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Sentiment Analysis

SentimentAnalysis performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as QDAP, Harvard IV or Loughran-McDonald. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.

Overview

The most important functions in SentimentAnalysis are:

  • Compute sentiment scores from contents stored in different formats with analyzeSentiment().

  • If desired, convert the continuous scores to either binary sentiment classes (negative or positive) or tertiary directions (negative, neutral or positive). This conversion can be done with convertToBinary() or convertToDirection() respectively.

  • Compare the calculated sentiment socres with a baseline (i.e. a gold standard). Here, compareToResponse() performs a statistical evaluation, while plotSentimentResponse() enables a visual comparison.

  • Generate customized dictionaries with the help of generateDictionary() as part of an advanced analysis. However, this prerequisites a response variable (i.e. the baseline).

To see examples of these functions in use, check out the help pages, the demos and the vignette.

Usage

This section shows the basic functionality of how to perform a sentiment analysis. First, install the package from CRAN. Then load the corresponding package SentimentAnalysis.

# install.packages("SentimentAnalysis")

library(SentimentAnalysis)

Quick demonstration

This simple example shows how to perform a sentiment analysis of a single string. The result is a two-level factor with levels "positive" and "negative."


# Analyze a single string to obtain a binary response (positive / negative)
sentiment <- analyzeSentiment("Yeah, this was a great soccer game of the German team!")
convertToBinaryResponse(sentiment)$SentimentGI
#> [1] positive
#> Levels: negative positive

Small example

The following demonstrates some of the functionality provided by SentimentAnalysis. It also shows its visualization and evaluation capabilities.

# Create a vector of strings
documents <- c("Wow, I really like the new light sabers!",
               "That book was excellent.",
               "R is a fantastic language.",
               "The service in this restaurant was miserable.",
               "This is neither positive or negative.",
               "The waiter forget about my a dessert -- what a poor service!")

# Analyze sentiment
sentiment <- analyzeSentiment(documents)

# Extract dictionary-based sentiment according to the QDAP dictionary
sentiment$SentimentQDAP
#> [1]  0.3333333  0.5000000  0.5000000 -0.3333333  0.0000000 -0.4000000

# View sentiment direction (i.e. positive, neutral and negative)
convertToDirection(sentiment$SentimentQDAP)
#> [1] positive positive positive negative neutral  negative
#> Levels: negative neutral positive

response <- c(+1, +1, +1, -1, 0, -1)

compareToResponse(sentiment, response)
#> Warning in cor(sentiment, response): the standard deviation is zero
#> Warning in cor(x, y): the standard deviation is zero

