{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-textblob","slug":"pypi-textblob","name":"textblob","type":"repo","url":"https://pypi.org/project/textblob/","page_url":"https://unfragile.ai/pypi-textblob","categories":["text-writing"],"tags":["textblob","nlp","linguistics","nltk","pattern"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-textblob__cap_0","uri":"capability://data.processing.analysis.sentence.level.tokenization.with.boundary.detection","name":"sentence-level tokenization with boundary detection","description":"Breaks text into individual sentences using a pluggable SentenceTokenizer component that handles edge cases like abbreviations, ellipses, and decimal points. The tokenizer uses pattern-based rules and optional NLTK integration to identify sentence boundaries without requiring external API calls, enabling offline processing of large text volumes.","intents":["I need to split a document into sentences for per-sentence sentiment analysis","I want to process each sentence independently while preserving document structure","I need to handle edge cases like 'Dr. Smith' and 'U.S.A.' without false splits"],"best_for":["NLP developers building text processing pipelines","Teams processing documents offline without cloud dependencies","Applications requiring fine-grained text segmentation"],"limitations":["Pattern-based approach may struggle with non-English languages or domain-specific abbreviations","No machine learning-based boundary detection — relies on heuristics","Performance degrades on very long documents (>100K words) due to regex operations"],"requires":["Python 3.6+","NLTK data files (optional, for enhanced tokenization)"],"input_types":["plain text string","multi-paragraph text"],"output_types":["list of Sentence objects","list of sentence strings"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_1","uri":"capability://data.processing.analysis.word.level.tokenization.with.morphological.awareness","name":"word-level tokenization with morphological awareness","description":"Tokenizes text into individual words using a WordTokenizer component that preserves contractions, handles punctuation attachment, and creates Word objects with lazy-loaded morphological properties. The architecture defers expensive operations like lemmatization and inflection until explicitly accessed, reducing memory overhead for large texts.","intents":["I need to extract individual words while preserving contractions like 'don't' and 'it's'","I want word-level access to lemmatization and inflection without upfront computation","I need to process word frequency analysis with morphological normalization"],"best_for":["Text analysis applications requiring word-level granularity","Developers building custom NLP pipelines with selective morphological processing","Memory-constrained environments processing large documents"],"limitations":["Contraction handling is English-centric and may not work for other languages","Punctuation attachment logic uses simple heuristics, not grammar-aware parsing","No built-in support for multi-word expressions or compound words in non-Germanic languages"],"requires":["Python 3.6+","Pattern library (for inflection/lemmatization features)"],"input_types":["plain text string","sentence string"],"output_types":["WordList object (iterable of Word objects)","list of word strings"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_10","uri":"capability://data.processing.analysis.hierarchical.text.representation.with.nested.object.access","name":"hierarchical text representation with nested object access","description":"Provides a hierarchical object model where TextBlob contains Sentences, which contain Words, enabling intuitive navigation and analysis at multiple granularities. Users can access text at document, sentence, or word level through nested object properties, with each level providing relevant methods (e.g., .sentiment on TextBlob/Sentence, .lemma on Word). The architecture maintains bidirectional references between levels, enabling context-aware analysis.","intents":["I need to analyze text at multiple levels (document, sentence, word) without manual parsing","I want to access sentence-level sentiment while maintaining document context","I need to iterate over words within sentences while preserving sentence boundaries"],"best_for":["Multi-level text analysis applications","Developers building hierarchical NLP pipelines","Applications requiring context-aware word or sentence analysis"],"limitations":["Nested object creation adds memory overhead for large documents","No built-in support for sub-sentence spans (e.g., noun phrases as first-class objects)","Bidirectional references can cause circular dependencies if not carefully managed","No support for overlapping or discontinuous text spans"],"requires":["Python 3.6+","Understanding of object-oriented design and nested data structures"],"input_types":["plain text string"],"output_types":["TextBlob object containing Sentence objects containing Word objects"],"categories":["data-processing-analysis","architecture-pattern"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_2","uri":"capability://data.processing.analysis.part.of.speech.tagging.with.pluggable.tagger.backends","name":"part-of-speech tagging with pluggable tagger backends","description":"Assigns grammatical parts of speech (noun, verb, adjective, etc.) to each word using a pluggable POS