{"id":16619777,"url":"https://github.com/rid17pawar/sentiment-analysis-model-experiments","last_synced_at":"2026-05-30T22:31:34.117Z","repository":{"id":177988949,"uuid":"661209371","full_name":"rid17pawar/Sentiment-Analysis-Model-Experiments","owner":"rid17pawar","description":"Experiments in the field of Sentiment Analysis using ML Algorithms namely Logistic Regression, Naive Bayes along with tfidf, one hot encoding, bag of words vectorization.  Different MLP and RNN models viz. LSTM, GRU, Bidirectional LSTM. Lastly, state of the art BERT model","archived":false,"fork":false,"pushed_at":"2023-07-02T11:44:46.000Z","size":1890,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-13T11:43:42.041Z","etag":null,"topics":["bag-of-words","bert","bidirectional-lstm","gru","logistic-regression","lstm","ml-algorithms","naive-bayes","neural-networks","one-hot-encoding","rnn","sentiment-analysis","sentiment-classification","text-vectorization","tfidf","tfidf-vectorizer","transformer-architecture","twitter-sentiment-analysis"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rid17pawar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2023-07-02T06:09:37.000Z","updated_at":"2023-07-18T09:34:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"7cebe75c-608d-4a35-a1c2-de00f4ca39d9","html_url":"https://github.com/rid17pawar/Sentiment-Analysis-Model-Experiments","commit_stats":null,"previous_names":["rid17pawar/sentiment-analysis-model-experiments"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rid17pawar/Sentiment-Analysis-Model-Experiments","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rid17pawar%2FSentiment-Analysis-Model-Experiments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rid17pawar%2FSentiment-Analysis-Model-Experiments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rid17pawar%2FSentiment-Analysis-Model-Experiments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rid17pawar%2FSentiment-Analysis-Model-Experiments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rid17pawar","download_url":"https://codeload.github.com/rid17pawar/Sentiment-Analysis-Model-Experiments/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rid17pawar%2FSentiment-Analysis-Model-Experiments/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33712579,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-30T02:00:06.278Z","response_time":92,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["bag-of-words","bert","bidirectional-lstm","gru","logistic-regression","lstm","ml-algorithms","naive-bayes","neural-networks","one-hot-encoding","rnn","sentiment-analysis","sentiment-classification","text-vectorization","tfidf","tfidf-vectorizer","transformer-architecture","twitter-sentiment-analysis"],"created_at":"2024-10-12T02:42:45.733Z","updated_at":"2026-05-30T22:31:34.100Z","avatar_url":"https://github.com/rid17pawar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sentiment-Analysis-Model-Experiments\n\n## Dataset Used: \n[Twitter US Airline Sentiment - Kaggle](https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment)\n\u003c/br\u003e\u003c/br\u003e\n\n## Experiments:\n#### Experiment-1. Using Machine Learning Algorithms and Vectorization Techniques For Sentiment Analysis\n\n*Text-Vectorization Techniques used:*\n- CountVectorizer\n- TfidfVectorizer\n- OneHotEncoding\n\u003c/br\u003e\u003c/br\u003e\n \n*ML Algorithms used:*\n- Logistic Regression\n- Naive Bayes\n\u003c/br\u003e\u003c/br\u003e\n\n#### Result:\n![image](https://github.com/rid17pawar/Sentiment-Analysis-Model-Experiments/assets/47048717/f8d8b986-f142-40ef-964a-b4c99483af3a)\n*BEST MODEL: TFIDFvectorizer_LogisticRegression*\n\u003c/br\u003e\u003c/br\u003e\n\n#### Experiment-2. Multi-Layer Perceptron (MLP) Models with different Model Architectures and Optimizers For Sentiment Analysis\nModel Architectures:\n- Model-1  \u003c/br\u003e\n Layer (type) -               Output Shape  \u003c/br\u003e\n layer_1 (Dense) -            (None, 64)    \u003c/br\u003e\n layer_2 (Dense) -            (None, 64)    \u003c/br\u003e\n layer_3 (Dense) -            (None, 3)   \n \u003c/br\u003e\n \n- Model-2 \u003c/br\u003e\n Layer (type) -               Output Shape     \u003c/br\u003e\n layer_1 (Dense) -            (None, 32)       \u003c/br\u003e\n layer_2 (Dense) -            (None, 3)            \n \u003c/br\u003e\n \n- Model-3 \u003c/br\u003e\n Layer (type) -               Output Shape      \u003c/br\u003e\n layer_1 (Dense) -            (None, 10)        \u003c/br\u003e\n layer_2 (Dense) -            (None, 3)       \n \u003c/br\u003e\n  \nOptimizers:\n- adam\n- rmsprop\n- sgd\n\u003c/br\u003e\u003c/br\u003e\n\n#### Result:\n![image](https://github.com/rid17pawar/Sentiment-Analysis-Model-Experiments/assets/47048717/246e00e1-0144-41b5-a8b1-1b935cbf22c9)\n\u003c/br\u003e\u003c/br\u003e\n\n#### Experiment-3. Recurrent Neural Network (RNN) Models For Sentiment Analysis\nModel Architectures:\n- Simple RNN Model  \u003c/br\u003e\n Layer (type) -               Output Shape     \u003c/br\u003e\n embedding_12 (Embedding)    (None, 22, 100)  \u003c/br\u003e\n layer_1 (SimpleRNN)         (None, 128)       \u003c/br\u003e\n layer_2 (Dense)             (None, 10)        \u003c/br\u003e\n output_layer (Dense)        (None, 3)         \u003c/br\u003e\n \u003c/br\u003e\n \n- LSTM Model \u003c/br\u003e\n Layer (type) -               Output Shape     \u003c/br\u003e\n embedding_12 (Embedding)    (None, 22, 100)  \u003c/br\u003e\n layer_1 (LSTM)              (None, 128)      \u003c/br\u003e\n output_layer (Dense)        (None, 3)        \u003c/br\u003e\n\u003c/br\u003e\n \n- GRU Model \u003c/br\u003e\n Layer (type) -               Output Shape     \u003c/br\u003e\n embedding_12 (Embedding)    (None, 22, 100)  \u003c/br\u003e\n layer_1 (GRU)               (None, 128)      \u003c/br\u003e\n output_layer (Dense)        (None, 3)        \u003c/br\u003e\n \u003c/br\u003e\n \n- Bidirectional LSTM Model \u003c/br\u003e\n Layer (type) -                     Output Shape     \u003c/br\u003e\n embedding_12 (Embedding)          (None, 22, 100)  \u003c/br\u003e\n bidirectional_6 (Bidirectional)   (None, 128)      \u003c/br\u003e\n output_layer (Dense)              (None, 3)        \u003c/br\u003e\n\u003c/br\u003e\u003c/br\u003e\n\n#### Result:\n![image](https://github.com/rid17pawar/Sentiment-Analysis-Model-Experiments/assets/47048717/854ef52a-4898-4946-9346-da5f7f242f6a)\n\u003c/br\u003e\u003c/br\u003e\n\n#### Experiment-3. Pretrained and Finetuned BERT Model For Sentiment Analysis\n\n#### Result:\n![image](https://github.com/rid17pawar/Sentiment-Analysis-Model-Experiments/assets/47048717/602f41bd-0c16-40ea-8224-0baceca013dd)\n\u003c/br\u003e\n**Overall Best Model: BERT**\n\u003c/br\u003e\u003c/br\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frid17pawar%2Fsentiment-analysis-model-experiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frid17pawar%2Fsentiment-analysis-model-experiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frid17pawar%2Fsentiment-analysis-model-experiments/lists"}