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©2023 American Medical Writers Association. All rights reserved.  
ISSN 2163-5315

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ABSTRACT 
The legal status of cannabis continues to evolve, rais-
ing challenges for medical writers who work in popula-
tion health and drug safety. To guide messaging, research 
has investigated how the public perceives cannabis, often 
relying on surveys or “big data” analyses of social media. 
However, these methods can be costly. As a supplement, 
we explored comments posted to a United States Food 
and Drug Administration docket on cannabis science and 
risk, which may offer an accessible, purposive, cost-effec-
tive source of data. We applied a multipronged methodol-
ogy that involved content analysis, sentiment analysis, and 
metadata analysis. The findings suggest that broad messag-
ing on cannabis may have limited effectiveness. Instead, 
medical writers should design messages that emphasize the 
risks of particular products as well as express empathy for 
consumers suffering from specific conditions. Moreover, 
among other things, the findings suggest that medical writ-
ers should use the terms “cannabis” and “marijuana” inten-
tionally, considering the implications of each. In the future, 
research should develop methods to further segment drug 
consumers demographically and psychographically, build-
ing on the methodology that we present here. This research 
may inform not just messaging but regulatory writing prac-
tices and state drug policies. 

The legal status of cannabis has been debated in numer-
ous countries, including the United States (US), where the 
legal cannabis industry may exceed $43 billion in sales by 
mid-decade.1 There have also been changes in public atti-
tudes. A recent survey by the Pew Research Center found 
that over the past decade, the number of US adults who 
oppose cannabis legalization has fallen 20 percentage 
points, from 52% to 32%.2 Moreover, 9 out of 10 US adults 
now support the legalization of cannabis for medical or rec-
reational use, raising numerous questions for public health.3

 The US Food and Drug Administration (FDA) subse-
quently convened a hearing on May 31, 2019, to “obtain 
scientific data and information about the safety, manu-

facturing, product quality, marketing, labeling, and sale of 
products containing cannabis or cannabis-derived com-
pounds.”4 Although the in-person proceedings concluded at 
6:00 PM that day, the discussion has continued through the 
comments posted to the hearing’s docket. The docket com-
ments are broadly accessible, excepting proprietary and 
other sensitive information. 
 Comments posted to federal dockets have received little 
attention from medical writers and researchers in adjacent 
fields. Yet, there are several reasons why these comments 
are potentially valuable. First, the commenters are invested 
in the legal status of cannabis, and thus their comments 
provide a form of purposeful sampling (see Palinkas et al.5). 
In aggregate, their comments, similar to social media posts, 
may texturize our understanding of how the public per-
ceives cannabis, offering a quick and low-cost alternative 
to surveys.6 Second, Regulations.gov, where FDA dockets 
are hosted, informs commenters that what they submit may 
be displayed there. The site relatedly informs commenters 
that, in addition to official agency uses, third parties may 
access or collect comments for their own purposes.7-9 Third, 
the FDA has stated that comments “can, and do, influence 
agency decisions,”10 potentially impacting the work of medi-
cal writers in regulatory settings. 
 Using a multipronged methodology, this study explored 
who the commenters are on FDA docket 2019-N-1482 and 
what they are commenting about. Our specific research 
questions were 

1. What are common themes and concepts in the  
comments? 

2. What sentiment is expressed in the comments? 
3. How did the commenters self-identify, based on the 

demographic categories that the FDA provides? 
4. What geolocations are the comments attached to? 

 The answers to these questions provided helpful insights 
into docket comments, suggesting ways that medical writers 
can gauge public perceptions of cannabis. 

Michael J. Madson, PhD1 and Andrew Madson, MA2 / 1Arizona State University, Mesa, AZ; 2Western Governors  
University, Salt Lake City, UT 

Data Mining FDA Docket 2019-N-1482: Content, Sentiment,  
and Metadata 

ORIGINAL RESEARCH

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AMWAJournal.org     43Data Mining FDA Docket 2019-N-1482: Content, Sentiment, and Metadata 

METHODS
Our methods involved 3 general steps: scraping the data, 
clearing the data, and visualizing the data. We briefly 
explain each below. 

Scraping the Data
Using a custom script in Python, we scraped all of the com-
ments posted to the docket by January 2021 (n = 4,300).  
We also scraped commenter geolocation and demographic 
category (eg, individual consumers, industry representa-
tives, health care professionals, members of government, 
etc.). Commenters can choose whether to include these 
metadata or not. 

