{"id":18244203,"url":"https://github.com/hayatiyrtgl/flm_reviews_analysis","last_synced_at":"2026-04-13T21:05:12.367Z","repository":{"id":235891154,"uuid":"791470116","full_name":"HayatiYrtgl/flm_reviews_analysis","owner":"HayatiYrtgl","description":" Sentiment analysis using ML classifiers for text data.","archived":false,"fork":false,"pushed_at":"2024-04-24T19:22:07.000Z","size":532,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T18:39:24.734Z","etag":null,"topics":["countvectorizer","machine-learning","natural-language-processing","natural-language-understanding","nltk","python","python3","random-forest","random-forest-classifier","scikitlearn-machine-learning","sentiment-analysis","sentiment-classification","sklearn","sklearn-classify"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/HayatiYrtgl.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-24T19:21:09.000Z","updated_at":"2024-04-24T19:23:12.000Z","dependencies_parsed_at":null,"dependency_job_id":"e6299a86-6090-4b1b-9d68-ec92587abdcc","html_url":"https://github.com/HayatiYrtgl/flm_reviews_analysis","commit_stats":null,"previous_names":["hayatiyrtgl/flm_reviews_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/HayatiYrtgl/flm_reviews_analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HayatiYrtgl%2Fflm_reviews_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HayatiYrtgl%2Fflm_reviews_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HayatiYrtgl%2Fflm_reviews_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HayatiYrtgl%2Fflm_reviews_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HayatiYrtgl","download_url":"https://codeload.github.com/HayatiYrtgl/flm_reviews_analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HayatiYrtgl%2Fflm_reviews_analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263046540,"owners_count":23405207,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["countvectorizer","machine-learning","natural-language-processing","natural-language-understanding","nltk","python","python3","random-forest","random-forest-classifier","scikitlearn-machine-learning","sentiment-analysis","sentiment-classification","sklearn","sklearn-classify"],"created_at":"2024-11-05T09:15:46.059Z","updated_at":"2026-04-13T21:05:12.328Z","avatar_url":"https://github.com/HayatiYrtgl.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"This script is a sentiment analysis project using machine learning classifiers such as Random Forests, Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Let's break down the key parts of the code:\n\n1. **Data Loading and Preprocessing**:\n   - The script loads training and test data from CSV files.\n   - It checks for missing values in both datasets and converts text to lowercase.\n   - Regular expressions are used to remove non-alphabetic characters from the text.\n   - Text is split into words and stemmed using the Porter stemming algorithm.\n   - Stop words are removed from the text.\n\n2. **Feature Extraction**:\n   - CountVectorizer is used to convert text data into numerical features.\n\n3. **Model Training**:\n   - Random Forests, Logistic Regression, Naive Bayes, KNN, and SVM classifiers are trained on the training data.\n\n4. **Model Evaluation**:\n   - Accuracy and F1 score are computed for each classifier using the test data.\n\n5. **Prediction**:\n   - There's a function `preprocess` defined to preprocess custom input.\n   - Custom input is preprocessed and transformed using the trained CountVectorizer.\n   - Predictions are made using each classifier for the custom input.\n\n6. **Output**:\n   - For the custom input, the script outputs the predicted probabilities (for Logistic Regression) and the predicted classes for each classifier.\n\nOverall, this script provides a comprehensive sentiment analysis pipeline, from data preprocessing to model evaluation and prediction. 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