores = score.sentiment(american.text, pos.words, neg.words, .progress='text')
  29. delta.scores = score.sentiment(delta.text, pos.words, neg.words, .progress='text')
  30. jetblue.scores = score.sentiment(jetblue.text, pos.words, neg.words, .progress='text')
  31. southwest.scores = score.sentiment(southwest.text, pos.words, neg.words, .progress='text')
  32. united.scores = score.sentiment(united.text, pos.words, neg.words, .progress='text')
  33. us.scores = score.sentiment(us.text, pos.words, neg.words, .progress='text')
  34. -
  35. american.scores$airline = 'American'
  36. american.scores$code = 'AA'
  37. delta.scores$airline = 'Delta'
  38. diff --git a/R/scrape.R b/R/scrape.R
  39. index 56dcf2b..a138e92 100644
  40. --- a/R/scrape.R
  41. +++ b/R/scrape.R
  42. @@ -9,23 +9,51 @@ if (VERBOSE)
  43. print("Searching Twitter for airline tweets and saving to disk")
  44.  
  45. require(twitteR)
  46. -
  47. -american.tweets = searchTwitter('@americanair', n=1500)
  48. +library(RCurl)
  49. +library(ROAuth)
  50. +
  51. +#Need to make sure we have a caert
  52. +
  53. +if (!file.exists("cacert.pem"))
  54. + download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
  55. +
  56. +if (file.exists("twitter authentication.Rdata")){
  57. + load("twitter authentication.Rdata")
  58. +} else
  59. +{
  60. + requestURL <- "https://api.twitter.com/oauth/request_token"
  61. + accessURL = "http://api.twitter.com/oauth/access_token"
  62. + authURL = "http://api.twitter.com/oauth/authorize"
  63. + consumerKey = "FILLINWITHCONSUMERKEY"
  64. + consumerSecret = "FILLINWITHCONSUMERSECRET"
  65. + Cred <- OAuthFactory$new(consumerKey=consumerKey,
  66. + consumerSecret=consumerSecret,
  67. + requestURL=requestURL,
  68. + accessURL=accessURL,
  69. + authURL=authURL)
  70. + #The next command provides a URL which you will need to copy and paste into your favourite browser
  71. + #Assuming you are logged into Twitter you will then be provided a PIN number to type into the R command line
  72. + Cred$handshake(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl") )
  73. + # Checks that you are authorised
  74. + save(Cred, file="twitter authentication.Rdata")
  75. +}
  76. +registerTwitterOAuth(Cred)
  77. +american.tweets = searchTwitter('@americanair', n=1500, cainfo="cacert.pem")
  78. save(american.tweets, file=file.path(dataDir, 'american.tweets.RData' ), ascii=T)
  79.  
  80. -delta.tweets = searchTwitter('@delta', n=1500)
  81. +delta.tweets = searchTwitter('@delta', n=1500, cainfo="cacert.pem")
  82. save(delta.tweets, file=file.path(dataDir, 'delta.tweets.RData' ), ascii=T)
  83.  
  84. -jetblue.tweets = searchTwitter('@jetblue', n=1500)
  85. +jetblue.tweets = searchTwitter('@jetblue', n=1500, cainfo="cacert.pem")
  86. save(jetblue.tweets, file=file.path(dataDir, 'jetblue.tweets.RData' ), ascii=T)
  87.  
  88. -southwest.tweets = searchTwitter('@southwestair', n=1500)
  89. +southwest.tweets = searchTwitter('@southwestair', n=1500, cainfo="cacert.pem")
  90. save(southwest.tweets, file=file.path(dataDir, 'southwest.tweets.RData' ), ascii=T)
  91.  
  92. -united.tweets = searchTwitter('@united', n=1500)
  93. +united.tweets = searchTwitter('@united', n=1500, cainfo="cacert.pem")
  94. save(united.tweets, file=file.path(dataDir, 'united.tweets.RData' ), ascii=T)
  95.  
