Daily sentiment
Net daily sentiment ranged from -73.51 for on Wednesday the 14th to 70.65 for on Friday the 16th.
The sentiment for each tweet is scored from -1 (most negative) to +1 (most positive) using VADER sentiment analysis. Net sentiment is calculated by summing the sentiment across all tweets for a given day and/or category, then normalizing the score by the number of tweets.
Medical conditions from the MedDRA dictionary
ADHD was the most frequently observed medical condition mentioned. Coldness, Cold had the highest overall net sentiment of 7.33. Pain had the lowest net sentiment this week (-52.05).
| medical condition | count | sentiment |
|---|---|---|
| ADHD | 692 | 4.21 |
| Addiction | 235 | -5.13 |
| Anxiety | 54 | -22.86 |
| Overdose | 52 | -31.03 |
| Redness | 51 | 4.11 |
| Coldness, Cold | 49 | 7.33 |
| Fall | 47 | -1.12 |
| Pain | 43 | -52.05 |
| Forgetfulness | 41 | -1.65 |
| Crying | 29 | -45.49 |
MedDra is a standardized medical terminology developed by the International Council for Harmonization Cross-referencing tweets against this list is a starting point for identifying medical conditions mentioned in tweets.
Cross-referencing the MedDRA dictionary by sentiment and topic
Positive tweets:
There were 527 tweets with an strong positive sentiment. The top 10 most frequent medical conditions mentioned within these tweets were (1) ADHD, (2) Addiction, (3) Coldness, Cold, (4) Redness, (5) Fall, (6) Sleepiness, Sleepy, (7) Anxiety, (8) Forgetfulness, (9) Shaking, (10) Worry. Of these terms, Sleepiness, Sleepy (n=11), Shaking (n=7), Worry (n=7) were not in the top 10 most frequent terms across all tweets.
Negative tweets:
There were 697 tweets with an strong negative sentiment. The top 10 most frequent medical conditions mentioned within these tweets were (1) ADHD, (2) Addiction, (3) Pain, (4) Overdose, (5) Anxiety, (6) Crying, (7) Forgetfulness, (8) Fall, (9) Hyperactivity, Hyperactive, (10) Sickness. Of these terms, Hyperactivity, Hyperactive (n=14), Sickness (n=14) were not in the top 10 most frequent terms across all tweets.
Word-level analysis
The 25 most important words within positive tweets (compared to negative and neutral) tweets are shown in the treemap below. The size of each box represents the weighted score of each word. The word “m” within the search for “Adderall” had the highest overall weight. When the words are summed for each topic, Adderall had the highest overall weight.
The 25 most important words within negative tweets (compared to positive and neutral) tweets are shown in the treemap below. The word “wasn” within the search for “Nesina” had the highest overall weight. When the words are summed for each topic, “Nesina” had the highest overall weight within negative tweets.
This analysis of words evaluates the stemmed version of words using the Snowball algorithm. By stemming words, words with similar meaning, such as pain, painful & pained, are grouped together as simply “pain”.
Negative Tweets
Tweets with Negative Sentiment
tweet search topic(s) medical condition(s) mentioned Adderall made my brain so active but the second it started to fade mental exhaustion was so bad. It also made me fidgety and anxious. This shit just makes me feel neurotypical all day? Adderall Anxious, Neurotypical “The Vyvanse crash everyday around this hour is literally a daily depression😣
I know the chemical in my brain is unbalanced but there’s no way to stop it, and I just feel so alone and helpless in this struggle.”Vyvanse Depression “Adderall is a hell of a drug
I used to eat them like mentos doc even prescribed me some bars stupid fucks I’m not a God just an adhd mess no need to 5150 I give the finger instead so hateful had to stop the cycle I was on another 1 like thriller um Micheal. #nft #music “Adderall Same here. Not just adderall but also straterra caused suicidal thoughts for me. And I’ve seen it in friends with Ritalin. It’s logical to expect that almost anything affecting neurotransmitters is going to cause depression when the crash comes. So sorry for this man’s loss. Adderall Suicial thoughts Guess which dumb bitch thought they were tired and took their Adderall and ended up putting themselves on the edge of a panic attack Adderall Panic attack ” Why is there so much mental illness amongst young people? Could it be b/c of Adderall or psychotropic drugs?Past generations coped with harsh living conditions, wars, drafts, economic depression & managed to remain functional during that adversity. Humans are devolving.” Adderall Mental Illness Real talk, though, Adderall fucked my muscles up so badly that I couldn’t stand up in line at the pharmacy and struggled to take a short walk. 500mg magnesium glycinate cleared it right up! What a weird and horrible side effect! Adderall _ When I say tired I mean like… over the summer I was just often physically falling asleep randomly, but only if I took adderall. Today it’s bad but not that bad. But I mean the effects of severe sleepiness kick in about an hour after taking it and makes my regular drowsiness way Adderall Sleepiness taking adderall on days where ur mental health is already shit is like the worlds worst gamble, ur either gonna stop being sad or spiral so bad Adderall Mental Health Guys so I officially stop taking my Vyvanse honestly I thought it was helping but my anxiety was so much worse and it hurt my tummy 🙁 also somehow I’m losing weight faster OFF OF IT??? Vyvanse Anxiety
References
Webpage created in R version 4.1.0 (2021-05-18) and R Studio (Version 1.4.1717) using the following packages: plotly, kableExtra, formattable, treemap, and wordpressr.
- C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
- Hao Zhu (2021). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4.
- Kun Ren and Kenton Russell (2021). formattable: Create ‘Formattable’ Data Structures. R package version 0.2.1.
- Martijn Tennekes (2021). treemap: Treemap Visualization. R package version 2.4-3.
- Simit Patel (2021). wordpressr: An API Wrapper for WordPress Site APIs. R package version 0.1.0.
- Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.