Sentiment Analysis of β-Hydroxybutyrate (BHB)
Supplements’ Consumer Online Reviews
Background: Advanced approaches such as sentiment analysis have been
developed to extract and analyze various objects for consumer insights. However, in the
field of dietary supplement, we have scarcely observed such application which actually
would help understand the consumer shopping behaviors on emerging supplement
products. Thus, more attempts are needed to explore the consumer behavior via those
Methods: The text data of 71 β-Hydroxybutyrate (BHB) products’ consumer
reviews were extracted with the aid of the Web Scraper Chrome extension. Then, a
lexicon-based sentiment analysis approach was developed to sort out the sentiment
or polarity of BHB products’ consumer online reviews. Word-level sentiment analysis
gave direct observation of BHB products’ consumer feedback, while sentence-level
sentiment analysis further scored the analyzed text snippets with the labels of flavor
and package. Besides, the compliment complex analysis helps verify the robustness of
Results: We find that that flavoring is important to the β-Hydroxybutyrate (BHB)
product performance among other factors such as packaging and brand. We also find
that consumers are more willing to accepting flavored BHB products than unflavored
BHB products despite of their high prices. Creativities such as lemon-raspberry flavor
even differentiate the BHB sensory products among competitors. On the other side,
high-volume packages provide us with more label space for product marketing and
education. Appropriate product development ensures the basic functions of the active
ingredients in products. In addition, brand building offers another layer of product
Conclusion: A lexicon-based sentiment analysis is used to analyze the
β-Hydroxbutyrate (BHB) products’ consumer online reviews. Through the
comprehensive text-mining, we concluded that appropriate flavoring could largely
enhance the BHB products’ market performance. High-volume packaging could further
promote product marketing and education. Meanwhile, we cannot ignore factors such
as active functions and brand building as well.
β-Hydroxybutyrate (BHB) is the conjugate base of the
organic compound hydroxybutyric acid. The ketone body BHB
can be synthesized in the liver through a series of reactions
during the metabolisms of fatty acids, ketogenic amino acids, and
β-methylbutyrate. It is an essential carrier of energy from the
liver to peripheral tissues during periods of long-time exercise,
starvation, and lack of carbohydrates. BHB can also serve as an
energy source by our brain when blood glucose is low . BHB
compound functions interactively in our body. It can interact
with inflammatory items in immune cells to decrease the level
of inflammatory cytokines and further reduce inflammation
. Previous studies also demonstrated that BHB possessed the
functions of stress reduction,  neural protection,  seizure
alleviation,  weight loss,  and body metabolism in starvation
Recognizing those functions, scientists spend efforts to
commercialize BHB supplement products for the massive
consumers. BHB products are currently commercialized more
as weight loss and energy enhancer on the dietary supplement
market. Thanks to their efforts, the consumers nowadays can easily
get access to those products through channels such as retail stores,
online platforms (e.g., Amazon), local clinics and comprehensive
hospitals. BHB supplement is still a small-sized emerging market
compared with traditional supplements such as vitamin C,
whey protein, and etc. Further understanding consumers’ BHB
shopping behavior, especially online, provides us with first-hand
consumer shopping data, guides R&D to design more targeted
BHB supplement products or derivatives. In a broad sense, such
study helps to develop cost-effective healthcare solutions for new
The obtained consumers’ online reviews served as the critical
building blocks of this research piece. Based on those building
blocks, sentiment analysis has been developed and applied to
mine the text of consumer feedbacks. The technology of sentiment
analysis is also found under terms such as emotion detection, 
semantic analysis,  opinion mining  and etc. Those terms are
more or less similar to the term “sentiment analysis” used here,
a computational study of the text content of people’s opinions,
sentiments, emotions, and attitudes. In detail, it is regarded as a
classification assignment as it classifies the orientation of a text into
either positive, negative, neutral or compound  In the era of big
data, it is useful for companies and individuals to monitor their
reputation and get timely feedback about their products, activities,
events, and policies . It was also quoted as one of the hottest
fields in computer science .