#> Warning in cor(x, y): the standard deviation is zero
#> Warning in cor(sentiment, response): the standard deviation is zero
#>                              WordCount SentimentGI NegativityGI
#> cor                        -0.18569534   0.9900115  -0.99748901
#> cor.t.statistic            -0.37796447  14.0440465 -28.16913204
#> cor.p.value                -0.37796447  14.0440465 -28.16913204
#> lm.t.value                 -0.37796447  14.0440465 -28.16913204
#> r.squared                   0.03448276   0.9801228   0.99498433
#> RMSE                        3.82970843   0.4501029   1.18665418
#> MAE                         3.33333333   0.4000000   1.10000000
#> Accuracy                    0.66666667   1.0000000   0.66666667
#> Precision                          NaN   1.0000000          NaN
#> Sensitivity                 0.00000000   1.0000000   0.00000000
#> Specificity                 1.00000000   1.0000000   1.00000000
#> F1                          0.00000000   0.5000000   0.00000000
#> BalancedAccuracy            0.50000000   1.0000000   0.50000000
#> avg.sentiment.pos.response  3.25000000   0.3333333   0.08333333
#> avg.sentiment.neg.response  4.00000000  -0.6333333   0.63333333
#>                            PositivityGI SentimentHE NegativityHE
#> cor                           0.9429542   0.4152274 -0.083045480
#> cor.t.statistic               5.6647055   0.9128709 -0.166666667
#> cor.p.value                   5.6647055   0.9128709 -0.166666667
#> lm.t.value                    5.6647055   0.9128709 -0.166666667
#> r.squared                     0.8891626   0.1724138  0.006896552
#> RMSE                          0.7136240   0.8416254  0.922958207
#> MAE                           0.6666667   0.7500000  0.888888889
#> Accuracy                      0.6666667   0.6666667  0.666666667
#> Precision                           NaN         NaN          NaN
#> Sensitivity                   0.0000000   0.0000000  0.000000000
#> Specificity                   1.0000000   1.0000000  1.000000000
#> F1                            0.0000000   0.0000000  0.000000000
#> BalancedAccuracy              0.5000000   0.5000000  0.500000000
#> avg.sentiment.pos.response    0.4166667   0.1250000  0.083333333
#> avg.sentiment.neg.response    0.0000000   0.0000000  0.000000000
#>                            PositivityHE SentimentLM NegativityLM
#> cor                           0.3315938   0.7370455  -0.40804713
#> cor.t.statistic               0.7029595   2.1811142  -0.89389841
#> cor.p.value                   0.7029595   2.1811142  -0.89389841
#> lm.t.value                    0.7029595   2.1811142  -0.89389841
#> r.squared                     0.1099545   0.5432361   0.16650246
#> RMSE                          0.8525561   0.7234178   0.96186547
#> MAE                           0.8055556   0.6333333   0.92222222
#> Accuracy                      0.6666667   0.8333333   0.66666667
#> Precision                           NaN   1.0000000          NaN
#> Sensitivity                   0.0000000   0.5000000   0.00000000
#> Specificity                   1.0000000   1.0000000   1.00000000
#> F1                            0.0000000   0.3333333   0.00000000
#> BalancedAccuracy              0.5000000   0.7500000   0.50000000
#> avg.sentiment.pos.response    0.2083333   0.2500000   0.08333333
#> avg.sentiment.neg.response    0.0000000  -0.1000000   0.10000000
#>                            PositivityLM RatioUncertaintyLM SentimentQDAP
#> cor                           0.6305283                 NA     0.9865356
#> cor.t.statistic               1.6247248                 NA    12.0642877
#> cor.p.value                   1.6247248                 NA    12.0642877
#> lm.t.value                    1.6247248                 NA    12.0642877
#> r.squared                     0.3975659                 NA     0.9732526
#> RMSE                          0.7757911          0.9128709     0.5398902
#> MAE                           0.7222222          0.8333333     0.4888889
#> Accuracy                      0.6666667          0.6666667     1.0000000
#> Precision                           NaN                NaN     1.0000000
#> Sensitivity                   0.0000000          0.0000000     1.0000000
#> Specificity                   1.0000000          1.0000000     1.0000000
#> F1                            0.0000000          0.0000000     0.5000000
#> BalancedAccuracy              0.5000000          0.5000000     1.0000000
#> avg.sentiment.pos.response    0.3333333          0.0000000     0.3333333
#> avg.sentiment.neg.response    0.0000000          0.0000000    -0.3666667
#>                            NegativityQDAP PositivityQDAP
#> cor                           -0.94433955      0.9429542
#> cor.t.statistic               -5.74114834      5.6647055
#> cor.p.value                   -5.74114834      5.6647055
#> lm.t.value                    -5.74114834      5.6647055
#> r.squared                      0.89177719      0.8891626
#> RMSE                           1.06840137      0.7136240
#> MAE                            1.01111111      0.6666667
#> Accuracy                       0.66666667      0.6666667
#> Precision                             NaN            NaN
#> Sensitivity                    0.00000000      0.0000000
#> Specificity                    1.00000000      1.0000000
#> F1                             0.00000000      0.0000000
#> BalancedAccuracy               0.50000000      0.5000000
#> avg.sentiment.pos.response     0.08333333      0.4166667
#> avg.sentiment.neg.response     0.36666667      0.0000000

# Optional visualization: plotSentimentResponse(sentiment$SentimentQDAP, response)

Dictionary generation

Research in finance and social sciences nowadays utilizes content analysis to understand human decisions in the face of textual materials. While content analysis has received great traction lately, the available tools are not yet living up to the needs of researchers. This package implements a novel approach named "**dictionary generation" to study tone, sentiment and reception of textual materials.

