{"adoption": {"forks": 602, "observed_at": "2026-08-28T04:05:17.109470+00:00", "stars": 1653}, "canonical_url": "https://ross.abutalabs.com/products/twitter-sentiment-analysis", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.", "domain": ["machine-learning", "social-media"], "enriched": true, "function": ["machine-learning", "nlp", "deep-learning"], "health_score": 20, "homepage": null, "language": "Python", "license": "MIT", "license_family": "permissive", "maturity": "abandoned", "member_repos": ["abdulfatir/twitter-sentiment-analysis"], "name": "abdulfatir/twitter-sentiment-analysis", "platform": ["python"], "pushed_at": "2023-02-27T09:08:55+00:00", "repo": "abdulfatir/twitter-sentiment-analysis", "stars": 1653, "tags": ["sentiment-analysis", "twitter", "lstm", "cnn", "naive-bayes", "svm", "keras", "course-project", "archived", "natural-language-processing"], "topics": ["machine-learning", "deeplearning", "sentiment-analysis", "sentiment-classification", "cnn", "keras", "python", "lstm"], "urls": [], "use_cases": ["classify tweets as positive or negative", "compare classical and deep learning models for sentiment classification", "learn how to preprocess Twitter data for NLP", "implement LSTM and CNN text classifiers in Keras", "study a sentiment analysis project report with results"], "what_it_is": "A collection of Python scripts comparing sentiment analysis methods on tweets, including Naive Bayes, SVM, logistic regression, CNN, and LSTM models built with scikit-learn and Keras. It was a course project, is archived, and cannot ship its dataset due to copyright restrictions.", "when_to_avoid": ["you need a maintained production-ready sentiment analysis library", "you expect the original Twitter dataset to be included", "you need support for recent Python, TensorFlow, or Keras versions without modifications"], "when_to_choose": ["you want reference implementations of multiple sentiment classifiers to study or adapt", "you need a starting point for tweet preprocessing and feature extraction", "you are learning NLP classification techniques with worked examples"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/twitter-sentiment-analysis", "repo": "abdulfatir/twitter-sentiment-analysis", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:05:17.109470+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T03:44:57.645572+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ed981cc08414a22bd79b05a66c0507b10dc5bf0f18c0b8d2d5eaf3d5901edcd6", "fetched_at": "2026-08-28T04:05:17.109470+00:00", "kind": "readme", "missing": false, "url": "https://github.com/abdulfatir/twitter-sentiment-analysis"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 3251, "days_push": 1283, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}