Both machine learning-based and lexicon -based approaches
have been developed to realize the sentiment analysis of text data
. Machine learning-based analysis depends on large volume
of data for accurate prediction. The more training data, the better
the performance of the latter analysis. Meanwhile, lexicon-based
approaches consult lexicons, the online or off-line dictionaries,
to classify the polarities or emotional orientations. It relies on
the consulting dictionary during which a fairly large number of
data is good but not a must condition. The previous studies show
that lexicon-based sentiment analysis work well on social media
type text,  does not require large training data, and perform
rapidly with streams of data . For instance, Paltoglou and
Thewall proposed their algorithm for unsupervised, lexicon-based
sentiment analysis of web-based textural communication such as
online discussions, tweets, and social network comments .
Under the wave of supervised, machine learning approaches in
recent years, their results of extensive tests on three real-world
datasets demonstrated that the developed algorithm outperformed
machine learning solutions in the majority of cases. It suggested
that lexicon-based sentiment analysis could be a robust and reliable
approach to conduct sentiment analysis of informal communication
on the internet. In another research, Kaushik and Mishra utilized a Hadoop-based technique to carry out the sentimental analysis
and opinion mining in a speedy and quantitative manner  Their
results showed that the Hadoop-based method was a speedy and
accurate technique ready for scaled data sets. Hence, amid the pool
of different data analytical tools, sentiment analysis is suitable for
analyzing the consumers’ feedback on an emerging market with a
rapid growth. Bearing such background, this paper illustrated the
application of lexicon-based sentiment analysis to systematically
analyze the consumers’ online reviews on various BHB products, an
emerging dietary supplement market. The resultant analysis helps
us understand consumers’ shopping behavior of innovative dietary
Figure 1: Framework of online review data sentiment
The framework of online reviews’ sentiment analysis is
displayed in Figure 1. It shows that the process of sentiment
analysis including scraping the customer review data from Amazon.
com, data cleaning, word-level sentiment analysis, sentence-level
sentiment analysis, and text complexity analysis.
Online Review Scrape
The Web Scraper, a Chrome extension is used to extract reviews’
texts from dynamic web pages. A sitemap that displays how the
website should be traversed and what data should be extracted
is created prior to online reviews’ scrape. A series of JSON codes
are developed and modified to scrape online customers’ reviews
from Amazon.com. The original code can be found in Scrapehero
package on Github.com. The modified JSON code was inserted into
the sitemap JSON box under Web Scraper extension before data
collection. The request interval is set at 2000 ms during online
review scrape. Depending on the complexity of the reviews, the
reviews’ scrape time for one product on Amazon varies from less
than 1 minute to 30 minutes. Text data sometimes require pre
-process or cleaning before text mining to minimize the noises or
biases . For the online reviews in this research, most users
expressed their comments in a brief and straightforward way. There are not many noise and uninformative parts as HTML
tags, scripts and advertisements as other online texts . We
simply cleaned the text data by removing special characters and
reorganizing the content for further analysis. On another side, we
also tried maintaining the originality of the review contents as
much as possible.
Word-level Sentiment Analysis
An external lexicon or dictionary served as resource to judge the
text sentiment or polarity [15,16]. The words in online reviews of
one product are obtained with NLTK tokenization before sentiment
classification . Then, they are classified into categories of
positive and negative for further analysis. Besides, word clouds
are generated based on the word-tokenized text contents with
the wordcloud function in NLTK . The word-level sentiment
analysis gives us a direct observation of the sentiment expressed
from the text comments.
Sentence-level Sentiment Analysis
Vader sentiment analysis of sentence-tokenized text of online
reviews of one product is performed to gain sentiments including
positive, negative, and polarity score . This approach provides
how positive or negative a snippet under analysis is. In details, the
sentence-level snippets are then classified into the categories of
positive, negative, neutral, and compound, during which scores are
assigned to each snippet. Among the four categories, the compound
score measures the sum of all the lexicon ratings (positive, negative,
and neutral) that have been normalized between -100% (most
extreme negative) and +100% (most extreme positive). It is also
called ‘Normalized, weighted composite score’. The higher the
compound score, the more overall positive we obtain. It provides us
with another angle to view the overall sentiment analysis.
Text Complexity Analysis
Text complexity analysis gives a statistical summary of the text
data we collected. The text complexity analysis summarizes the
number of online reviews for one product, number of characters,
number of words, number of sentences, and number of unique
words in those reviews. The text complexity analysis enables us
to take one more dimension to view those text data, judge the text
feature, and predict the product market confidently.
