Text Mining and Sentiment Analysis using Twitter Data
Before Cleaning
- Converting to Lower Case.
- Removing Punctuations.
- Text Stemming.
- Removing Stopwords.
- Removing Numbers.
## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 5
##
## [1] Party least receive say or single. Prevent prevent husband affect. May himself cup style evening protect. Effect another themselves stage perform.\nPossible try tax share style television with. Successful much sell development economy effect.
## [2] Hotel still Congress may member staff. Media draw buy fly. Identify on another turn minute would.\nLocal subject way believe which question some message. Own all imagine join agency indicate.
## [3] Nice be her debate industry that year. Film where generation push discover partner level.\nNearly money store style may enjoy. Kid discuss blue save. Model another about along.\nEverybody especially dinner character yard.
## [4] Laugh explain situation career occur serious. Five particular important size.\nCatch continue east teach dark discussion spring. Then candidate finally deep child. Big field billion force the there get.
## [5] Involve sense former often approach government. While season family term close do number. Cost through second image indeed.\nProduction thousand will to. Natural research land. Bank option one party nation.\nMother appear leader student discussion. Region arrive idea another.After Cleaning
## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 5
##
## [1] parti least receiv say singl prevent prevent husband affect may cup style even protect effect anoth stage perform possibl tri tax share style televis success much sell develop economi effect
## [2] hotel still congress may member staff media draw buy fli identifi anoth turn minut local subject way believ question messag imagin join agenc indic
## [3] nice debat industri year film generat push discov partner level near money store style may enjoy kid discuss blue save model anoth along everybodi especi dinner charact yard
## [4] laugh explain situat career occur serious five particular import size catch continu east teach dark discuss spring candid final deep child big field billion forc get
## [5] involv sens former often approach govern season famili term close number cost second imag inde product thousand will natur research land bank option one parti nation mother appear leader student discuss region arriv idea anoth## Docs
## Terms 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## affect 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## anoth 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## cup 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## develop 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## economi 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## effect 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## even 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## husband 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## least 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0
## may 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Glimpse of the First 10 Sentiments
head(s, 10)## anger anticipation disgust fear joy sadness surprise trust negative positive
## 1 0 2 0 1 2 1 0 3 1 3
## 2 0 0 1 0 0 0 0 1 1 2
## 3 1 3 0 0 3 1 1 4 0 9
## 4 1 4 1 1 4 1 4 5 1 10
## 5 0 3 0 1 1 1 0 4 2 7
## 6 1 2 1 4 2 1 2 6 2 5
## 7 1 1 0 1 1 0 0 1 1 4
## 8 1 5 0 0 3 1 0 5 2 6
## 9 1 2 0 1 2 1 0 2 0 5
## 10 1 3 0 1 1 0 1 6 0 3Sentiments
Reaction
78% positive 22% negative
Emotions in Text
