{ "_id": "6a155d0cacfb0bcc41d5f911", "Package": "SentimentAnalysis", "Type": "Package", "Title": "Dictionary-Based Sentiment Analysis", "Version": "1.3-5", "Date": "2023-08-23", "Authors@R": "c(person(\"Nicolas\", \"Proellochs\", email=\"nicolas@nproellochs.com\",\nrole=c(\"aut\", \"cre\")),\nperson(\"Stefan\", \"Feuerriegel\", email=\"sentiment@sfeuerriegel.com\",\nrole=c(\"aut\")))", "Description": "Performs a sentiment analysis of textual contents in R.\nThis implementation utilizes various existing dictionaries,\nsuch as Harvard IV, or finance-specific dictionaries.\nFurthermore, it can also create customized dictionaries. The\nlatter uses LASSO regularization as a statistical approach to\nselect relevant terms based on an exogenous response variable.", "License": "MIT + file LICENSE", "URL": "https://github.com/sfeuerriegel/SentimentAnalysis", "BugReports": "https://github.com/sfeuerriegel/SentimentAnalysis/issues", "LazyData": "true", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "libxml2-dev", "Repository": "https://sfeuerriegel.r-universe.dev", "Date/Publication": "2023-08-23 19:56:31 UTC", "RemoteUrl": "https://github.com/sfeuerriegel/sentimentanalysis", "RemoteRef": "HEAD", "RemoteSha": "279194dc86d1764be9181c12273087d8de8d75d6", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 08:38:19 UTC", "User": "root" }, "Author": "Nicolas Proellochs [aut, cre],\nStefan Feuerriegel [aut]", "Maintainer": "Nicolas Proellochs ", "MD5sum": "04a9b47f7509b303ec221ba94be82f47", "_user": "sfeuerriegel", "_type": "src", "_file": 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"analyzeSentiment", "compareDictionaries", "compareToResponse", "convertToBinaryResponse", "convertToDirection", "countWords", "enetEstimation", "extractWords", "generateDictionary", "glmEstimation", "lassoEstimation", "lmEstimation", "loadDictionaryGI", "loadDictionaryHE", "loadDictionaryLM", "loadDictionaryLM_Uncertainty", "loadDictionaryQDAP", "loadImdb", "lookupEstimationMethod", "ngram_tokenize", "numEntries", "numNegativeEntries", "numPositiveEntries", "plotSentiment", "plotSentimentResponse", "preprocessCorpus", "read", "ridgeEstimation", "ruleLinearModel", "ruleNegativity", "rulePositivity", "ruleRatio", "ruleSentiment", "ruleSentimentPolarity", "ruleWordCount", "SentimentDictionary", "SentimentDictionaryBinary", "SentimentDictionaryWeighted", "SentimentDictionaryWordlist", "spikeslabEstimation", "toDocumentTermMatrix", "transformIntoCorpus", "write" ], "_datasets": [ { "name": "DictionaryGI", "title": "Dictionary with opinionated words from the Harvard-IV dictionary as used in the General Inquirer software", "object": "DictionaryGI", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "DictionaryHE", "title": "Dictionary with opinionated words from Henry's Financial dictionary", "object": "DictionaryHE", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "DictionaryLM", "title": "Dictionary with opinionated words from Loughran-McDonald Financial dictionary", "object": "DictionaryLM", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "analyzeSentiment", "title": "Sentiment analysis", "topics": [ "analyzeSentiment", "analyzeSentiment.character", "analyzeSentiment.Corpus", "analyzeSentiment.data.frame", "analyzeSentiment.DocumentTermMatrix", "analyzeSentiment.TermDocumentMatrix" ] }, { "page": "compareDictionaries", "title": "Compares two dictionaries", "topics": [ "compareDictionaries" ] }, { "page": "compareToResponse", "title": "Compare sentiment values to existing response variable", "topics": [ "compareToResponse", "compareToResponse.data.frame", "compareToResponse.factor", "compareToResponse.integer", "compareToResponse.logical", "compareToResponse.numeric" ] }, { "page": "convertToBinaryResponse", "title": "Convert continuous sentiment to direction", "topics": [ "convertToBinaryResponse" ] }, { "page": "convertToDirection", "title": "Convert continuous sentiment to direction", "topics": [ "convertToDirection" ] }, { "page": "countWords", "title": "Count words", "topics": [ "countWords", "countWords.character", "countWords.Corpus", "countWords.data.frame", "countWords.DocumentTermMatrix", "countWords.TermDocumentMatrix" ] }, { "page": "DictionaryGI", "title": "Dictionary with opinionated words from the Harvard-IV dictionary as used in the General Inquirer software", "topics": [ "DictionaryGI" ] }, { "page": "DictionaryHE", "title": "Dictionary with opinionated words from Henry's Financial dictionary", "topics": [ "DictionaryHE" ] }, { "page": "DictionaryLM", "title": "Dictionary with opinionated words from Loughran-McDonald Financial dictionary", "topics": [ "DictionaryLM" ] }, { "page": "enetEstimation", "title": "Elastic net estimation", "topics": [ "enetEstimation" ] }, { "page": "extractWords", "title": "Extract words from dictionary", "topics": [ "extractWords" ] }, { "page": "generateDictionary", "title": "Generates dictionary of decisive terms", "topics": [ "generateDictionary", "generateDictionary.character", "generateDictionary.Corpus", "generateDictionary.data.frame", "generateDictionary.DocumentTermMatrix", "generateDictionary.TermDocumentMatrix" ] }, { "page": "glmEstimation", "title": "Estimation via generalized least squares", "topics": [ "glmEstimation" ] }, { "page": "lassoEstimation", "title": "Lasso estimation", "topics": [ "lassoEstimation" ] }, { "page": "lmEstimation", "title": "Ordinary least squares estimation", "topics": [ "lmEstimation" ] }, { "page": "loadDictionaryGI", "title": "Loads Harvard-IV dictionary into object", "topics": [ "loadDictionaryGI" ] }, { "page": "loadDictionaryHE", "title": "Loads Henry's finance-specific dictionary into object", "topics": [ "loadDictionaryHE" ] }, { "page": "loadDictionaryLM", "title": "Loads Loughran-McDonald dictionary into object", "topics": [ "loadDictionaryLM" ] }, { "page": "loadDictionaryLM_Uncertainty", "title": "Loads uncertainty words from Loughran-McDonald into object", "topics": [ "loadDictionaryLM_Uncertainty" ] }, { "page": "loadDictionaryQDAP", "title": "Loads polarity words from qdap package into object", "topics": [ "loadDictionaryQDAP" ] }, { "page": "loadImdb", "title": "Retrieves IMDb dataset", "topics": [ "loadImdb" ] }, { "page": "lookupEstimationMethod", "title": "Estimation method", "topics": [ "lookupEstimationMethod" ] }, { "page": "ngram_tokenize", "title": "N-gram tokenizer", "topics": [ "ngram_tokenize" ] }, { "page": "numEntries", "title": "Number of words in dictionary", "topics": [ "numEntries" ] }, { "page": "numNegativeEntries", "title": "Number of negative words in dictionary", "topics": [ "numNegativeEntries" ] }, { "page": "numPositiveEntries", "title": "Number of positive words in dictionary", "topics": [ "numPositiveEntries" ] }, { "page": "plot.SentimentDictionaryWeighted", "title": "KDE plot of estimated coefficients", "topics": [ "plot.SentimentDictionaryWeighted" ] }, { "page": "plotSentiment", "title": "Line plot with sentiment scores", "topics": [ "plotSentiment" ] }, { "page": "plotSentimentResponse", "title": "Scatterplot with trend line between sentiment and response", "topics": [ "plotSentimentResponse" ] }, { "page": "predict.SentimentDictionaryWeighted", "title": "Prediction for given dictionary", "topics": [ "predict.SentimentDictionaryWeighted" ] }, { "page": "preprocessCorpus", "title": "Default preprocessing of corpus", "topics": [ "preprocessCorpus" ] }, { "page": "print", "title": "Output content of sentiment dictionary", "topics": [ "print.SentimentDictionaryBinary", "print.SentimentDictionaryWeighted", "print.SentimentDictionaryWordlist" ] }, { "page": "read", "title": "Read dictionary from text file", "topics": [ "read" ] }, { "page": "ridgeEstimation", "title": "Ridge estimation", "topics": [ "ridgeEstimation" ] }, { "page": "ruleLinearModel", "title": "Sentiment based on linear model", "topics": [ "ruleLinearModel" ] }, { "page": "ruleNegativity", "title": "Ratio of negative words", "topics": [ "ruleNegativity" ] }, { "page": "rulePositivity", "title": "Ratio of positive words", "topics": [ "rulePositivity" ] }, { "page": "ruleRatio", "title": "Ratio of dictionary words", "topics": [ "ruleRatio" ] }, { "page": "ruleSentiment", "title": "Sentiment score", "topics": [ "ruleSentiment" ] }, { "page": "ruleSentimentPolarity", "title": "Sentiment polarity score", "topics": [ "ruleSentimentPolarity" ] }, { "page": "ruleWordCount", "title": "Counts word frequencies", "topics": [ "ruleWordCount" ] }, { "page": "SentimentDictionary", "title": "Create new sentiment dictionary based on input", "topics": [ "SentimentDictionary" ] }, { "page": "SentimentDictionaryBinary", "title": "Create a sentiment dictionary of positive and negative words", "topics": [ "SentimentDictionaryBinary" ] }, { "page": "SentimentDictionaryWeighted", "title": "Create a sentiment dictionary of words linked to a score", "topics": [ "SentimentDictionaryWeighted" ] }, { "page": "SentimentDictionaryWordlist", "title": "Create a sentiment dictionary consisting of a simple wordlist", "topics": [ "SentimentDictionaryWordlist" ] }, { "page": "spikeslabEstimation", "title": "Spike-and-slab estimation", "topics": [ "spikeslabEstimation" ] }, { "page": "summary", "title": "Output summary information on sentiment dictionary", "topics": [ "summary.SentimentDictionaryBinary", "summary.SentimentDictionaryWeighted", "summary.SentimentDictionaryWordlist" ] }, { "page": "toDocumentTermMatrix", "title": "Default preprocessing of corpus and conversion to document-term matrix", "topics": [ "toDocumentTermMatrix" ] }, { "page": "transformIntoCorpus", "title": "Transforms the input into a Corpus object", "topics": [ "transformIntoCorpus" ] }, { "page": "write", "title": "Write dictionary to text file", "topics": [ "write", "write.SentimentDictionaryBinary", "write.SentimentDictionaryWeighted", "write.SentimentDictionaryWordlist" ] } ], "_readme": "https://github.com/sfeuerriegel/sentimentanalysis/raw/HEAD/README.md", "_rundeps": [ "BH", "cli", "codetools", "cpp11", "farver", "foreach", "ggplot2", "glmnet", "glue", "gtable", "isoband", "iterators", "labeling", "lars", "lattice", "lifecycle", "Matrix", "moments", "ngramrr", "NLP", "qdapDictionaries", "R6", "randomForest", "RColorBrewer", "Rcpp", "RcppEigen", "rlang", "S7", "scales", "shape", "slam", "spikeslab", "stringdist", "survival", "tau", "tm", "vctrs", "viridisLite", "withr", "xml2" ], "_vignettes": [ { "source": "SentimentAnalysis.Rmd", "filename": "SentimentAnalysis.html", "title": "SentimentAnalysis Vignette", "author": "Stefan Feuerriegel, Nicolas Proellochs", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Applications in research", "Applications in practice", "Methods for sentiment analysis", "Setup of the SentimentAnalysis package", "Brief demonstration", "Functionality", "Interface", "Vector of strings", "Document-term matrix", "Corpus object", "Built-in dictionaries", "Dictionary functions", "SentimentDictionaryWordlist", "SentimentDictionaryBinary", "SentimentDictionaryWeighted", "Dictionary generation", "Performance evaluation", "Configuration of preprocessing", "Performance optimization", "Language support and extensibility", "Worked examples", "Word couting", "Outlook", "License", "References" ], "created": "2016-06-18 16:05:46", "modified": "2023-08-23 19:56:31", "commits": 19 } ], "_score": 8.533873695287102, "_indexed": true, "_nocasepkg": "sentimentanalysis", "_universes": [ "sfeuerriegel" ], 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