The approach utilizes LASSO regularization to extract words from documents that statistically feature a positive and negative polarity. This immediately reveals manifold implications for practitioners, finance research and social sciences: researchers can use R to extract text components that are relevant for readers and test their hypothesis based on these.

  • Proellochs, Feuerriegel and Neumann (2018): Statistical inferences for polarity identification in natural language, PLOS ONE 13(12):e0209323. DOI: 10.1371/journal.pone.0209323

License

SentimentAnalysis is released under the MIT License

Copyright (c) 2021 Stefan Feuerriegel & Nicolas Pröllochs

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install.packages('SentimentAnalysis')

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1.3-4

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Last Published

February 18th, 2021

Functions in SentimentAnalysis (1.3-4)

SentimentAnalysis

SentimentAnalysis: A package for analyzing sentiment of texts
compareToResponse

Compare sentiment values to existing response variable
DictionaryLM

Dictionary with opinionated words from Loughran-McDonald Financial dictionary
loadDictionaryQDAP

Loads polarity words from qdap package into object
lassoEstimation

Lasso estimation
loadDictionaryHE

Loads Henry's finance-specific dictionary into object
lmEstimation

Ordinary least squares estimation
analyzeSentiment

Sentiment analysis
loadDictionaryGI

Loads Harvard-IV dictionary into object
compareDictionaries

Compares two dictionaries
numEntries

Number of words in dictionary
numNegativeEntries

Number of negative words in dictionary
generateDictionary

Generates dictionary of decisive terms
lookupEstimationMethod

Estimation method
ruleRatio

Ratio of dictionary words
loadImdb

Retrieves IMDb dataset
glmEstimation

Estimation via generalized least squares
read

Read dictionary from text file
ngram_tokenize

N-gram tokenizer
print.SentimentDictionaryWordlist

Output content of sentiment dictionary
SentimentDictionaryWordlist

Create a sentiment dictionary consisting of a simple wordlist
SentimentDictionaryWeighted

Create a sentiment dictionary of words linked to a score
loadDictionaryLM

Loads Loughran-McDonald dictionary into object
loadDictionaryLM_Uncertainty

Loads uncertainty words from Loughran-McDonald into object
convertToBinaryResponse

Convert continuous sentiment to direction
plotSentiment

Line plot with sentiment scores
numPositiveEntries

Number of positive words in dictionary
preprocessCorpus

Default preprocessing of corpus
predict.SentimentDictionaryWeighted

Prediction for given dictionary
plotSentimentResponse

Scatterplot with trend line between sentiment and response
plot.SentimentDictionaryWeighted

KDE plot of estimated coefficients
ruleSentiment

Sentiment score
ruleSentimentPolarity

Sentiment polarity score
transformIntoCorpus

Transforms the input into a Corpus object
toDocumentTermMatrix

Default preprocessing of corpus and conversion to document-term matrix
countWords

Count words
ridgeEstimation

Ridge estimation
rulePositivity

Ratio of positive words
ruleNegativity

Ratio of negative words
ruleLinearModel

Sentiment based on linear model
spikeslabEstimation

Spike-and-slab estimation
convertToDirection

Convert continuous sentiment to direction
summary.SentimentDictionaryWordlist

Output summary information on sentiment dictionary
ruleWordCount

Counts word frequencies
write

Write dictionary to text file
DictionaryHE

Dictionary with opinionated words from Henry's Financial dictionary
DictionaryGI

Dictionary with opinionated words from the Harvard-IV dictionary as used in the General Inquirer software
SentimentDictionaryBinary

Create a sentiment dictionary of positive and negative words
SentimentDictionary

Create new sentiment dictionary based on input
enetEstimation

Elastic net estimation
extractWords

Extract words from dictionary