{"id":23950830,"url":"https://github.com/recker-dev/exploring-nlp","last_synced_at":"2025-06-16T09:34:46.979Z","repository":{"id":270544988,"uuid":"910711985","full_name":"Recker-Dev/Exploring-NLP","owner":"Recker-Dev","description":"This Repo, explores various processes for sentiment analysis using Amazon Customer Review dataset.","archived":false,"fork":false,"pushed_at":"2025-01-01T06:49:03.000Z","size":20101,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-24T09:19:21.600Z","etag":null,"topics":["bagofwords","distilbert","huggingface","machine-learning","nlp-machine-learning","tfidf","word2vec"],"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/Recker-Dev.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":"2025-01-01T06:45:51.000Z","updated_at":"2025-02-21T09:27:02.000Z","dependencies_parsed_at":"2025-01-01T07:35:54.686Z","dependency_job_id":null,"html_url":"https://github.com/Recker-Dev/Exploring-NLP","commit_stats":null,"previous_names":["recker-dev/exploring-nlp"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Recker-Dev/Exploring-NLP","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Recker-Dev%2FExploring-NLP","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Recker-Dev%2FExploring-NLP/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Recker-Dev%2FExploring-NLP/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Recker-Dev%2FExploring-NLP/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Recker-Dev","download_url":"https://codeload.github.com/Recker-Dev/Exploring-NLP/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Recker-Dev%2FExploring-NLP/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260133909,"owners_count":22963819,"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":["bagofwords","distilbert","huggingface","machine-learning","nlp-machine-learning","tfidf","word2vec"],"created_at":"2025-01-06T12:55:52.542Z","updated_at":"2025-06-16T09:34:46.952Z","avatar_url":"https://github.com/Recker-Dev.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sentiment Analysis Project with Text Preprocessing Techniques\n\n## Project Overview\nThis project explores various **text preprocessing techniques** for sentiment analysis using a dataset of customer reviews. The goal is to evaluate the performance of different approaches in classifying the sentiment of text data. The techniques evaluated include:\n\n1. **TF-IDF and Bag-of-Words (BoW)**: These are traditional text representation methods that convert text into numerical features. TF-IDF weighs word frequencies, while BoW represents text as word counts.\n2. **Word2Vec and Average Word2Vec**: Word2Vec generates dense vector representations of words, capturing their semantic relationships. Average Word2Vec aggregates word vectors to represent an entire review.\n3. **Fine-Tuning with DistilBert**: A pre-trained transformer model, DistilBert, is fine-tuned for sentiment classification to leverage powerful contextual representations of text.\n\n## Findings\n\n### TF-IDF and BoW:\n- **Accuracy**: Lower accuracy in sentiment classification compared to other methods.\n- **Analysis**: While simple to implement, these techniques fail to capture the semantic meaning of words, which limits their performance on sentiment analysis tasks.\n\n### Word2Vec and Average Word2Vec:\n- **Accuracy**: Significantly better performance than TF-IDF and BoW, demonstrating the benefit of capturing the semantic relationships between words.\n- **Analysis**: This approach performs better by using word embeddings and similarity search, which provide richer representations of the text.\n\n### Fine-Tuning with DistilBert:\n- **Accuracy**: Achieved the highest accuracy, outperforming both TF-IDF, BoW, and Word2Vec.\n- **Analysis**: The power of pre-trained language models like DistilBert is showcased in this approach, leveraging contextual understanding of text to achieve superior performance.\n\n## Recommendations\nBased on the findings, we recommend the following:\n\n- **Word2Vec or Similar Word Embedding Techniques**: For sentiment analysis tasks on similar datasets, word embeddings like Word2Vec offer a good balance of performance and simplicity, capturing semantic meaning effectively.\n- **Fine-Tuning a Pre-Trained Language Model**: Fine-tuning models like **DistilBert** is likely to yield the best results, especially for larger datasets or more complex sentiment analysis tasks, due to their ability to capture contextual relationships in text.\n\n## Notes\n- The specific performance of each technique may vary depending on the dataset and machine learning model used.\n- Experimentation with different techniques, hyperparameters, and fine-tuning strategies is essential to achieving optimal results in sentiment analysis tasks.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frecker-dev%2Fexploring-nlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frecker-dev%2Fexploring-nlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frecker-dev%2Fexploring-nlp/lists"}