tagger component with multiple implementations: NLTKTagger (using NLTK's averaged perceptron model) and PatternTagger (using Pattern library's rules). The architecture allows runtime selection of taggers and custom implementations via dependency injection, enabling trade-offs between accuracy and speed.","intents":["I need to identify nouns, verbs, and adjectives in text for semantic analysis","I want to use a fast rule-based tagger for real-time processing or a statistical tagger for higher accuracy","I need to extract noun phrases based on POS tag patterns"],"best_for":["NLP developers building grammar-aware text processing","Applications requiring real-time POS tagging with configurable accuracy/speed trade-offs","Teams needing custom POS taggers for domain-specific vocabularies"],"limitations":["NLTK tagger accuracy ~96% on standard English; degrades on informal text, slang, or non-English","Pattern tagger is rule-based and less accurate (~90%) but faster for real-time use","No support for fine-grained POS tags (e.g., distinguishing past vs. present tense verbs)","Tagger selection is global per TextBlob instance; cannot mix taggers within a single document"],"requires":["Python 3.6+","NLTK 3.0+ with averaged_perceptron_tagger data (for NLTKTagger)","Pattern library (for PatternTagger)"],"input_types":["Sentence object with tokenized words","list of word strings"],"output_types":["list of (word, POS_tag) tuples","Word objects with .pos_tag attribute"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_3","uri":"capability://data.processing.analysis.noun.phrase.extraction.with.pattern.based.and.statistical.methods","name":"noun phrase extraction with pattern-based and statistical methods","description":"Extracts noun phrases (multi-word noun groups like 'the quick brown fox') from sentences using pluggable NP extractor components: FastNPExtractor (pattern-based using POS tag sequences) and ConllExtractor (statistical model trained on CoNLL data). The extractors operate on POS-tagged text and identify contiguous noun phrase chunks based on grammar patterns or learned models.","intents":["I need to extract key noun phrases from documents for topic modeling or keyword extraction","I want to identify entities and concepts represented as multi-word noun phrases","I need to choose between fast pattern-based extraction and more accurate statistical extraction"],"best_for":["Information extraction and keyword analysis applications","Teams building topic modeling or document summarization systems","Developers needing configurable NP extraction with accuracy/speed trade-offs"],"limitations":["FastNPExtractor uses simple POS patterns and misses complex nested noun phrases","ConllExtractor requires pre-trained CoNLL model data and is slower (~50-100ms per sentence)","Both extractors are English-centric and perform poorly on other languages","No support for discontinuous noun phrases (e.g., 'the cat ... that sat on the mat')","Accuracy degrades on informal text, social media, or domain-specific jargon"],"requires":["Python 3.6+","NLTK with POS tagger data","CoNLL model data (for ConllExtractor)"],"input_types":["Sentence object with POS tags","list of (word, POS_tag) tuples"],"output_types":["list of noun phrase strings","list of WordList objects representing noun phrases"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_4","uri":"capability://data.processing.analysis.sentiment.analysis.with.polarity.and.subjectivity.scoring","name":"sentiment analysis with polarity and subjectivity scoring","description":"Analyzes the emotional tone of text by computing two independent scores: polarity (ranging from -1.0 for negative to +1.0 for positive) and subjectivity (ranging from 0.0 for objective to 1.0 for subjective). The implementation uses a lexicon-based approach backed by the Pattern library, which maintains a dictionary of words with pre-computed sentiment scores and aggregates them across the text with optional intensity modifiers (negation, intensifiers).","intents":["I need to classify text as positive, negative, or neutral for sentiment-based filtering or monitoring","I want to measure both sentiment polarity and subjectivity to distinguish opinions from facts","I need fast, offline sentiment analysis without API calls or model downloads"],"best_for":["Social media monitoring and brand sentiment analysis","Customer feedback analysis and review classification","Real-time sentiment detection in resource-constrained environments"],"limitations":["Lexicon-based approach struggles with sarcasm, irony, and context-dependent sentiment","No support for aspect-based sentiment (e.g., 'great food but terrible service')","Accuracy ~70-75% on standard benchmarks; significantly lower on informal text or domain-specific language","Negation handling is simplistic (flips sign of next word) and fails on complex negation patterns","No support for multi-lingual sentiment analysis; English-only"],"requires":["Python 3.6+","Pattern library (for sentiment lexicon and scoring)"],"input_types":["plain text string","Sentence object","TextBlob object"],"output_types":["Sentiment namedtuple with (polarity, subjectivity) floats","polarity: float in range [-1.0, 1.0]","subjectivity: float in range [0.0, 1.0]"],"categories":["data-processing-analysis","nlp-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_5","uri":"capability://data.processing.analysis.word.inflection.and.morphological.transformation","name":"word