{"id":16418647,"url":"https://github.com/meet57/data-extraction-sentiment-analysis","last_synced_at":"2026-03-02T19:06:57.975Z","repository":{"id":203821859,"uuid":"478118218","full_name":"Meet57/Data-Extraction-Sentiment-Analysis","owner":"Meet57","description":"Emotion Analysis of any text | Twitter data extraction and sentiment analysis based on COVID data","archived":false,"fork":false,"pushed_at":"2022-04-05T12:36:04.000Z","size":334,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-06T17:28:33.067Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"HTML","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/Meet57.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}},"created_at":"2022-04-05T12:27:16.000Z","updated_at":"2022-04-05T12:33:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"736d2e21-6beb-493f-a1c6-db457ad1b742","html_url":"https://github.com/Meet57/Data-Extraction-Sentiment-Analysis","commit_stats":null,"previous_names":["meet57/data-extraction-sentiment-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Meet57%2FData-Extraction-Sentiment-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Meet57%2FData-Extraction-Sentiment-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Meet57%2FData-Extraction-Sentiment-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Meet57%2FData-Extraction-Sentiment-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Meet57","download_url":"https://codeload.github.com/Meet57/Data-Extraction-Sentiment-Analysis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240468268,"owners_count":19806146,"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":[],"created_at":"2024-10-11T07:14:42.964Z","updated_at":"2026-03-02T19:06:52.936Z","avatar_url":"https://github.com/Meet57.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data-Extraction-Sentiment-Analysis\nEmotion Analysis of any text | Twitter data extraction and sentiment analysis based on COVID data\n\n## Project Summary\n\nIn this application, we have dashboard where all the\ninformation about our service will be given. Initially user gets 4 services which are:-\n  1. Extract Tweets\n  2. Sentiment Analysis based on COVID Model\n  3. Use case of Google Perspective API\n  4. Extract tweet with sentiment score\nMain aim is to provide easy data mining as well as basic sentiment analysis using\ndifferent model as well as API.\n\n## Conclusion\n\nNowadays, sentiment analysis or opinion mining is a hot topic in machine learning. We are\nstill far to detect the sentiments of corpus of texts very accurately because of the complexity\nin the English language.\nIn this project we tried to show the basic way of classifying tweets into positive or negative\ncategory using LSTM as baseline and how language models are related to the LSTM and\ncan produce better results. We could further improve our classifier by trying to extract more\nfeatures from the tweets, trying different kinds of features, tuning the parameters of the\nLSTM, TextBlob, perspective API classifier, or trying another classifier all together.\nAs we know every coin have two sides, sentiment analysis is great but it’s a difficult task.\nThe difficulty increases with increase in complexity of opinions expressed. In some of the\nfields like product reviews, face recognition, span filter etc. are relatively easy whereas\nfields like books, movies, art, music, indirect expressions of opinion are more difficult.\nSentiment analysis is in demand because of its efficiency. Thousands of text documents\ncan be processed for sentiment in seconds, compared to the hours by a team of people to\nmanually complete it. It is so efficient, accurate and fast that many businesses are adopting\ntext and sentiment analysis and incorporating it into their business processes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmeet57%2Fdata-extraction-sentiment-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmeet57%2Fdata-extraction-sentiment-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmeet57%2Fdata-extraction-sentiment-analysis/lists"}