  96. -us.tweets = searchTwitter('@usairways', n=1500)
  97. +us.tweets = searchTwitter('@usairways', n=1500, cainfo="cacert.pem")
  98. save(us.tweets, file=file.path(dataDir, 'us.tweets.RData' ), ascii=T)
  99.  
  100.  
  101. @@ -45,7 +73,7 @@ acsi.df = acsi.raw.df[,c(1,19)]
  102. colnames(acsi.df) = c('airline', 'score')
  103.  
  104. # add codes for later matching, and make sure score is treated as a number (not a string)
  105. -acsi.df$code = c('WN', NA, NA, 'CO', 'AA', 'UA', 'US', 'DL', 'NW')
  106. +acsi.df$code = c('B6', 'WN', NA, NA, 'DL', 'US', 'AA', 'UA', NA, 'NW')
  107. acsi.df$score = as.numeric(acsi.df$score)
  108.  
  109. save(acsi.raw.df, file=file.path(dataDir, 'acsi.raw.df.RData'), ascii=T)
  110. diff --git a/R/sentiment.R b/R/sentiment.R
  111. index 4b2be84..f389fff 100644
  112. --- a/R/sentiment.R
  113. +++ b/R/sentiment.R
  114. @@ -14,7 +14,7 @@ score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
  115. {
  116. require(plyr)
  117. require(stringr)
  118. -
  119. +
  120. # we got a vector of sentences. plyr will handle a list or a vector as an "l" for us
  121. # we want a simple array of scores back, so we use "l" + "a" + "ply" = laply:
  122. scores = laply(sentences, function(sentence, pos.words, neg.words) {
  123. diff --git a/data/acsi.df.RData b/data/acsi.df.RData
  124. index 97d6f46..8b7511a 100644
  125. --- a/data/acsi.df.RData
  126. +++ b/data/acsi.df.RData
  127. @@ -1,115 +1,121 @@
  128. RDA2
  129. A
  130. 2
  131. -134400
  132. +196608
  133. 131840
  134. 1026
  135. 1
  136. -9
  137. +262153
  138. 7
  139. acsi.df
  140. 787
  141. 3
  142. 16
  143. -9
  144. -9
  145. +10
  146. +262153
  147. +7
  148. +JetBlue
  149. +262153
  150. 9
  151. Southwest
  152. -9
  153. +262153
  154. 10
  155. All\040Others
  156. -9
  157. +262153
  158. 8
  159. Airlines
  160. -9
  161. -11
  162. -Continental
  163. -9
  164. +262153
  165. +5
  166. +Delta
  167. +262153
  168. +10
  169. +US\040Airways
  170. +262153
  171. 8
  172. American
  173. -9
  174. +262153
  175. 6
  176. United
  177. -9
  178. -10
  179. -US\040Airways
  180. -9
  181. -5
  182. -Delta
  183. -9
  184. +262153
  185. +11
  186. +Continental
  187. +262153
  188. 18
  189. Northwest\040Airlines
  190. 14
  191. -9
  192. +10
  193. +NA
  194. 81
  195. 76
  196. 65
  197. -64
  198. -63
  199. +56
  200. 61
  201. +63
  202. 61
  203. -56
  204. +64
  205. NA
  206. 16
  207. -9
  208. -9
  209. +10
  210. +262153
  211. +2
  212. +B6
  213. +262153
  214. 2
  215. WN
  216. 9
  217. -1
  218. 9
  219. -1
  220. -9
  221. +262153
  222. 2
  223. -CO
  224. -9
  225. +DL
  226. +262153
  227. +2
  228. +US
  229. +262153
  230. 2
  231. AA
  232. -9
  233. +262153
  234. 2
  235. UA
  236. 9
  237. -2
  238. -US
  239. -9
  240. -2
  241. -DL
  242. -9
  243. +-1
  244. +262153
  245. 2
  246. NW
  247. 1026
  248. 1
  249. -9
