{"id":15657834,"url":"https://github.com/lit26/tweet_analysis_covid-19","last_synced_at":"2025-10-08T12:49:37.920Z","repository":{"id":115223544,"uuid":"261942829","full_name":"lit26/Tweet_Analysis_COVID-19","owner":"lit26","description":"Tweet Analysis on COVID-19","archived":false,"fork":false,"pushed_at":"2020-05-07T05:09:53.000Z","size":164,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-26T00:44:00.121Z","etag":null,"topics":["coronavirus","lda-model","sentiment-analysis","topic-modeling","tweet-analysis"],"latest_commit_sha":null,"homepage":null,"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/lit26.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":"2020-05-07T03:43:43.000Z","updated_at":"2021-05-13T12:01:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"a0f03409-a1f0-484b-a4b4-3dc1d9301146","html_url":"https://github.com/lit26/Tweet_Analysis_COVID-19","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/lit26/Tweet_Analysis_COVID-19","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lit26%2FTweet_Analysis_COVID-19","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lit26%2FTweet_Analysis_COVID-19/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lit26%2FTweet_Analysis_COVID-19/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lit26%2FTweet_Analysis_COVID-19/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lit26","download_url":"https://codeload.github.com/lit26/Tweet_Analysis_COVID-19/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lit26%2FTweet_Analysis_COVID-19/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278948017,"owners_count":26073747,"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","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"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":["coronavirus","lda-model","sentiment-analysis","topic-modeling","tweet-analysis"],"created_at":"2024-10-03T13:09:56.828Z","updated_at":"2025-10-08T12:49:37.876Z","avatar_url":"https://github.com/lit26.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Tweet_Analysis_COVID-19\n\n## Fetching tweet\n\nThis is a modified version of getting past tweets from Jefferson Henrique (https://github.com/Jefferson-Henrique/GetOldTweets-python)\n\n```\npython3 GetOldTweets3.py\n```\n\n## Data Clean\n\nWe need to remove mention, website, non-ASCII, keywords before analysis\n\n```\npython3 dataClean.py\n```\n\n## Natural Language Processing\n```\nTweetAnalysis.ipynb\n```\n\n### Sentiment Analysis\n\nHere I am using the nltk package to do the sentiment analysis\n\n![screenshot1](asset/screenshot1.png)\n\n### LDA Topic Modelling\n\nI am using the 200,000 for training the LDA model and analysis the whole dataset. The dataset can be clustered into 27 topics. Topics are shown below:\n| | Topic |\n| ------------- | ------------- |\n| 0 | hand, wash, tw, prisoner, clinical_trial, officer, writes, water, changing, soap |\n| 1 | india, warned, trump, hart_island, oil, loved_one, latino, nyt, forever, changed |\n| 2 | resource, join, pandemic, crisis, student, help, free, curve, webinar, health |\n| 3 | like, people, know, shit, got, thing, going, gonna, day, think |\n| 4 | dr_fauci, fauci, florida, texas, plasma, poverty, trump, saved, pollution, laid |\n| 5 | stay, stayhome, home, nyc, safe, igshid, quarantine, time, socialdistancing, new |\n| 6 | relief, worker, fund, help, pandemic, support, health, healthcare, donate, need |\n| 7 | tiger, homeless, worker, essential, bronx_zoo, positive, test, mta, shelter, new |\n| 8 | spain, grocery, initiative, threatens, hardest_hit, brief, pause, singapore, weekly, pledge |\n| 9 | sign, petition, pandemic, trump, intelligence, month, spread, crisis, american_moneyforthepeople, projection |\n| 10 | contact_tracing, mom, reopen, survivor, utm_source, minority, campaign, lost, project, shutdown |\n| 11 | mask, face, wearing, wear, people, igshid, glove, like, articleshare, html_referringsource |\n| 12 | testing, vaccine, trial, voting, site, launch, mail, vote, record, south_korea |\n| 13 | black, people, rate, data, death, american, dying, higher, african, killing |\n| 14 | case, death, new, york, death_toll, number, state, confirmed, city, total |\n| 15 | update, live, briefing, mayor, blasio, latest, watch, news, new, task_force |\n| 16 | prison, jail, inmate, passed_away, pandemic, release, charity, housing, rent, crisis |\n| 17 | patient, drug, hydroxychloroquine, treatment, hospital, treat, doctor, supply, survey, study |\n| 18 | tracking, privacy, apple_google, equipment, scam, google, technology, track, misinformation, news |\n| 19 | cat, wisconsin, pet, dog, russia, easter_sunday, election, real_estate, animal, warns |\n| 20 | trump, china, president, american, hoax, response, people, donald, biden, democrat |\n| 21 | map, icu, amazon, feature, defeat, ea, challenging, bailout, democracy, cc |\n| 22 | ventilator, frontline, fuck, sport, hero, peak, season, game, celebrity, fan |\n| 23 | gate, inequality, nursing_home, pandemic, reveals, recovering, startup, crisis, utm_medium, success |\n| 24 | passover, flight, airline, refund, tracker, cousin, payment, cancel, ease, pastor |\n| 25 | test, recovered, flu, tested, antibody, people, symptom, immigrant, testing, know |\n| 26 | easter, boris_johnson, honor, celebrate, navigate, veteran, church, chloroquine, tribute, magazine |\n\n### Analysis\n\nCombine the topics and sentiment results. \n\n![screenshot1](asset/screenshot2.png)\n\nThe hot topics in tweet are topics 2, 3, 5, 6,14, 20.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flit26%2Ftweet_analysis_covid-19","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flit26%2Ftweet_analysis_covid-19","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flit26%2Ftweet_analysis_covid-19/lists"}