Review Data Summary
Table 1: Statistics of BHB product review data.
Table 1 shows the statistics of Hydroxybutyrate (BHB)
products’ review data collected on Amazon.com. The BHB product
reviews in text were collected within 2 months of the year 2019.
The entire text dataset include 30877 reviews, 105703 sentences,
and 1574171 words. Those product reviews reflect the clients’
comments on 71 products under 26 brands.
Word-level Sentiment Analysis
Table 2: Word-level sentiment analysis of lemon-series BHB powders and capsules.
Note: *#1 to #6 are flavored BHB products.
Word-level sentiment analysis utilizes lexicon to classify the
words in the online reviews of one product into positive and negative
categories. The process put all the recognized positive words and
recognized negative words into two separate classes. Since the
human language is abundant with the complicated expressions, the
portions of positive and negative words are relatively small. We then
viewed those numbers comparatively. Table 2 partially showcases
the word-level sentiment analysis of β-Hydroxybutyrate (BHB)
powder and capsule products. The flavors used in BHB powder
products include lemon, lime, lemon lime, lemon strawberry, and lemon raspberry. Most of the flavored items were assigned with
positive/negative ratios higher than the unflavored items. Only one
lemon item had the positive/negative ratio 1.68. Three out of four
capsule items received less than 2 or even lower positive/negative
ratios. Such results suggest that appropriate flavoring improves the
consumer acceptance of BHB products. Besides, word clouds were
generated based on the online reviews of flavored/unflavored BHB
products listed in Table 2.
Figure 2: Word clouds of lemon-series and unflavored
BHB powder products’ reviews including product #2, #3,
#4, and #7 from Table 2.
Figure 2 displays the word clouds of lemon-series and
unflavored BHB powder products #2, #3, #4, and #7 from Table 2.
The word size in the word cloud is proportional to the frequency
of that word occurring in the reviews. For instance, the word cloud
of product #2 clearly shows that consumers care about the taste
and ketosis functions of the BHB product (i.e., increasing body
ketone). The big words such as “flavor” and “great” suggested that
the consumers who used product #2 expressed highly-positive feedback on the flavoring part of the product. We then summarized
the top-3 high-frequency words in the word clouds of analyzed BHB
products in Table 3. High-frequency words such as “keto”, “taste”,
“product”, “flavor”, and “great” can be found in the word clouds of
products #2, #3, #4, and #7 (See Table 3). The patterns of top-3
high-frequency words were not identical from product to product,
however, the same words such as “taste” occurred repeatedly
in different orders of BHB products’ high-frequency word lists.
From those highly-repeated words, we speculate that the product
development team behind those products attempted to grab their
consumers by making tasty functional drink mix. In the word
cloud of product #7, we found the word “diarrhea” clearly on the
corner indicating the occurrence of such side effect in body. Similar
side effects caused by magnesium citrate over intake were found
in clinics, and it worsened gastrointestinal load. It also illustrates
that not everyone adapts to the BHB supplements, and appropriate
daily intake (e.g., amount, dosage, and intake approach) should be
Figure 3: Word clouds of BHB capsule products’ reviews
including product #8, #9, #10, and #11 from Table 2.
Table 3: High-frequency words in the word clouds of BHB product reviews.
We then extracted the online reviews of the rest BHB capsule
products (i.e., product #8, #9, #10, and #11) in Table 2, and
generated their word clouds (See Figure 3). We saw different big or
small words on each word cloud in Figure 3. “lost pounds”, “weight
loss”, “lose”, “weight” can be observed in the word clouds of product
#8, #9, and #11, respectively, which reflects the weight loss function
of BHB as supplement. The word “appetite” in the word cloud of
product #9 might be related to the appetite disturbance caused by
BHB. Then, we captured the word “energy” in the word clouds of
product #9, #10, and #11 suggesting the energy enhancer function
of BHB. Plus, the words “help”, “great”, “will”, “work”, “helped”, and
“happy” in the word clouds in Figure 3 give us confidence in the
BHB’s supplement functions. Their corresponding high-frequency
words pattern is straightforward. The words “keto”, “help”,
“supplement”, “product” in the word clouds of product #8 to #11
appeared in different frequency orders (See Table 3). We can see
that the massive consumers put emphasis upon BHB’s functions.
