Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER.
<p>Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER.</p>
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2025
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| _version_ | 1855367078029033472 |
|---|---|
| author | Yuqi Zhang (286958) |
| author2 | Yingning Wang (4576864) |
| author2_role | author |
| author_facet | Yuqi Zhang (286958) Yingning Wang (4576864) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yuqi Zhang (286958) Yingning Wang (4576864) |
| dc.date.none.fl_str_mv | 2025-09-18T17:23:45Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332414.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Examples_of_positive_negative_and_neutral_tweets_Each_tweet_is_assigned_to_only_one_of_the_categories_based_on_its_sentiment_score_calculated_by_VADER_/30158874 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Cancer Science Policy Mental Health valence aware dictionary representative sentences showed extract representative sentences collected using snscrape associated lung injury gum &# 8221 cannabis &# 8221 discussions significantly increased twitter </ p study examines trends sentiment scores decreased vaping &# 8221 528 smoking cessation smoking cessation discussions sentiment scores &# 8220 discussions related smoking cessation quit smoking xlink "> september 2019 september 1 seentiment reasoner results deepen remaining stable related tweets related keywords qualitative insights public perceptions product use offering insights january 31 entire timespan |
| dc.title.none.fl_str_mv | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_2e98adee4f33ca690ee7edc5c859287c |
| identifier_str_mv | 10.1371/journal.pone.0332414.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30158874 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER.Yuqi Zhang (286958)Yingning Wang (4576864)BiotechnologyCancerScience PolicyMental Healthvalence aware dictionaryrepresentative sentences showedextract representative sentencescollected using snscrapeassociated lung injurygum &# 8221cannabis &# 8221discussions significantly increasedtwitter </ pstudy examines trendssentiment scores decreasedvaping &# 8221528 smoking cessationsmoking cessation discussionssentiment scores&# 8220discussions relatedsmoking cessationquit smokingxlink ">september 2019september 1seentiment reasonerresults deepenremaining stablerelated tweetsrelated keywordsqualitative insightspublic perceptionsproduct useoffering insightsjanuary 31entire timespan<p>Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER.</p>2025-09-18T17:23:45ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0332414.t001https://figshare.com/articles/dataset/Examples_of_positive_negative_and_neutral_tweets_Each_tweet_is_assigned_to_only_one_of_the_categories_based_on_its_sentiment_score_calculated_by_VADER_/30158874CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301588742025-09-18T17:23:45Z |
| spellingShingle | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. Yuqi Zhang (286958) Biotechnology Cancer Science Policy Mental Health valence aware dictionary representative sentences showed extract representative sentences collected using snscrape associated lung injury gum &# 8221 cannabis &# 8221 discussions significantly increased twitter </ p study examines trends sentiment scores decreased vaping &# 8221 528 smoking cessation smoking cessation discussions sentiment scores &# 8220 discussions related smoking cessation quit smoking xlink "> september 2019 september 1 seentiment reasoner results deepen remaining stable related tweets related keywords qualitative insights public perceptions product use offering insights january 31 entire timespan |
| status_str | publishedVersion |
| title | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| title_full | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| title_fullStr | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| title_full_unstemmed | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| title_short | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| title_sort | Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. |
| topic | Biotechnology Cancer Science Policy Mental Health valence aware dictionary representative sentences showed extract representative sentences collected using snscrape associated lung injury gum &# 8221 cannabis &# 8221 discussions significantly increased twitter </ p study examines trends sentiment scores decreased vaping &# 8221 528 smoking cessation smoking cessation discussions sentiment scores &# 8220 discussions related smoking cessation quit smoking xlink "> september 2019 september 1 seentiment reasoner results deepen remaining stable related tweets related keywords qualitative insights public perceptions product use offering insights january 31 entire timespan |