{ "_id": "6a1f11d8b401979e7341db69", "Package": "quickSentiment", "Title": "A Fast and Flexible Pipeline for Text Classification", "Version": "0.3.4", "Authors@R": "person(\"Alabhya\", \"Dahal\", email = \"alabhya.dahal@gmail.com\", role = c(\"aut\", \"cre\"))", "Description": "A high-level pipeline that simplifies text classification\ninto three streamlined steps: preprocessing, model training,\nand standardized prediction. It unifies the interface for\nmultiple algorithms (including 'glmnet', 'ranger', 'xgboost',\nand 'naivebayes') and memory-efficient sparse matrix\nvectorization methods (Bag-of-Words, Term Frequency, TF-IDF,\nand Binary). Users can go from raw text to a fully evaluated\nsentiment model, complete with ROC-optimized thresholds, in\njust a few function calls. The resulting model artifact\nautomatically aligns the vocabulary of new datasets during the\nprediction phase, safely appending predicted classes and\nprobability matrices directly to the user's original dataframe\nto preserve metadata.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Language": "en-US", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-17 07:32:19 UTC", "User": "root" }, "Author": "Alabhya Dahal [aut, cre]", "Maintainer": "Alabhya Dahal ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-04-17 06:17:01 UTC", "RemoteUrl": "https://github.com/cran/quickSentiment", "RemoteRef": "HEAD", "RemoteSha": "1c621de51fa914282a57addc9a6deeb6b614130a", "MD5sum": "d4bae0fa2b24452138ab01be05ab6b12", "_user": "cran", "_type": "src", "_file": "quickSentiment_0.3.4.tar.gz", "_fileid": "ec9cdcea3983b5194b7cdf4b0b1936b58517831f1f39b1f8fa3b64ab07ade45b", "_filesize": 312146, "_sha256": 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Naive Bayes for Text Classification", "topics": [ "nb_model" ] }, { "page": "pipeline", "title": "Run a Full Text Classification Pipeline on Preprocessed Text", "topics": [ "pipeline" ] }, { "page": "plot.quickSentiment_prc", "title": "Plot Precision-Recall Curve", "topics": [ "plot.quickSentiment_prc" ] }, { "page": "plot.quickSentiment_roc", "title": "Plot ROC Curve", "topics": [ "plot.quickSentiment_roc" ] }, { "page": "pre_process", "title": "Preprocess a Vector of Text Documents", "topics": [ "pre_process" ] }, { "page": "predict_sentiment", "title": "Predict Sentiment on New Data Using a Saved Pipeline Artifact", "topics": [ "predict_sentiment" ] }, { "page": "print.quickSentiment_eval", "title": "Print quickSentiment Evaluation Results", "topics": [ "print.quickSentiment_eval" ] }, { "page": "qs_negations", "title": "Standard Negation Words for Sentiment Analysis", "topics": [ "qs_negations" ] }, { "page": "rf_model", "title": "functions/random_forest_fast.R Train a Random Forest Model using Ranger", "topics": [ "rf_model" ] }, { "page": "xgb_model", "title": "Train a Gradient Boosting Model using XGBoost", "topics": [ "xgb_model" ] } ], "_readme": "https://github.com/cran/quickSentiment/raw/HEAD/README.md", "_rundeps": [ "cli", "codetools", "cpp11", "data.table", "digest", "doParallel", "dplyr", "dtt", "english", "fastmatch", "foreach", "generics", "glmnet", "glue", "hunspell", "ISOcodes", "iterators", "jsonlite", "koRpus", "koRpus.lang.en", "lattice", "lexicon", "lifecycle", "magrittr", "Matrix", "mgsub", "naivebayes", "NLP", "pillar", "pkgconfig", "purrr", "qdapRegex", "quanteda", "R6", "ranger", "Rcpp", "RcppEigen", "rlang", "shape", "slam", "SnowballC", "stopwords", "stringi", "stringr", "survival", "sylly", "sylly.en", "syuzhet", "textclean", "textshape", "textstem", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "withr", "xgboost", "xml2", "yaml", "zoo" ], "_vignettes": [ { "source": "introduction-to-quickSentiment.Rmd", "filename": 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SETUP: LOAD LIBRARIES ---", "-------------------------------------------------------------------", "--- 2. LOAD AND PREPARE TRAINING DATA ---", "--- 3. PREPROCESS THE TEXT ---", "Use the pre_process() function from our package to clean the raw text.", "This step is done externally to the main pipeline, allowing you to reuse", "the same cleaned text for multiple different models or analyses in the future.", "--- 4. RUN THE MAIN TRAINING PIPELINE ---", "This is the core of the package. We call the main pipeline() function", "to handle the train/test split, vectorization, model training, and evaluation.", "===================================================================", "--- 5. EVALUATE THE RESULTS", "Get the AUC, ROC and Accuracy at Decile Threshold", "--- 6. PREDICTION ON NEW, UNSEEN DATA ---", "The training is complete. The 'result' object now contains our trained", "model and all the necessary \"artifacts\" for prediction." ], "created": "2026-02-06 13:30:02", "modified": "2026-04-17 06:17:01", "commits": 3 } ], "_score": 3.4771212547196626, "_indexed": true, "_nocasepkg": "quicksentiment", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.3.4", "date": "2026-05-17T07:34:37.000Z", "distro": "noble", "commit": "1c621de51fa914282a57addc9a6deeb6b614130a", "fileid": "d3ef504d617c9960bfc9851f675528fc6501a3b24d866ef15c2904540487c1af", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984667118" }, { "r": "4.6.0", "os": "linux", "version": "0.3.4", "date": "2026-05-17T07:34:36.000Z", "distro": "noble", "commit": "1c621de51fa914282a57addc9a6deeb6b614130a", "fileid": "9ce275e7bb74dfeba2fdfe99e8e656e2875cde969d1cca819cc16da5807ba560", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984667118" }, { "r": "4.6.0", "os": "wasm", "version": "0.3.4", "date": "2026-06-02T17:24:17.000Z", "commit": "1c621de51fa914282a57addc9a6deeb6b614130a", "fileid": "5a035a7dc69417194559b8a1c669ac14652f8e2ff15721dbb4bd6d4e775e4dc2", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984667118" } ] }