  250. +262153
  251. 5
  252. names
  253. 16
  254. 3
  255. -9
  256. +262153
  257. 7
  258. airline
  259. -9
  260. +262153
  261. 5
  262. score
  263. -9
  264. +262153
  265. 4
  266. code
  267. 1026
  268. 1
  269. -9
  270. +262153
  271. 9
  272. row.names
  273. 13
  274. 2
  275. NA
  276. --9
  277. +-10
  278. 1026
  279. 1
  280. -9
  281. +262153
  282. 5
  283. class
  284. 16
  285. 1
  286. -9
  287. +262153
  288. 10
  289. data.frame
  290. 254
  291. diff --git a/data/acsi.raw.df.RData b/data/acsi.raw.df.RData
  292. index 546910e..c9e98ff 100644
  293. --- a/data/acsi.raw.df.RData
  294. +++ b/data/acsi.raw.df.RData
  295. @@ -1,711 +1,844 @@
  296. RDA2
  297. A
  298. 2
  299. -134400
  300. +196608
  301. 131840
  302. 1026
  303. 1
  304. -9
  305. +262153
  306. 11
  307. acsi.raw.df
  308. 787
  309. -21
  310. +23
  311. 16
  312. -9
  313. -9
  314. +10
  315. +262153
  316. +7
  317. +JetBlue
  318. +262153
  319. 9
  320. Southwest
  321. -9
  322. +262153
  323. 10
  324. All\040Others
  325. -9
  326. +262153
  327. 8
  328. Airlines
  329. -9
  330. -11
  331. -Continental
  332. -9
  333. +262153
  334. +5
  335. +Delta
  336. +262153
  337. +10
  338. +US\040Airways
  339. +262153
  340. 8
  341. American
  342. -9
  343. +262153
  344. 6
  345. United
  346. -9
  347. -10
  348. -US\040Airways
  349. -9
  350. -5
  351. -Delta
  352. -9
  353. +262153
  354. +11
  355. +Continental
  356. +262153
  357. 18
  358. Northwest\040Airlines
  359. 16
  360. -9
  361. -9
  362. +10
  363. +262153
  364. +2
  365. +NM
  366. +262153
  367. 2
  368. 78
  369. -9
  370. +262153
  371. 2
  372. NM
  373. -9
  374. +262153
  375. 2
  376. 72
  377. -9
  378. +262153
  379. 2
  380. -67
  381. -9
  382. +77
  383. +262153
  384. +2
  385. +72
  386. +262153
  387. 2
  388. 70
  389. -9
  390. +262153
  391. 2
  392. 71
  393. -9
  394. -2
  395. -72
  396. -9
  397. +262153
  398. 2
  399. -77
  400. -9
  401. +67
  402. +262153
  403. 2
  404. 69
  405. 16
  406. -9
  407. -9
  408. +10
  409. +262153
  410. +2
  411. +NM
  412. +262153
  413. 2
  414. 76
  415. -9
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  418. 70
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  421. 2
  422. 69
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  1472. diff --git a/output/twitter_acsi_comparison.pdf b/output/twitter_acsi_comparison.pdf
  1473. index fc66b1d..73b4c04 100644
  1474. --- a/output/twitter_acsi_comparison.pdf
  1475. +++ b/output/twitter_acsi_comparison.pdf
  1476. @@ -1,11 +1,12 @@
  1477. - airline
  1478. - q
  1479. - 80 q American
  1480. - q Delta
  1481. - q Southwest
  1482. - q US Airways
  1483. - q United
  1484. - 75
  1485. + airline
  1486. + q
  1487. + 80
  1488. + q American
  1489. + q Delta
  1490. + q JetBlue
  1491. + q Southwest
  1492. + q United
  1493. + 75 q US Airways
  1494.  
  1495.  
  1496.  