Many of them reflect positive feedbacks on BHB’s functions,
especially weight loss and energy enhancer.
Sentence-level sentiment analysis: Flavor and Price
Figure 4 shows the sentence-level sentiment analysis of BHB
products’ online reviews with flavor focus, including bubble chart
of BHB products (Figure 4A), compound scores of selected branded
BHB products (Figure 4B), and average compound scores of flavored
BHB products (Figure 4C). In Figure 4A, some of the BHB products
have large circles while others have small ones. Large circles
indicate that the product review polarities are higher and more
customers gave positive feedbacks than those who gave negative
feedbacks. Other small circles suggest the adverse direction.
Flavored Products: From Figure 4A, we can see that all sorts
of flavors such as apple, berry, lemon, orange, and etc. have been
used to diversify the β-Hydroxybutyrate (BHB) products’ taste
profiles. Those different flavored BHB products are priced within
a wide range, from $18 to $99. Creative flavor combinations such
as lemon-lime, lemon-raspberry, and orange-mango were observed
in Figure 4A. Those BHB products with combined flavors actually
had higher compound scores than other flavored BHB products. For
instance, some apple or caramel flavored BHB products had high
compound scores, however, more BHB products with combined
flavors had such high compound scores, and some among them had
the highest compound scores. Those BHB products with combined
flavor actually occupied a certain space in Figure 4A. We selected
some of the representative BHB products and placed them together
in Figure 4B. The compound scores of those flavored BHB powder
products were high, more than 20%. Besides, we grouped all the
flavored BHB products together in Figure 4C. From Figure 4C, we
found that most of the flavor categories have average compound
scores higher than 20%. Five flavor categories among them have average compound scores even higher than 25%, and three of
them got average compound scores close to or higher than 30%.
Those top products were flavored with apple, berry, cherry, lemonlime,
and lemon-strawberry flavors, most of which fell in the
category of the citrus flavors. It is not too surprising. Citrus flavors
have been investigated for a long time . This flavor category
has been widely-accepted to people all over the world, more
importantly, is available and tastes similar globally. Those flavors
have clean, refreshing tasting note, and are well-compatible with
many other flavors and ingredients. Thus, it is relatively easy to
commercialize citrus flavor–involved products around the world.
The feasibility of design, process, and production also makes the
application of citrus flavors convenient [20,21]. Citrus flavors have
succeeded in bringing crisp sensation to various beverages, snacks
and confections. Products with citrus flavors under brands such
as Tropicana, Gatorade, Hi Chew can be found everywhere. The
high average compound scores of those BHB products flavored
with citrus flavors aligned with the historical popularity of citrus
flavors. It is worth mentioning that the combination of different
citrus flavors such as orange-mango, lemon-raspberry, and so
forth can lead to even higher polarity of their product feedbacks.
Such creation makes the monotonous supplements stand out and
differentiate the brand among competitors.
Unflavored Products: For unflavored BHB products, most
prices overlapped with each other within a narrow range from $15
to $40 (See Figure 4A). The compound scores of unflavored BHB
products had a broad range from -22% to 37% (See Figures 4A, 4B).
The compound scores of BHB capsule products fluctuated heavily.
Among them, quite a few BHB capsule products had the compound
scores above 20%, while others had the compound scores below
5%. The other few unflavored BHB powders and liquid had the
compound scores of 12.21% (powder), 15.06% (powder), and
13.94% (liquid). Those items were good products, but they were
not as competitive as those with higher compound scores. Although
the unflavored BHB products were sold at low prices, they were still
less popular or competitive than others. For instance, flavored BHB
products under the brand C and E clearly exhibited the compound
scores higher than the other unflavored BHB products (See Figure
4B). More additives such as flavors and sweeteners increase the
product price, however the high quality of the resultant products
still drive consumers back to the products. Besides, those products’
compound scores were calculated based on the certain amounts
of consumers’ reviews. The compound scores of the BHB products
such as A Capsule 2 and B Powder Unflavored 1 from Figure 4B
were generated based on 39 and 170 reviews, respectively. The less
appealing compound scores of those unflavored powder/capsule
products are based on the common agreement among consumers.
