{"id":24316054,"url":"https://github.com/venkat-0706/sentimental-analysis","last_synced_at":"2025-03-10T04:47:33.149Z","repository":{"id":253365744,"uuid":"843282261","full_name":"venkat-0706/Sentimental-Analysis","owner":"venkat-0706","description":"Build a model to classify text as positive, negative, or neutral. Apply NLP techniques for preprocessing and machine learning for classification. Aim for accurate sentiment prediction on various text formats.","archived":false,"fork":false,"pushed_at":"2024-08-16T07:51:35.000Z","size":287,"stargazers_count":7,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-17T12:17:56.549Z","etag":null,"topics":["data-visualization","feature-engineering","machine-learning","natural-language-processing","numpy","pandas","python","scikit-learn","sentiment-detection","supervised-learning","text-classification","text-preprocessing","tokenizaiton","wordcloud"],"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/venkat-0706.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":"2024-08-16T07:07:07.000Z","updated_at":"2025-01-03T11:15:20.000Z","dependencies_parsed_at":"2024-08-16T08:56:58.777Z","dependency_job_id":null,"html_url":"https://github.com/venkat-0706/Sentimental-Analysis","commit_stats":null,"previous_names":["venkat-0706/sentimental-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/venkat-0706%2FSentimental-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/venkat-0706%2FSentimental-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/venkat-0706%2FSentimental-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/venkat-0706%2FSentimental-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/venkat-0706","download_url":"https://codeload.github.com/venkat-0706/Sentimental-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242793058,"owners_count":20185949,"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":["data-visualization","feature-engineering","machine-learning","natural-language-processing","numpy","pandas","python","scikit-learn","sentiment-detection","supervised-learning","text-classification","text-preprocessing","tokenizaiton","wordcloud"],"created_at":"2025-01-17T12:17:58.028Z","updated_at":"2025-03-10T04:47:33.129Z","avatar_url":"https://github.com/venkat-0706.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sentimental-Analysis\nAbsolutely! Here's a breakdown of the Coursera Project Network project focusing on sentiment analysis, similar to the previous breakdown but tailored to this specific task:\n\n**Project Scenario:**\n\n* The goal is to understand user sentiment towards a brand or product based on social media posts.\n* You'll use Python to collect social media data (e.g., tweets), analyze the text content, and classify sentiment (positive, negative, neutral).\n* This analysis will help the agency tailor their marketing strategies to better resonate with the target audience.\n\n**Project Objectives:**\n\n* Gain insights into user sentiment towards a brand or product.\n* Inform marketing strategies based on user sentiment analysis.\n* Improve brand perception and increase customer engagement.\n\n** Challenge:**\n\n* Collect social media data relevant to a chosen brand or product.\n* Preprocess the text data (cleaning, normalization).\n* Build a sentiment analysis model using machine learning techniques.\n* Evaluate the model's performance and refine it if necessary.\n* Analyze the results and draw meaningful conclusions about user sentiment.\n\n**Project Deliverables:**\n\n* A sentiment analysis model capable of classifying user opinions (positive, negative, neutral)\n* A report summarizing the findings and insights about user sentiment.\n* Recommendations for the marketing agency based on the sentiment analysis.\n\n**Key Skills Developed:**\n\n* Text Preprocessing Techniques (lowercasing, tokenization, stop word removal)\n* Machine Learning Algorithms for Classification (e.g., Naive Bayes, Logistic Regression)\n* Data Visualization Techniques for Presenting Findings\n\n**Project Breakdown:**\n\n1. **Data Acquisition:**\n    * Choose a social media platform (e.g., Twitter) and relevant API for data collection.\n    * Focus on data related to the brand or product of interest.\n    * Consider using libraries like Tweepy (for Twitter) to collect data.\n\n2. **Data Preprocessing:**\n    * Clean the text data by removing irrelevant information (e.g., URLs, punctuation).\n    * Normalize the text by converting to lowercase, handling emojis, and stemming/lemmatization.\n    * Tokenize the text into individual words for further analysis.\n\n3. **Sentiment Analysis Model Building:**\n    * Choose a suitable machine learning algorithm for sentiment classification.\n    * Train the model on a labeled dataset where sentiment is already identified (positive, negative, neutral).\n    * Evaluate the model's performance using metrics like accuracy, precision, and recall.\n    * Fine-tune the model if necessary to improve classification accuracy.\n\n4. **Analysis and Recommendations:**\n    * Analyze the sentiment distribution (positive vs. negative vs. neutral) towards the brand or product.\n    * Identify common themes or topics associated with positive and negative sentiment.\n    * Generate insights about user perception and areas for improvement.\n    * Recommend marketing strategies based on the sentiment analysis findings.\n\n**Additional Considerations:**\n\n* Explore techniques like handling sarcasm or negation to enhance model accuracy.\n* Consider visualizing the sentiment distribution using charts or word clouds.\n* Address limitations of the project, such as data bias or chosen algorithms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvenkat-0706%2Fsentimental-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvenkat-0706%2Fsentimental-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvenkat-0706%2Fsentimental-analysis/lists"}