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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
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# SentimentDictionaries
**SentimentDictionaries** is an R package containing dictionaries for sentiment analysis of movie reviews and financial filings.
## Overview
This package provides domain-specific dictionaries for sentiment analysis in R. Each dictionary consists of words that statistically feature a positive or negative polarity in movie reviews or financial filings The dictionaries are extracted from two different corpora, namely, IMDb movie reviews and U.S. regulated Form 8-K filings. Details are available from the following reference.
* Proellochs, Feuerriegel and Neumann (2017): Language That Matters: Statistical Inferences for Polarity Identification in Natural Language, Working Paper, Chair for Information Systems Research, University of Freiburg, Germany.
Both dictionaries can be easily used in combination with the [SentimentAnalysis](https://github.com/sfeuerriegel/SentimentAnalysis) package.
## Installation
You can install **SentimentDictionaries** from github with:
```R
# install.packages("devtools")
devtools::install_github("nproellochs/SentimentDictionaries", subdir = "R-package")
```
## Usage
This section shows the basic functionality of how to load the provided dictionaries into R. First, load the corresponding package **SentimentDictionaries**.
```{r, message=FALSE}
library(SentimentDictionaries)
```
### Load IMDb movie reviews dictionary
The following code shows how to load the dictionary for sentiment analysis of movie reviews.
```{r, message=FALSE}
dictionaryIMDB <- loadDictionaryIMDB()
head(dictionaryIMDB)
```
### Load Form 8-K filings dictionary
The following code shows how to load the dictionary for sentiment analysis of financial filings.
```{r, message=FALSE}
dictionary8K <- loadDictionary8K()
head(dictionary8K)
```
## License
**SentimentDictionaries** is released under the [MIT License](https://opensource.org/licenses/MIT)
Copyright (c) 2017 Nicolas Proellochs & Stefan Feuerriegel
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