The monotonous products cannot arouse consumers’ continuous
Figure 4: Sentence-level sentiment analysis of BHB
products’ online reviews with flavor focus. A Bubble chart
of BHB product price versus flavor; Bubble size indicates
the compound score ranging from -100% to +100%; B
Compound scores of partially-selected branded BHB
products; The item name “A Capsule 1” indicates brand
A, dosage capsule, and product number under brand A; C
Bar chart of BHB product average compound score versus
flavor; The line of Compound = 20% is the differentiating
line; The item name “Lemon, Lime” indicates the products
flavored with lemon, lime, or lemon-lime.
Sentence-level sentiment analysis: Dosage and Package
The current BHB market provides our consumers with different
packages. The packages of 1 oz to 55 oz were used to bottle BHB
products with different dosage forms. Figure 5 shows the sentencelevel
sentiment analysis of BHB products’ online reviews with
All Dosage: Figure 5A shows the bubble chart of BHB products
plotted with package versus dosage format. From Figure 5A, we
observed 3 major dosage forms on the current market of BHB
products, including powder, capsule, and liquid. For BHB capsule,
their packaging sizes were either 4 oz or 5 oz bottle, close to
120 cc or 150 cc. That size fit most of the bottling capacity of
capsule manufactures. The BHB capsule products received a wide
range of compound scores, from -22% to 37%. The BHB capsule
products have straightforward packaging and functions, hence, it
is less obvious to explain the fluctuation of their compound scores.
Nevertheless, when we zoomed into each review of BHB capsules with low compound scores, we found the reviews such as “Gives
you a lot of energy but no weight loss”, “It did nothing and there
was no information with it to tell me what I should do to make it
work, sorry I was very disappointed.”, and “Did nothing at all. No
change, not even a pound dropped combined with diet and exercise.
Save your money.” among all the other positive reviews. The body
metabolisms of BHB products still deserve further investigation.
More clinic studies of BHB products are needed to address this
issue. The consumers might also be subjective and with incorrect
expectation to some extent. On the other hand, the supplement
manufacturers should review those insufficient customers’
feedbacks and educate their clients with the appropriate use
of their products. Unlike capsules, BHB powder products were
commercialized with various packages shown in Figure 5A. There
is no unified package size for the powder products. More creation
becomes possible in bottling BHB powder products.
Figure 5: Sentence-level sentiment analysis of BHB
products’ online reviews with package focus. A Bubble
chart of BHB product package versus dosage form; Bubble
size indicates the compound score ranging from -100% to
+100%; B Bar chart of BHB product average compound
score versus package; The line of Compound = 20% is the
Powder Dosage: Figure 5B displays the average compound
scores of BHB powder products in each package category. Most
of the packages received the average compound scores higher
than 20%. Among them, three packages such as 12 oz, 30 oz, and
42 oz bottles received the average compound scores higher than
30%. The average compound score of BHB powder products in 1
oz packet was also above 20%, suggesting that consumers should
welcome the single-size samples prior to massive consumption.
Some of those high polarity items such as BHB powder in 42 oz
package did not have sufficient amount of online reviews. Unlike
flavor factor, we did not observe exclusive impact of package upon consumption orientation, especially for BHB powder products.
All package items have experienced both high and low compound
scores. Package size is not a dominant factor in BHB product design.
Other factors including formula, price, and label all play a role in the
market performance of the BHB products.
The sentiment analysis provides us with the polarity information
of the text data, while the complex analysis summarizes the word
number, sentence number, and character number in the reviews
of each BHB product. The combination of both analyses enables
us to understand the robustness of the consumers’ feedbacks on
BHB products. Table 4 lists the complex analysis of three brands’
BHB products. It is part of the 71 BHB products’ complex analyses.
The three products under brand A received more than 300 reviews
referring to over 1000 sentences, 14000 words, and 65000
characters. The consumers paid certain amount of attention to the
BHB products under brand A. Interestingly, the products under
brand A had very similar patterns of text complexity, which was
also found in the products of brand C. Five out of six products under
brand C had almost overlapping numeric values in # of reviews, #
of sentences, # of words, and # of characters with small deviations.
Similar text complexity is observed under one brand, while large
differences appear among brands. We can see that brands help
differentiating products among companies. Rational consumers
are willing to trust products with higher reputation more than
others. Those products with high reputation automatically form a
marketing event for their brands.
Table 3: Complex analysis display of three brands’ BHB products.