inflection and morphological transformation","description":"Transforms words between different morphological forms (singular/plural, base/past tense, comparative/superlative) using rule-based morphological operations backed by the Pattern library. The Word class provides methods like .singularize(), .pluralize(), .lemmatize() that apply linguistic rules and exception dictionaries to generate correct inflected forms without requiring a full morphological analyzer.","intents":["I need to normalize words to singular form for frequency analysis and deduplication","I want to generate plural forms for text generation or data augmentation","I need to convert words to base forms for lemmatization-based text comparison"],"best_for":["Text normalization and preprocessing pipelines","Search and information retrieval systems requiring morphological matching","Text generation and augmentation applications"],"limitations":["Rule-based approach has ~95% accuracy on common English words but fails on irregular forms and neologisms","No support for verb conjugation beyond basic tense (no mood, aspect, or voice)","Language support is English-only; other languages require custom rule sets","Lemmatization is rule-based, not dictionary-backed, so accuracy varies by word frequency","No support for compound words or multi-word expressions"],"requires":["Python 3.6+","Pattern library (for morphological rules and exception dictionaries)"],"input_types":["Word object","word string"],"output_types":["transformed word string","Word object with updated form"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_6","uri":"capability://data.processing.analysis.spelling.correction.with.edit.distance.and.frequency.based.ranking","name":"spelling correction with edit distance and frequency-based ranking","description":"Corrects misspelled words by generating candidate corrections using edit distance (Levenshtein distance) and ranking them by word frequency in a reference corpus. The correct() method operates on TextBlob or Sentence objects and replaces misspelled words with the highest-ranked correction, using a pre-built frequency dictionary to prefer common words over rare ones.","intents":["I need to clean user-generated text with typos before processing","I want to correct OCR errors in scanned documents","I need to normalize informal text (e.g., 'ur' → 'your') for downstream NLP tasks"],"best_for":["Text cleaning and preprocessing for user-generated content","OCR post-processing and document digitization","Informal text normalization for social media analysis"],"limitations":["Edit distance approach generates many false positives; relies on frequency ranking which may prefer incorrect common words","No context awareness — cannot distinguish between valid homophones or domain-specific terms","Frequency dictionary is static and may not reflect modern language, slang, or technical jargon","Performance degrades on heavily misspelled text (>3 character edits) due to exponential candidate generation","No support for non-English languages without custom frequency dictionaries"],"requires":["Python 3.6+","Pattern library (for frequency dictionary and edit distance)","Pre-built word frequency corpus"],"input_types":["TextBlob object","Sentence object","plain text string"],"output_types":["corrected TextBlob object","corrected text string"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_7","uri":"capability://data.processing.analysis.text.classification.with.custom.trained.classifiers","name":"text classification with custom trained classifiers","description":"Trains and applies custom text classifiers using a Naive Bayes algorithm that learns word-feature associations from labeled training examples. The Classifier class extracts features (word presence/absence) from text and learns conditional probabilities of class labels given features, enabling domain-specific text categorization without external ML libraries.","intents":["I need to classify documents into custom categories based on labeled training data","I want to build a spam detector or sentiment classifier specific to my domain","I need to train a classifier on small datasets without heavy ML dependencies"],"best_for":["Custom text classification for domain-specific categories","Rapid prototyping of text classifiers with small labeled datasets","Applications requiring interpretable classification decisions"],"limitations":["Naive Bayes assumes feature independence, which is unrealistic for text (adjacent words are correlated)","No support for feature engineering beyond word presence/absence; no TF-IDF, n-grams, or embeddings","Accuracy typically 70-85% on standard benchmarks; significantly lower on complex classification tasks","Requires manual feature extraction and hyperparameter tuning; no automatic model selection","No built-in cross-validation, evaluation metrics, or model persistence beyond pickle","Scales poorly to large datasets (>100K examples) due to in-memory storage of all features"],"requires":["Python 3.6+","Labeled training data as list of (text, label) tuples"],"input_types":["list of (text_string, label_string) training examples","text string for classification"],"output_types":["predicted label string","confidence score (probability)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_8","uri":"capability://tool.use.integration.factory.based.configuration.and.component.injection","name":"factory-based