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SentimentAnalysis (version 1.3-4)

generateDictionary: Generates dictionary of decisive terms

Description

Routine applies method for dictionary generation (LASSO, ridge regularization, elastic net, ordinary least squares, generalized linear model or spike-and-slab regression) to the document-term matrix in order to extract decisive terms that have a statistically significant impact on the response variable.

Usage

generateDictionary(
  x,
  response,
  language = "english",
  modelType = "lasso",
  filterTerms = NULL,
  control = list(),
  minWordLength = 3,
  sparsity = 0.9,
  weighting = function(x) tm::weightTfIdf(x, normalize = FALSE),
  ...
)

# S3 method for Corpus generateDictionary( x, response, language = "english", modelType = "lasso", filterTerms = NULL, control = list(), minWordLength = 3, sparsity = 0.9, weighting = function(x) tm::weightTfIdf(x, normalize = FALSE), ... )

# S3 method for character generateDictionary( x, response, language = "english", modelType = "lasso", filterTerms = NULL, control = list(), minWordLength = 3, sparsity = 0.9, weighting = function(x) tm::weightTfIdf(x, normalize = FALSE), ... )

# S3 method for data.frame generateDictionary( x, response, language = "english", modelType = "lasso", filterTerms = NULL, control = list(), minWordLength = 3, sparsity = 0.9, weighting = function(x) tm::weightTfIdf(x, normalize = FALSE), ... )

# S3 method for TermDocumentMatrix generateDictionary( x, response, language = "english", modelType = "lasso", filterTerms = NULL, control = list(), minWordLength = 3, sparsity = 0.9, weighting = function(x) tm::weightTfIdf(x, normalize = FALSE), ... )

# S3 method for DocumentTermMatrix generateDictionary( x, response, language = "english", modelType = "lasso", filterTerms = NULL, control = list(), minWordLength = 3, sparsity = 0.9, weighting = function(x) tm::weightTfIdf(x, normalize = FALSE), ... )

Value

Result is a matrix which sentiment values for each document across all defined rules

Arguments

x

A vector of characters, a data.frame, an object of type Corpus, TermDocumentMatrix or DocumentTermMatrix.

response

Response variable including the given gold standard.

language

Language used for preprocessing operations (default: English).

modelType

A string denoting the estimation method. Allowed values are lasso, ridge, enet, lm or glm or spikeslab.

filterTerms

Optional vector of strings (default: NULL) to filter terms that are used for dictionary generation.

control

(optional) A list of parameters defining the model used for dictionary generation.

If modelType=lasso is selected, individual parameters are as follows:

  • "s" Value of the parameter lambda at which the LASSO is evaluated. Default is s="lambda.1se" which takes the calculated minimum value for \(\lambda\) and then subtracts one standard error in order to avoid overfitting. This often results in a better performance than using the minimum value itself given by lambda="lambda.min".

  • "family" Distribution for response variable. Default is family="gaussian". For non-negative counts, use family="poisson". For binary variables family="binomial". See glmnet for further details.

  • "grouped" Determines whether grouped LASSO is used (with default FALSE).

If modelType=ridge is selected, individual parameters are as follows:

  • "s" Value of the parameter lambda at which the ridge is evaluated. Default is s="lambda.1se" which takes the calculated minimum value for \(\lambda\) and then subtracts one standard error in order to avoid overfitting. This often results in a better performance than using the minimum value itself given by lambda="lambda.min".

  • "family" Distribution for response variable. Default is family="gaussian". For non-negative counts, use family="poisson". For binary variables family="binomial". See glmnet for further details.

  • "grouped" Determines whether grouped function is used (with default FALSE).

If modelType=enet is selected, individual parameters are as follows:

  • "alpha" Abstraction parameter for switching between LASSO (with alpha=1) and ridge regression (alpha=0). Default is alpha=0.5. Recommended option is to test different values between 0 and 1.

  • "s" Value of the parameter lambda at which the elastic net is evaluated. Default is s="lambda.1se" which takes the calculated minimum value for \(\lambda\) and then subtracts one standard error in order to avoid overfitting. This often results in a better performance than using the minimum value itself given by lambda="lambda.min".

  • "family" Distribution for response variable. Default is family="gaussian". For non-negative counts, use family="poisson". For binary variables family="binomial". See glmnet for further details.