Note: *#1 to #6 are flavored BHB products.
To have a comprehensive understanding, we mapped the BHB
products’ distributions under the confinement of sentiment and
complexity analyses. As such, Figure 6 presents the mapping of
BHB products labeled with flavors under the combined conditions
of online reviews’ polarity and their complexity. In Figure. 6A and
Figure 6B, the marks’ colors indicate different flavors, while the
same or similar color suggests the same flavor or similar ones.
Figure 6A shows the flavor impact on product reviews’ polarity in
the context of the # of reviews. Data points scattered all over the
plot in Figure 6A. For instance, we observed an unflavored BHB
product with compound score -22% generated from 1 review, a
lemon-lime BHB powder with compound score 55% from 3 reviews,
and another lemon -lime BHB powder with compound score 23%
from over 235 reviews. Products had very distinctive analytical
results. For convenience, we divided the entire plot into 4 sections
by using the boundary of compound% = 20 and # of Reviews or # of
Sentences = 100. In Figure 6A, most of the unflavored BHB products
were located in the center of the plot, while a certain amount of the
unflavored BHB products sat in the left-down part of the figure, the
low compound score and low # of Reviews region. On the contrary,
quite a few flavored BHB products gathered in the right-top part,
the high compound score and high # of Reviews region. Besides,
almost all the flavored BHB products had the compound scores
higher than 20%, nevertheless, there were more unflavored BHB
products with compound scores < 20% than those with compound
scores > 20%. Such observations fortified our previous observation
that flavored BHB products were more easily accepted by the
It should be noticed that the compound score is assigned based
on the entire pool of reviews for one product. We should then take
the text statistics of those reviews into consideration. Unlike the
lengthy text in books, most of the online reviews for one product
involve less than 5 sentences, and only a few of them directly
express sentiment. We utilized # of Sentences as substitute of #
of Reviews for the same analysis. It gives us another angle to view
the product polarity in the context of the alternative text statistics.
Figure 6B shows the plot of compound score versus # of Sentences
with flavor label. Compared with Figure 6A, Figure 6B with # of Sentences has similar range of text statistics. The data in Figure 6B
overally shifted to the right side of higher order magnitude. It was
because large portion of the BHB products’ reviews contained more
than one sentence. Most of the flavored BHB products assembled in
the right-top part of Figure 6B, the high compound score and high #
of Reviews region. Many of the flavors in that region were combined
citrus flavors such as lemon-lime, lemon-strawberry, lemonraspberry,
and orange-mango. Other flavored BHB products were
found outside of that region. For instance, chocolate, apple, and
another lemon-lime BHB powder products received less than 10
either # of Reviews or # of Sentences. Those products were located
in the left-top region. It is likely that they would receive more
positive online reviews, and shift towards right-top region. Similar
to Figure 6A, more unflavored BHB products with compound score
< 20% was observed in Figure 6B. Meanwhile, the relative pointto-
point distances of the unflavored BHB products were changed
also due to the fact that many reviews contain more sentences than
Figure 6: Mapping BHB products under combined
conditions of online reviews’ compound score and
complexity by flavor differentiation including: A. Plot of
Compound - # of Reviews; B. Plot of Compound - # of
Sentences; Each color represents one flavor category (e.g.,
Lemon, Lime indicates lemon, lime, or lemon-lime).
Packaging is another dimension we previously mentioned in
sentence-level sentiment analysis. We applied the same approach
in mapping the BHB products’ distributions labeled with packages
under the confinement of sentiment and complexity analyses (see
Figure 7A and Figure 7B). The colors in Figure 7A and Figure 7B
indicate different packages, especially volumes. Among all the
packages, similar volumes such as 20 oz and 25 oz, 1 oz and 4 oz
share the same blue and voilet in Figure 7, respectively. Figure
7A displays the BHB products’ distribution labeled with packages under the combined conditions of compound score and # of
Reviews. Large volume packages appeared on the right-top side of
the plot. Those BHB products’ bottles had volumes from 20 oz to 55
oz. Those items overlapped with the flavored BHB products with
compound scores > 20% in Figure 6A. The majority of the flavored
BHB powder products have package volumes higher than those of
unflavored BHB products. Higher volume such as 55 oz can not only
load more servings in one bottle but also introduce more marketing
and educational information, which makes the product look more
appealing. Some items packed in 1 oz packages had compound score
> 20% while other items bottled in 4 oz volume had compound
score <20%. Those 1 oz items were all flavored BHB products
inserted in 1 serving packages. It demonstrates that consumers are
willing to accepting test samples of flavored BHB products. Their
feedbacks, especially positive ones, are independent on package
volume. With the experience of test samples, customers will likely
give more positive comments to their full-size items. Figure 7B
shows the BHB products’ distribution labeled with packages under
the combined conditions of compound score and # of Sentences.