configuration and component injection","description":"Provides a Blobber factory class that creates TextBlob instances with consistent, customizable configurations for tokenizers, POS taggers, and NP extractors. The factory pattern enables dependency injection of alternative component implementations at instantiation time, allowing users to swap between fast and accurate implementations or inject custom components without modifying TextBlob core code.","intents":["I need to create multiple TextBlob instances with consistent custom component configurations","I want to swap between fast and accurate NLP components for different use cases","I need to inject custom tokenizers, taggers, or extractors without modifying TextBlob source"],"best_for":["Applications requiring multiple TextBlob instances with different configurations","Teams building extensible NLP pipelines with pluggable components","Developers creating custom NLP components for domain-specific tasks"],"limitations":["Configuration is set at Blobber instantiation time; cannot change components per-document","No built-in configuration validation; invalid component types fail at runtime","Limited documentation on custom component interface requirements","No support for conditional component selection based on text properties (e.g., language detection)"],"requires":["Python 3.6+","Understanding of TextBlob component interfaces (Tokenizer, Tagger, NPExtractor)"],"input_types":["component class references (Tokenizer, Tagger, NPExtractor subclasses)","configuration parameters"],"output_types":["Blobber factory instance","TextBlob instances created with configured components"],"categories":["tool-use-integration","architecture-pattern"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-textblob__cap_9","uri":"capability://architecture.pattern.lazy.evaluated.text.processing.with.deferred.computation","name":"lazy-evaluated text processing with deferred computation","description":"Implements lazy evaluation of expensive NLP operations by deferring computation until properties are explicitly accessed. Sentence objects don't compute POS tags until .tags is accessed, Word objects don't compute lemmas until .lemma is accessed, and sentiment analysis only runs when .sentiment is accessed. This architecture reduces memory overhead and computation time for large texts where only a subset of analyses are needed.","intents":["I need to process large documents efficiently without computing all NLP analyses upfront","I want to access only the specific NLP properties I need for my use case","I need to reduce memory footprint for batch processing of many documents"],"best_for":["Large-scale text processing with selective analysis requirements","Memory-constrained environments (mobile, embedded, serverless)","Applications where only a subset of NLP analyses are used per document"],"limitations":["Lazy evaluation adds complexity to debugging; errors may occur far from the access point","First access to a property incurs full computation cost (no caching across accesses)","No built-in caching mechanism; repeated accesses recompute the same analysis","Difficult to estimate total processing time upfront due to deferred computation"],"requires":["Python 3.6+","Understanding of lazy evaluation and property decorators"],"input_types":["text string"],"output_types":["TextBlob, Sentence, or Word objects with lazy-evaluated properties"],"categories":["architecture-pattern","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+","NLTK data files (optional, for enhanced tokenization)","Pattern library (for inflection/lemmatization features)","Understanding of object-oriented design and nested data structures","NLTK 3.0+ with averaged_perceptron_tagger data (for NLTKTagger)","Pattern library (for PatternTagger)","NLTK with POS tagger data","CoNLL model data (for ConllExtractor)","Pattern library (for sentiment lexicon and scoring)","Pattern library (for morphological rules and exception dictionaries)"],"failure_modes":["Pattern-based approach may struggle with non-English languages or domain-specific abbreviations","No machine learning-based boundary detection — relies on heuristics","Performance degrades on very long documents (>100K words) due to regex operations","Contraction handling is English-centric and may not work for other languages","Punctuation attachment logic uses simple heuristics, not grammar-aware parsing","No built-in support for multi-word expressions or compound words in non-Germanic languages","Nested object creation adds memory overhead for large documents","No built-in support for sub-sentence spans (e.g., noun phrases as first-class objects)","Bidirectional references can cause circular dependencies if not carefully managed","No support for overlapping or discontinuous text spans","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:25.058Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-textblob","compare_url":"https://unfragile.ai/compare?artifact=pypi-textblob"}},"signature":"yorWDZSXNcLSdzeOnRhwPU7bRcIy2JrDks5i/Fukr8Kb6J4Wbo4OMxplsmNVW2wg8tHNjTn2kS0//OMQoD2nBw==","signedAt":"2026-06-20T22:40:24.286Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-textblob","artifact":"https://unfragile.ai/pypi-textblob","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-textblob","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}