Cleaning the Data
This consisted of several sub-steps that are common in data 
analytics. We removed leading and trailing whitespace, 
standardized spellings (drug and chemical names, in partic-
ular), and filtered out stopwords. Our stopwords were hon-
orifics “thanks,” “thank you,” and “sincerely” because these 
words convey phatic rather than substantive meaning in 
the data set. They also included prepositions (eg, “of,” “to,” 
“at”) and coordinating conjunctions (eg, “so,” “and,” “but”), 
which tend to carry little semantic meaning. 
 For content analysis, we used the stemming algorithm 
in Leximancer, a data analytics program that is commonly 
used in health-related research.11-14 For sentiment analysis, 
we lemmatized the data to optimize output from Valence 
Aware Dictionary and sEntiment Reasoner (VADER), as 
Symeonidis et al.15 recommend. 

Visualizing the Data 
We visualized the data both demographically and psycho-
graphically. To do so, we applied content analysis, senti-
ment analysis, and what we called “metadata analysis.”
For content analysis, we uploaded the data set to 
Leximancer, as mentioned above. Leximancer calculates 
the presence and frequency of key concepts as well as their 
co-occurrence.16(p8) Concepts are clusters of terms that 
tend to “travel together” in a data set and, when grouped 
together as themes, maximize the relevancy of all the other 
words in a data set.16(p11) Based on the concepts it detects, 
Leximancer produces a heat map showing the relation-
ships between themes and their underlying concepts as 
well as frequency. The former is indicated by the location of 
a theme or concept on the map and the latter by its color: 
the “hotter” the color (with red being the hottest, purple the 
coldest), the greater the frequency. 
 For sentiment analysis, we used VADER, which takes a 
“bag of words” approach. That is, it analyzes lexical features 
that, based on their meanings, are typically perceived as 

positive, negative, or neutral.17 In our study, we used VADER 
to calculate a compound sentiment score for each docket 
comment and then average a final score for the whole data 
set. For both subjectivity and polarity, sentiment scores 
are normalized between −1.0 (negative sentiment) and 1.0 
(positive sentiment).17

 For metadata analysis, we focused on how commenters 
self-identified as well as where the comments were geolo-
cated. Specifically, we quantified the frequency of each FDA 
demographic category, each country attached to the com-
ments, and each US state attached to the comments. We 
charted these findings through Microsoft Excel and Tableau. 

RESULTS
What Are Common Themes and Concepts in the 
Docket Comments? 
Our content analysis with Leximancer identified 10 common 
themes in the data set, which are displayed in Figure 1. 
 The most common theme was CBD, referring to can-
nabidiol (10,954 occurrences). Its primary concept, CBD, 
tended to co-occur with oil (2,093 co-occurrences), use 
(1,902), take (1,260), helped (1,110), relief (419), milligrams 
(364), daily (340), doctor (255), and dose (243). 
 The next most common theme was pain (8,138 occur-
rences). Its primary concept, pain, tended to co-occur with 
chronic (537 co-occurrences), anxiety (519), life (400), sleep 
(369), work (309), arthritis (280), able (275), tried (251), 
started (230), better (214), year (182), depression (175), old 
(121), days (118), down (101), and symptoms (99). 
 The third most common theme was medical (6,883 
occurrences). Its primary concept, medical, tended to 
co-occur with effects (305 co-occurrences), prescription 
(175), need (167), people (154), issues (112), conditions 
(106), patients (97), cause (74), treatment (73), active (38), 
and disease (34).
 The fourth most common theme was products (6,188 
occurrences). Its primary concept, products, tended to 
co-occur with hemp (633 co-occurrences), consumer (395), 
testing (288), benefits (279), supplement (273), extract 
(266), pharmaceutical (136), food (266), companies (245), 
potential (190), and form (133).
 The fifth most common theme was health (5,591 occur-
rences). Its primary concept, health, tended to co-occur 
with believe (69 co-occurrences), levels (67), access (58), 
children (57), consider (56), natural (49), available (45), 
allow (43), provide (42), medicine (42), THC or tetrahydro-
cannabinol (39), and quality (35).
 The sixth most common theme was cannabis (5,043 
occurrences). Its primary concept, cannabis, tended to 
co-occur with regulations (288 co-occurrences), plant (269), 
FDA (231), support (196), compounds (140), safety (95), 

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AMWAJournal.org     44Data Mining FDA Docket 2019-N-1482: Content, Sentiment, and Metadata 

public (91), industry (85), 
market (81), cannabinoids 
(74), information (53), data 
(51), and based (35). 
 The seventh most 
common theme was drug 
(4,197 occurrences). Its pri-
mary concept, drug, tended 
to co-occur with legal (97 
co-occurrences), control 
(70), alcohol (49), research 
(43), studies (40), states (33), 
and government (33). 
 The eighth most common 
theme was time (2,824 occur-
rences). Its primary concept, 
time, tended to co-occur with 
seizures (53 co-occurrences), 
family (36), body (29), and 
cancer (20).
 The ninth most common 
theme was marijuana (1,833). 
Its primary concept, mar-
ijuana, tended to co-occur 
with substance (55), law (49), 
DEA or Drug Enforcement 
Agency (43), respondent (31), and money (20). 
 The tenth most common theme was months (878 occur-
rences). Its primary concept, months, tended to co-occur 
with night (25). 