Likewise, the BHB products’ distribution in Figure 7B is similar to
that in Figure 7A. The only difference lies in the relative positions of
each data point as mentioned before.
Figure 7: Mapping BHB products under combined
conditions of online reviews’ compound score and
complexity by package differentiation including: A. Plot
of Compound - # of Reviews; B. Plot of Compound - # of
Sentences; Each color presents one package size category
(e.g., 1oz, 4oz, and 5oz share the same color).
This piece of research conducted sentiment analysis of BHB
products’ online reviews to understand how the current BHB
products be accepted by the massive consumers. Two factors including flavors and packages were taken into consideration
during analysis. In terms of flavors, they are not only edible
ingredients but also multisensory phenomena with the integration
of taste, olfactory, and other sensory information into a perceived
property of the product rather than a collection of individual
sensory attributes. For clients, the sensory pleasure is their
motivation to consume a product and experience the flavoring
journey again and again . The sensory qualities from flavors
reduce the product risk and increase its consumer affinity. When we
consume supplements, especially sensory products, flavors become
more important than the sum of other parts. In fact, flavor can be
regarded as a primary factor in driving consumption behavior .
It was demonstrated that liking with flavored products increases
chewing and swallowing rates . In the current research, we
observed that flavored BHB products were more popular than
unflavored BHB products. The popularity of flavored BHB products
is independent on the package volume. Among the flavored BHB
products, the products with the combined citrus flavors such as
lemon-raspberry can further increase the polarity of their online
reviews. In other words, those particular flavored BHB products
stand out among competitors.
Package is another factor when we conducted sentiment
analysis of BHB products. It is the first visualization of product to
the consumers. It asserts a critical role in product marketing and
sales. Research shows that even the position of an image on the
packaging affects consumers’ perception of the product weight and
package evaluation . A considerable amount of investigations on
multisensory product perception suggest that packaging features
can bias consumers’ flavor evaluations . Another consumer
research shows that altering packaging materials affect not only
sustainability perceptions but also several other aspects including
perceived taste and quality . Our observation indicates that the
packages of the flavored BHB products are distinct from those of
the unflavored BHB products, especially packaging size. For the
most of unflavored BHB products, their packaging sizes are limited
in design. Compared with those unflavored BHB products, flavored
BHB products have a wider range of package volumes which offers
more possibilities for product marketing and education. In addition
to the major labels of flavor and package, the sentiment analysis
enables us to notice the fact that massive consumers emphasize
on the BHB’s functions such as weight loss and energy enhancer.
Consumers are not willing to seeing that products lose those
functions and work only as placebos. Some side effects of BHB
products such as diarrhea suggest that we should continue more
clinical studies and user education. From the complex analysis,
brand implicitly deploys product differentiation and credit
enhancement on this emerging market. The product differentiation
refers to the business strategy of highlighting the unique features
and benefits to separate it from competitors. When it functions,
brand can create additional intangible value, consumer loyalty, and
even market trend.
Sentiment analysis of β-Hydroxybutyrate (BHB) products’
online consumer reviews was carried out to explore consumer
insights on the emerging marketing of dietary supplements. Our
observations demonstrate that flavoring plays a key role in the BHB
product market together with other factors such as packaging and
brand. Flavored BHB products are more popular than unflavored
BHB products, and they are more acceptable by massive consumers
despite of their high prices. Creative flavors such as lemonraspberry
enable BHB products to stand out among competitors.
High volume packaging provides consumers with more possibilities
of marketing and education. Meanwhile, we cannot ignore other
factors such as active functions and brand building.
I would like to thank my parents for their endless love and
support all the time, and sincerely hope that the technological
innovation improves their life quality and other elders’.
This research was made possible with funding from
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