Journal of medical Internet research

BMJ (Clinical research ed.)

[1] Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses.

[2] When I use a word . . . Medical slang: gomers and gomerettes.

[3] UK: Be Careful What You Tweet

[4] English verb regularization in books and tweets.

[5] A "fitness" Theme May Mitigate Regional Prevalence of Overweight and Obesity: Evidence from Google Search and Tweets.

[6] Creative identity (re)Construction, creative community building, and creative resistance: A qualitative analysis of queer ingroup members' tweets after the Orlando Shooting

[7] Sentiment Analysis of Tweets Including Emoji Data

[8] What does your tweet emotion mean? Neural emoji prediction for sentiment analysis

[9] Actionable Pattern Discovery for Tweet Emotions

[10] Engagement in HIV Care Among New York City Transgender Women of Color: Findings from the Peer-Led, TWEET Intervention, a SPNS Trans Women of Color Initiative.

[11] Sentiment Analysis on Tweets for Trains Using Machine Learning

[12] Topic modeling and sentiment analysis of global climate change tweets

[13] Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018.

[14] Determination and summarization of important tweets after natural disasters

[15] Prescribing and Research in Medicines Management (UK & Ireland) Annual Conference 2018, NCVO, Society Building, London, UK, 14th December 2018: 'Person-centred Care in the Digital Age: Nudge, Nudge, Tweet, Tweet'

[16] An Improved Approach for Sarcasm Detection Avoiding Null Tweets

[17] Sarcasm Detection For Sentiment Analysis in Indonesian Tweets

[18] Sarcastic sentiment detection in tweets streamed in real time: a big data approach

[19] JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System.

[20] Sentiment analysis of tweets using refined neutrosophic sets

[21] An Approach for Sentiment Analysis of GST Tweets Using Words Popularity Versus Polarity Generation

[22] Sentiment analysis of tweets using supervised learning algorithms

[23] Self-presentation and Discreditation – the Key Strategies of the Preelection Political Discourse (as Exemplified in the Pre-election Tweets of D. Trump and H. Clinton)

[24] Language manifestation of the discrediting communicative strategy as a way of political image formation in the virtual space (as exemplified in pre-election tweets by D. Trump)

[25] Cancer, aging and Twitter: A mixed methods evaluation of tweets about Geriatric Oncology (#gerionc) and Geriatric Hematology (#geriheme).

[26] Prediction of Personal Experience Tweets of Medication Use via Contextual Word Representations.

[27] Using Social Media to Determine the Affective and Cognitive Components of Tweets about Sunburn.

[28] Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets

[29] healthy tweets: use and importance of twitter for health prevention

[30] klasifikasi topik keluhan pelanggan berdasarkan tweet dengan menggunakan penggabungan feature hasil ekstraksi pada metode support vector machine (svm)

[31] weaveux tweetposium: may 2, 2014

[32] die tweeterreineleer van natuur en bonatuur

[33] Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

[34] Patient Participation at Health Care Conferences: Engaged Patients Increase Information Flow, Expand Propagation, and Deepen Engagement in the Conversation of Tweets Compared to Physicians or Researchers

[35] Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis

[36] Health activism, vaccine, and mpox discourse: BERTopic based mixed-method analyses of tweets from sexual minority men and gender diverse (SMMGD) individuals in the U.S.

[37] Deep Learning-based Sentiment Analysis of Olympics Tweets

[38] UK: Be Careful What You Tweet