What Is the Sentiment in the Comments? 
We found that the data set had a mean subjectivity score of 
0.418, with a standard deviation of 0.190. The data set had  
a mean polarity score of 0.121, with a standard deviation  
of 0.167. 

How Did the Commenters Self-Identify? 
The FDA docket did not require commenters to self-identify 
by selecting a demographic category. In our sample, only 
467 commenters did choose to self-identify: as individual 
consumers (81 commenters), health professionals (8),  
international public citizens (1), or representatives of  
various organizations. 
 Most commenters affiliated with an organization chose 
the most general demographic categories, such as other  
organizations (157 commenters), association (103), or private 
industry (32). Some were more specific, self-identifying as 
representatives of the drug industry (23), a consumer group 
(16), the food industry (8), a health care association (5), or 
international industry (4). Some commenters self-identified 

as representatives of local (1), state (2), federal (3), or other 
government (15) as well as academia (5) or the media (2). 
See Figure 2.

What Geolocations Are the Comments Attached to? 
The majority of comments were not geolocated, but slightly 
more than two-fifths (1,821 comments) were. A few com-
ments were reportedly from a geolocation outside of the US: 
the United Kingdom (3), Canada (3), Australia (2), Norway 
(1), South Korea (1), or Germany (1). 
 Most were from a geolocation in the US, as displayed 
in Figure 3. All 50 states were represented, and so was the 
District of Columbia. The states with the most comments 
were California (183 comments), Texas (152), and Florida 
(128), Missouri (82), New York (73), North Carolina (66), 
Illinois (62), Colorado (57), Kansas (57), and Wisconsin (54). 
Several other states had at least 50 comments: Georgia (52 
comments), Oklahoma (51), and Virginia (50).
 Eight states had fewer than 50 comments but at least 30: 
Washington (46 comments), Arizona (43), Michigan (41), 
Ohio (40), Massachusetts (35), Pennsylvania (35), Oregon 
(32), and Tennessee (30). 
 Nineteen states and the District of Columbia had fewer 
than 30 comments but at least 10: New Jersey (29 com-
ments), Maryland (27), South Carolina (26), Indiana (24), 

Figure 1. A heat map generated by Leximancer showing the relationships between themes and their 
underlying concepts as well as frequency. Relationships are indicated by the location of a theme or concept, 
and frequency is indicated by hue. 

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AMWAJournal.org     45Data Mining FDA Docket 2019-N-1482: Content, Sentiment, and Metadata 

Kentucky (24), Arkansas (23), 
the District of Columbia (23), 
Alabama (22), Minnesota (22), 
Nebraska (20), Connecticut (19), 
Nevada (19), Utah (19), Iowa (17), 
New Mexico (15), Louisiana (11), 
Montana (11), Vermont (11), Idaho 
(10), and New Hampshire (10). 
 The states with the fewest com-
ments were Mississippi  
(9 comments), West Virginia (9), 
Hawaii (8), Rhode Island (8), Alaska 
(6), Wyoming (6), Maine (5), North 
Dakota (3), South Dakota (3), and 
Delaware (2). The average number 
of comments per state was 35.5 
with a standard deviation of 36.4. 

DISCUSSION
Prior “big data” research that explores public perceptions 
of cannabis has generally focused on social media posts.18-

28 Expanding on this research, our study investigated public 
comments to FDA docket 2019-N-1482, applying content 
analysis, sentiment analysis, and metadata analysis in ways 
that may be relevant for medical writers.
 The content analysis suggests that the commenters were 
less concerned with cannabis in the abstract and more con-
cerned with specific products and symptoms. Of particular 
concern were CBD and hemp and the treatment of pain,  
anxiety, and sleep issues. This finding may have relevance 
for public health messaging: rather than targeting cannabis 
in general, messages might be more effective if they discuss 

the risks associated with particular products or if they express 
empathy for consumers suffering from particular symptoms 
or conditions. 
 The concepts “use” and “take” appeared frequently in 
the data. This makes sense, given that a large share of com-
menters who chose a demographic category self-identified 
as individual consumers. Future messaging should strate-
gically employ different verbs, such as “use” and “take,” so 
that medical writers can evaluate the effects. On first glance, 
“take” may have a stronger association with health and med-
ical discourses. “Use” may have a stronger association with 
illicit or recreational activity. Such associations may have a 
significant influence on a message’s overall effectiveness. 
 The content analysis also suggests that cannabis and 
marijuana have different semantic orientations in the data 

Figure 3. The geolocation of comments in the data set, specific to the US (n = 1,810). Darker shades 
indicate a greater number of comments.