  • "grouped" Determines whether grouped function is used (with default FALSE).

If modelType=lm is selected, no parameters are passed on.

If modelType=glm is selected, individual parameters are as follows:

  • "family" Distribution for response variable. Default is family="gaussian". For non-negative counts, use family="poisson". For binary variables family="binomial". See glm for further details.

If modelType=spikeslab is selected, individual parameters are as follows:

  • "n.iter1" Number of burn-in Gibbs sampled values (i.e., discarded values). Default is 500.

  • "n.iter2" Number of Gibbs sampled values, following burn-in. Default is 500.

minWordLength

Removes words given a specific minimum length (default: 3). This preprocessing is applied when the input is a character vector or a corpus and the document-term matrix is generated inside the routine.

sparsity

A numeric for removing sparse terms in the document-term matrix. The argument sparsity specifies the maximal allowed sparsity. Default is sparsity=0.9, however, this is only applied when the document-term matrix is calculated inside the routine.

weighting

Weights a document-term matrix by e.g. term frequency - inverse document frequency (default). Other variants can be used from DocumentTermMatrix.

...

Additional parameters passed to function for e.g. preprocessing or glmnet.

References

Pr\"ollochs and Feuerriegel (2018). Statistical inferences for Polarity Identification in Natural Language, PloS One 13(12).

See Also

analyzeSentiment, predict.SentimentDictionaryWeighted, plot.SentimentDictionaryWeighted and compareToResponse for advanced evaluations

Examples

Run this code
# Create a vector of strings
documents <- c("This is a good thing!",
               "This is a very good thing!",
               "This is okay.",
               "This is a bad thing.",
               "This is a very bad thing.")
response <- c(1, 0.5, 0, -0.5, -1)

# Generate dictionary with LASSO regularization
dictionary <- generateDictionary(documents, response)

# Show dictionary
dictionary
summary(dictionary)
plot(dictionary)

# Compute in-sample performance
sentiment <- predict(dictionary, documents)
compareToResponse(sentiment, response)
plotSentimentResponse(sentiment, response)

# Generate new dictionary with spike-and-slab regression instead of LASSO regularization
library(spikeslab)
dictionary <- generateDictionary(documents, response, modelType="spikeslab")

# Generate new dictionary with tf weighting instead of tf-idf

library(tm)
dictionary <- generateDictionary(documents, response, weighting=weightTf)
sentiment <- predict(dictionary, documents)
compareToResponse(sentiment, response)

# Use instead lambda.min from the LASSO estimation
dictionary <- generateDictionary(documents, response, control=list(s="lambda.min"))
sentiment <- predict(dictionary, documents)
compareToResponse(sentiment, response)

# Use instead OLS as estimation method
dictionary <- generateDictionary(documents, response, modelType="lm")
sentiment <- predict(dictionary, documents)
sentiment

dictionary <- generateDictionary(documents, response, modelType="lm", 
                                 filterTerms = c("good", "bad"))
sentiment <- predict(dictionary, documents)
sentiment

dictionary <- generateDictionary(documents, response, modelType="lm", 
                                 filterTerms = extractWords(loadDictionaryGI()))
sentiment <- predict(dictionary, documents)
sentiment

# Generate dictionary without LASSO intercept
dictionary <- generateDictionary(documents, response, intercept=FALSE)
dictionary$intercept
 
if (FALSE) {
imdb <- loadImdb()

# Generate Dictionary
dictionary_imdb <- generateDictionary(imdb$Corpus, imdb$Rating, family="poisson")
summary(dictionary_imdb)

compareDictionaries(dictionary_imdb,
                    loadDictionaryGI())
                    
# Show estimated coefficients with Kernel Density Estimation (KDE)
plot(dictionary_imdb)
plot(dictionary_imdb) + xlim(c(-0.1, 0.1))

# Compute in-sample performance
pred_sentiment <- predict(dict_imdb, imdb$Corpus)
compareToResponse(pred_sentiment, imdb$Rating)

# Test a different sparsity parameter
dictionary_imdb <- generateDictionary(imdb$Corpus, imdb$Rating, family="poisson", sparsity=0.99)
summary(dictionary_imdb)
pred_sentiment <- predict(dict_imdb, imdb$Corpus)
compareToResponse(pred_sentiment, imdb$Rating)
}

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