Repository Analysis

sloria/TextBlob

Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

3.4 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of sloria/TextBlob, a Python project with 9,540 GitHub stars. SynthScan v2.0 examined 41,263 lines of code across 58 source files, recording 134 pattern matches distributed across 9 syntactic categories. The overall adjusted score of 3.4 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

3.4
Adjusted Score
3.4
Raw Score
100%
Time Factor
2026-07-14
Last Push
9.5K
Stars
Python
Language
41.3K
Lines of Code
58
Files
134
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 1LOW 133

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 134 distinct pattern matches across 9 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers67 hits · 68 pts
SeverityFileLineSnippetContext
LOWtests/test_decorators.py15def test_decorator_raises_missing_corpus_exception():CODE
LOWtests/test_tokenizers.py69 def test_tokenize_with_multiple_punctuation(self):CODE
LOWtests/test_inflect.py13 def s_singular_pluralize_test(self):CODE
LOWtests/test_inflect.py16 def s_singular_singularize_test(self):CODE
LOWtests/test_inflect.py19 def diagnoses_singularize_test(self):CODE
LOWtests/test_inflect.py37 def test_pluralize_already_plural_jeans(self):CODE
LOWtests/test_inflect.py52 def test_all_singular_irregular(self):CODE
LOWtests/test_np_extractor.py38 def test_filter_insignificant(self):STRING
LOWtests/test_np_extractor.py53def test_cannot_instantiate_incomplete_extractor():CODE
LOWtests/test_formats.py34 def test_delimiter_defaults_to_comma(self):CODE
LOWtests/test_formats.py61 def test_read_from_file_object(self):CODE
LOWtests/test_formats.py76 def test_read_from_file_object(self):CODE
LOWtests/test_taggers.py65def test_cannot_instantiate_incomplete_tagger():CODE
LOWtests/test_classifiers.py118 def test_show_informative_features(self):CODE
LOWtests/test_classifiers.py121 def test_informative_features(self):CODE
LOWtests/test_classifiers.py126 def test_custom_feature_extractor(self):CODE
LOWtests/test_classifiers.py179 def test_data_with_no_available_format(self):CODE
LOWtests/test_classifiers.py186 def test_accuracy_on_a_csv_file(self):CODE
LOWtests/test_classifiers.py191 def test_accuracy_on_json_file(self):CODE
LOWtests/test_classifiers.py343def test_basic_extractor_with_list():CODE
LOWtests/test_classifiers.py351def test_contains_extractor_with_string():CODE
LOWtests/test_classifiers.py360def test_contains_extractor_with_list():CODE
LOWtests/test_classifiers.py59 def test_raises_value_error_without_nltk_class(self):CODE
LOWtests/test_classifiers.py85 def test_classify_a_list_of_words(self):CODE
LOWtests/test_classifiers.py89 def test_train_from_lists_of_words(self):CODE
LOWtests/test_classifiers.py138 def test_init_with_csv_file_without_format_specifier(self):CODE
LOWtests/test_classifiers.py152 def test_init_with_json_file_without_format_specifier(self):CODE
LOWtests/test_classifiers.py159 def test_init_with_custom_format(self):CODE
LOWtests/test_classifiers.py203 def test_init_with_bad_format_specifier(self):CODE
LOWtests/test_classifiers.py233 def test_custom_feature_extractor(self):CODE
LOWtests/test_classifiers.py378def test_get_words_from_dataset():CODE
LOWtests/test_blob.py363 def test_blob_with_no_sentences(self):CODE
LOWtests/test_blob.py455 def test_pos_tags_includes_one_letter_articles(self):CODE
LOWtests/test_blob.py460 def test_np_extractor_defaults_to_fast_tagger(self):CODE
LOWtests/test_blob.py465 def test_np_extractor_is_shared_among_instances(self):CODE
LOWtests/test_blob.py471 def test_can_use_different_np_extractors(self):CODE
LOWtests/test_blob.py478 def test_can_use_different_sentanalyzer(self):CODE
LOWtests/test_blob.py487 def test_can_get_subjectivity_and_polarity_with_different_analyzer(self):CODE
LOWtests/test_blob.py493 def test_pos_tagger_defaults_to_pattern(self):CODE
LOWtests/test_blob.py497 def test_pos_tagger_is_shared_among_instances(self):CODE
LOWtests/test_blob.py502 def test_can_use_different_pos_tagger(self):CODE
LOWtests/test_blob.py508 def test_can_pass_np_extractor_to_constructor(self):CODE
LOWtests/test_blob.py565 def test_using_indices_for_slicing(self):CODE
LOWtests/test_blob.py571 def test_indices_with_only_one_sentences(self):CODE
LOWtests/test_blob.py576 def test_indices_with_multiple_puncutations(self):CODE
LOWtests/test_blob.py739 def test_tokenizer_defaults_to_word_tokenizer(self):CODE
LOWtests/test_blob.py745 def test_can_use_an_different_tokenizer(self):CODE
LOWtests/test_blob.py750 def test_words_uses_custom_tokenizer(self):CODE
LOWtests/test_blob.py221 def test_words_are_word_objects(self):CODE
LOWtests/test_blob.py340 def test_senences_with_space_before_punctuation(self):CODE