Figure 2. How the commenters self-identified, based on the demographic categories provided by the FDA (n = 467).  

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AMWAJournal.org     46Data Mining FDA Docket 2019-N-1482: Content, Sentiment, and Metadata 

set. “Cannabis” was associated with concepts that seem  
regulatory and scientific, such as safety, public, regulations, 
compounds, industry, cannabinoids, and data. “Marijuana” 
may have a more legalistic or punitive orientation, con-
sidering its co-occurrences with concepts like substance, 
law, and money. Future studies could test how participants 
respond to messages about “cannabis” compared with mes-
sages about “marijuana.” In the meantime, medical writers 
should use the 2 terms intentionally, considering the pos-
sible implications of each. Although common in everyday 
speech, “marijuana” may carry more stigma.
 The sentiment scores indicated positive polarity and sub-
jectivity. The polarity score suggests that commenters gener-
ally had neutral or favorable views of cannabis, which should 
be confirmed through additional research. The subjectivity 
score suggests that commenters tended to express personal 
feelings, opinions, and preferences. It is unknown whether 
FDA officials will consider these subjectivities to be “sound 
grounds” for decision-making.10 Because the number of 
comments per state was so variable (the average being 35.5 
with a standard deviation of 36.4), we did not calculate senti-
ment scores by state. A richer level of granularity that allows 
comparisons across states would improve on the methodol-
ogy that we reported here. That granularity could also sup-
port inter- and intra-state policy evaluations, suggesting how 
cannabis policies may have “moved the needle.” 
 The metadata analysis was small scale, as only 10.9% 
of the comments indicated the commenters’ demographic 
category. More than half of these comments were from 
other organizations or associations, and slightly less than a 
fifth were from individual consumers. Because these demo-
graphic categories are self-reported, they cannot be fully 
verified. Future studies might develop techniques of cate-
gorizing demographic information in the comments them-
selves, beyond the limited categories provided by the FDA. 
It would be interesting, for instance, to examine how senti-
ment may vary by occupation, education level, income, age, 
and gender. The findings could support more targeted med-
ical and regulatory communication regarding cannabis as 
well as policy development. 
 Geographically, the metadata analysis indicated that the 
docket has attracted comments from all 50 US states and 
the District of Columbia as well as 6 countries besides the 
US. More than half of the comments were not geolocated. 
Of those that were, about a third came from just 5 states: 
California, Texas, Florida, Missouri, and New York. Because 
most comments were not geolocated, it is not possible to 
determine the representativeness of the docket comments. 
Indeed, the comments may not be representative of public 
opinions toward cannabis writ large. Yet, the median number 
of comments per state and standard deviation suggests  

considerable geographic variation in the docket’s “public 
participation” and “open exchange of ideas.”29

CONCLUSION
At minimum, federal docket comments seem well suited to 
hypothesis generation based on themes/concepts, senti-
ment, and metadata. Future studies should explore ways to 
further segment drug consumers demographically and psy-
chographically, building on the multipronged methodology 
we described here. These studies may inform not just mes-
saging but regulatory communication and state drug policy, 
supporting the work of medical writers. 

Author declaration and disclosures: The authors note no  
commercial associations that may pose a conflict of interest in 
relation to this article.

Author contact: Michael J. Madson, michael.madson@asu.edu

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and-regulatory-review

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https://static1.squarespace.com/static/5e26633cfcf7d67bbd350a7f/t/60682893c386f915f4b05e43/1617438916753/Leximancer+User+Guide+4.5.pdf
https://static1.squarespace.com/static/5e26633cfcf7d67bbd350a7f/t/60682893c386f915f4b05e43/1617438916753/Leximancer+User+Guide+4.5.pdf
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https://obamawhitehouse.archives.gov/the-press-office/2011/01/18/executive-order-13563-improving-regulation-and-regulatory-review
https://obamawhitehouse.archives.gov/the-press-office/2011/01/18/executive-order-13563-improving-regulation-and-regulatory-review
https://obamawhitehouse.archives.gov/the-press-office/2011/01/18/executive-order-13563-improving-regulation-and-regulatory-review