  1497. @@ -19,16 +20,16 @@ score.acsi
  1498. 65
  1499.  
  1500.  
  1501. - q
  1502. + q
  1503.  
  1504. - q q
  1505. + q q
  1506. 60
  1507.  
  1508.  
  1509.  
  1510.  
  1511. - q
  1512. -
  1513. - 40 45 50 55 60 65 70
  1514. - score.twitter
  1515. + q
  1516. + 55
  1517. + 40 60 80 100
  1518. + score.twitter
  1519. \ No newline at end of file
  1520. diff --git a/output/twitter_acsi_comparison_with_fit.pdf b/output/twitter_acsi_comparison_with_fit.pdf
  1521. index 2905f0a..4a43e36 100644
  1522. --- a/output/twitter_acsi_comparison_with_fit.pdf
  1523. +++ b/output/twitter_acsi_comparison_with_fit.pdf
  1524. @@ -1,11 +1,12 @@
  1525. - airline
  1526. - q
  1527. - 80 q American
  1528. - q Delta
  1529. - q Southwest
  1530. - q US Airways
  1531. - q United
  1532. - 75
  1533. + airline
  1534. + q
  1535. + 80
  1536. + q American
  1537. + q Delta
  1538. + q JetBlue
  1539. + q Southwest
  1540. + q United
  1541. + 75 q US Airways
  1542.  
  1543.  
  1544.  
  1545. @@ -19,16 +20,16 @@ score.acsi
  1546. 65
  1547.  
  1548.  
  1549. - q
  1550. + q
  1551.  
  1552. - q q
  1553. + q q
  1554. 60
  1555.  
  1556.  
  1557.  
  1558.  
  1559. - q
  1560. -
  1561. - 40 45 50 55 60 65 70
  1562. - score.twitter
  1563. + q
  1564. + 55
  1565. + 40 60 80 100
  1566. + score.twitter
  1567. \ No newline at end of file
  1568. diff --git a/output/twitter_score_histograms.pdf b/output/twitter_score_histograms.pdf
  1569. index 55f4562..7ce1a1d 100644
  1570. --- a/output/twitter_score_histograms.pdf
  1571. +++ b/output/twitter_score_histograms.pdf
  1572. @@ -1,66 +1,52 @@
  1573. + American
  1574. + 1000
  1575. 500
  1576. + 0
  1577.  
  1578. + 1000
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  1580.  
  1581.  
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  1584. - 300
  1585. - 200
  1586. - 100
  1587. - 0
  1588. +
  1589. + Delta
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  1591. - 400
  1592. + 0
  1593. + airline
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  1595.  
  1596.  
  1597.  
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  1599. - 300
  1600. - 200
  1601. - 100
  1602. - 0
  1603. - 500 airline
  1604. + JetBlue
  1605. + 1000 American
  1606. + 500 Delta
  1607. +count
  1608.  
  1609.  
  1610.  
  1611.  
  1612. - JetBlue
  1613. - 400
  1614. - 300 American
  1615. - 200
  1616. - 100 Delta
  1617. - 0
  1618. -count
  1619. + 0 JetBlue
  1620.  
  1621.  
  1622.  
  1623.  
  1624. - JetBlue
  1625. + Southwest
  1626. + 1000 Southwest
  1627.  
  1628. + 500 United
  1629.  
  1630. + 0 US Airways
  1631.  
  1632.  
  1633. - Southwest US Airways
  1634. - 500
  1635. - 400 Southwest
  1636. - 300
  1637. - 200
  1638. - 100 US Airways
  1639. - 0
  1640. - United
  1641. +
  1642. +
  1643. + United
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  1645. 500
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  1647. - 300
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  1651. + US Airways
  1652. + 1000
  1653. 500
  1654. - 400 United
  1655. - 300
  1656. - 200
  1657. - 100
  1658. 0
  1659. - −6 −4 −2 0 2 4 6
  1660. - score
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  1663. \ No newline at end of file
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