LOWtests/test_blob.py345 def test_sentiment_of_foreign_text(self):CODE
LOWtests/test_blob.py419 def test_words_includes_apostrophes_in_contractions(self):CODE
LOWtests/test_blob.py666 def test_sentences_after_concatenation(self):CODE
LOWtests/test_blob.py693 def test_sentiment_of_emoticons(self):CODE
LOWtests/test_blob.py727 def test_words_are_word_objects(self):CODE
LOWtests/test_blob.py765 def test_tags_uses_custom_tokenizer(self):CODE
LOWtests/test_blob.py779 def test_tags_with_custom_tokenizer_and_tagger(self):CODE
LOWtests/test_blob.py832 def test_passing_bad_init_params(self):CODE
LOWtests/test_blob.py854 def test_classify_without_classifier(self):CODE
LOWtests/test_blob.py859 def test_word_string_type_after_pos_tags_is_str(self):CODE
7 more matches not shown…
Unused Imports26 hits · 26 pts
SeverityFileLineSnippetContext
LOWsrc/textblob/np_extractors.py11CODE
LOWsrc/textblob/np_extractors.py12CODE
LOWsrc/textblob/np_extractors.py12CODE
LOWsrc/textblob/__init__.py1CODE
LOWsrc/textblob/__init__.py1CODE
LOWsrc/textblob/__init__.py1CODE
LOWsrc/textblob/__init__.py1CODE
LOWsrc/textblob/__init__.py1CODE
LOWsrc/textblob/inflect.py11CODE
LOWsrc/textblob/inflect.py11CODE
LOWsrc/textblob/parsers.py10CODE
LOWsrc/textblob/parsers.py11CODE
LOWsrc/textblob/taggers.py11CODE
LOWsrc/textblob/taggers.py12CODE
LOWsrc/textblob/taggers.py12CODE
LOWsrc/textblob/utils.py1CODE
LOWsrc/textblob/formats.py24CODE
LOWsrc/textblob/sentiments.py11CODE
LOWsrc/textblob/sentiments.py12CODE
LOWsrc/textblob/sentiments.py12CODE
LOWsrc/textblob/sentiments.py12CODE
LOWsrc/textblob/sentiments.py12CODE
LOWsrc/textblob/base.py8CODE
LOWsrc/textblob/decorators.py3CODE
LOWsrc/textblob/en/inflect.py8CODE
LOWsrc/textblob/en/inflect.py14CODE
Fake / Example Data16 hits · 21 pts
SeverityFileLineSnippetContext
LOWtests/test_classifiers.py105 res1 = self.classifier.prob_classify("lorem ipsum")CODE
LOWtests/test_classifiers.py107 self.classifier.update([("lorem ipsum", "positive")])CODE
LOWtests/test_classifiers.py109 res2 = self.classifier.prob_classify("lorem ipsum")CODE
LOWtests/test_classifiers.py229 self.classifier.update([("lorem ipsum", "positive")])CODE
LOWtests/test_blob.py373 blob = tb.TextBlob("lorem ipsum")CODE
LOWtests/test_blob.py374 assert len(blob) == len("lorem ipsum")CODE
LOWtests/test_blob.py377 blob1 = tb.TextBlob("lorem ipsum")CODE
LOWtests/test_blob.py378 assert repr(blob1) == 'TextBlob("{}")'.format("lorem ipsum")CODE
LOWtests/test_blob.py381 blob1 = tb.TextBlob("lorem ipsum")CODE
LOWtests/test_blob.py382 blob2 = tb.TextBlob("lorem ipsum")CODE
LOWtests/test_blob.py383 blob3 = tb.TextBlob("dolor sit amet")CODE
LOWtests/test_blob.py514 blob = tb.TextBlob("lorem ipsum")CODE
LOWtests/test_blob.py519 blob = tb.TextBlob("lorem ipsum")CODE
LOWtests/test_blob.py521 assert blob.upper() == tb.TextBlob("LOREM IPSUM")CODE
LOWtests/test_blob.py528 blob = tb.TextBlob("Lorem Ipsum")CODE
LOWtests/test_blob.py530 assert blob.lower() == tb.TextBlob("lorem ipsum")CODE
Deep Nesting11 hits · 11 pts
SeverityFileLineSnippetContext
LOWsrc/textblob/_text.py350CODE
LOWsrc/textblob/_text.py459CODE
LOWsrc/textblob/_text.py1178CODE
LOWsrc/textblob/_text.py1316CODE
LOWsrc/textblob/_text.py1360CODE
LOWsrc/textblob/_text.py783CODE
LOWsrc/textblob/_text.py970CODE
LOWsrc/textblob/_text.py1048CODE
LOWsrc/textblob/en/__init__.py51CODE
LOWsrc/textblob/en/inflect.py537CODE
LOWsrc/textblob/en/inflect.py852CODE
Modern Structural Boilerplate6 hits · 6 pts
SeverityFileLineSnippetContext
LOWsrc/textblob/np_extractors.py14__all__ = [CODE
LOWsrc/textblob/__init__.py3__all__ = [CODE
LOWsrc/textblob/inflect.py13__all__ = [CODE
LOWsrc/textblob/parsers.py13__all__ = [CODE
LOWsrc/textblob/taggers.py14__all__ = [CODE
LOWsrc/textblob/sentiments.py19__all__ = [CODE
Over-Commented Block5 hits · 5 pts
SeverityFileLineSnippetContext
LOWsrc/textblob/_text.py181# The default part-of-speech tagset used in Pattern is Penn Treebank II.COMMENT
LOWsrc/textblob/_text.py821COMMENT
LOWsrc/textblob/_text.py1381 ch[-1] = "I-PNP"COMMENT
LOWsrc/textblob/_text.py1401# ------------------------------------------------------------------COMMENT
LOWsrc/textblob/en/inflect.py601#### SINGULARIZE ###################################################################################COMMENT
Decorative Section Separators1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMsrc/textblob/_text.py1401# ------------------------------------------------------------------COMMENT
AI Structural Patterns1 hit · 1 pts
SeverityFileLineSnippetContext
LOWsrc/textblob/_text.py1178CODE
Excessive Try-Catch Wrapping1 hit · 1 pts
SeverityFileLineSnippetContext
LOWsrc/textblob/classifiers.py98 except Exception as error:CODE