https://github.com/mananshah99/sentR

Simple sentiment analysis framework for R
https://github.com/mananshah99/sentR

nlp r sentiment-analysis

Last synced: about 2 months ago
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Simple sentiment analysis framework for R

• Host: GitHub
• URL: https://github.com/mananshah99/sentR
• Owner: mananshah99
• License: mit
• Created: 2015-03-12T07:24:52.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2016-09-14T04:53:24.000Z (about 8 years ago)
• Last Synced: 2024-05-21T02:52:31.573Z (4 months ago)
• Topics: nlp, r, sentiment-analysis
• Language: R
• Homepage:
• Size: 462 KB
• Stars: 31
• Watchers: 8
• Forks: 18
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


  



SentR is an R package that provides functional sentiment analysis utilities with complete documentation and simple workflow integration. 

## Install [![Build Status](https://travis-ci.org/mananshah99/sentR.svg?branch=master)](https://travis-ci.org/mananshah99/sentR)

1. Open the R shell (32 or 64 bit)

2. Install devtools: run `install.packages('devtools')`

2. Require devtools: run `require('devtools')`

3. Install sentR@latest: run `install_github('mananshah99/sentR')`

# Sentiment Scoring Examples

Input:

```R

require('sentR')

# Create small vectors for happy and sad words (useful in aggregate(...) function)

positive <- c('happy', 'well-off', 'good', 'happiness')

negative <- c('sad', 'bad', 'miserable', 'terrible')

# Words to test sentiment

test <- c('I am a very happy person.', 'I am a very sad person', 

'I’ve always understood happiness to be appreciation. There is no greater happiness than appreciation for what one has- both physically and in the way of relationships and ideologies. The unhappy seek that which they do not have and can not fully appreciate the things around them. I don’t expect much from life. I don’t need a high paying job, a big house or fancy cars. I simply wish to be able to live my life appreciating everything around me. 

')

# 1. Simple Summation

out <- classify.aggregate(test, positive, negative)

out

# 2. Naive Bayes

out <- classify.naivebayes(test)

out

```

Output:

```

  score

1     1

2    -1

3     2

     POS                NEG                 POS/NEG             SENT      

[1,] "9.47547003995745" "0.445453222112551" "21.2715265477714"  "positive"

[2,] "1.03127774142571" "9.47547003995745"  "0.108836578774127" "negative"

[3,] "67.1985217685598" "35.1792261323723"  "1.9101762362738"   "positive"

```