Published on in Vol 5, No 12 (2021): December

Preprints (earlier versions) of this paper are available at, first published .
User Reviews of Depression App Features: Sentiment Analysis

User Reviews of Depression App Features: Sentiment Analysis

User Reviews of Depression App Features: Sentiment Analysis

Authors of this article:

Julien Meyer1 Author Orcid Image ;   Senanu Okuboyejo2 Author Orcid Image

Original Paper

1School of Health Services Management, Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada

2Department of Computer and Information Science, Covenant University, Ota, Nigeria

Corresponding Author:

Julien Meyer, MSc, PhD

School of Health Services Management

Ted Rogers School of Management

Ryerson University

350 Victoria Street

Office: TRS 3-081

Toronto, ON, M4T 1G6


Phone: 1 4169795000 ext 6296


Background: Mental health in general, and depression in particular, remain undertreated conditions. Mobile health (mHealth) apps offer tremendous potential to overcome the barriers to accessing mental health care and millions of depression apps have been installed and used. However, little is known about the effect of these apps on a potentially vulnerable user population and the emotional reactions that they generate, even though emotions are a key component of mental health. App reviews, spontaneously posted by the users on app stores, offer up-to-date insights into the experiences and emotions of this population and are increasingly decisive in influencing mHealth app adoption.

Objective: This study aims to investigate the emotional reactions of depression app users to different app features by systematically analyzing the sentiments expressed in app reviews.

Methods: We extracted 3261 user reviews of depression apps. The 61 corresponding apps were categorized by the features they offered (psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment). We then produced word clouds by features and analyzed the reviews using the Linguistic Inquiry Word Count 2015 (Pennebaker Conglomerates, Inc), a lexicon-based natural language analytical tool that analyzes the lexicons used and the valence of a text in 4 dimensions (authenticity, clout, analytic, and tone). We compared the language patterns associated with the different features of the underlying apps.

Results: The analysis highlighted significant differences in the sentiments expressed for the different features offered. Psychoeducation apps exhibited more clout but less authenticity (ie, personal disclosure). Medical assessment apps stood out for the strong negative emotions and the relatively negative ratings that they generated. Therapeutic treatment app features generated more positive emotions, even though user feedback tended to be less authentic but more analytical (ie, more factual). Supportive resources (connecting users to physical services and people) and entertainment apps also generated fewer negative emotions and less anxiety.

Conclusions: Developers should be careful in selecting the features they offer in their depression apps. Medical assessment features may be riskier as users receive potentially disturbing feedback on their condition and may react with strong negative emotions. In contrast, offering information, contacts, or even games may be safer starting points to engage people with depression at a distance. We highlight the necessity to differentiate how mHealth apps are assessed and vetted based on the features they offer. Methodologically, this study points to novel ways to investigate the impact of mHealth apps and app features on people with mental health issues. mHealth apps exist in a rapidly changing ecosystem that is driven by user satisfaction and adoption decisions. As such, user perceptions are essential and must be monitored to ensure adoption and avoid harm to a fragile population that may not benefit from traditional health care resources.

JMIR Form Res 2021;5(12):e17062




Major depressive disorders account for almost 300 million cases worldwide, with a loss of 63 million disability-adjusted life years every year [1]. Effective treatments are frequently unavailable to those with the greatest need [2,3]. Barriers to receiving mental health and behavioral care include transportation problems, time constraints, costs, emotional barriers, and stigma [3]. Young adults, in particular, tend to have a negative opinion of the mental health care system, feel disconnected from its services, and prefer handling their concerns by themselves rather than resorting to mental health care services [4].

Mobile health (mHealth) apps deliver health care through mobile information technologies such as smartphones and offer an opportunity to address these barriers and expand the reach of depression care, especially in areas with limited or no specialists. They offer multiple advantages such as quasi-unlimited capacity, 24/7 availability, equitable access, anonymity, tailored approach, links to other systems, and low cost [5]. Patients also tend to prefer psychological treatment to medication [4]. As a result, mHealth apps have been installed millions of times [6]. mHealth apps can address a variety of needs and researchers have identified 6 categories of features offered by depression apps, as summarized in Textbox 1 [6,7].

Definition of depression apps features.


  • Educate, train or inform users through books, guides, news, journal articles, commentaries or opinions, tips, and lessons.

Medical assessment

  • Allow users to screen, diagnose, assess risks, assess self, determine treatment.

Symptom management

  • Allow users to track symptoms, gather history, including physical health data and provide useful, comprehensible output.

Therapeutic treatment

  • Prescribe solutions to improve the condition (therapeutic or not). Includes relaxation, hypnosis, mindfulness exercises, meditation, spiritual or faith-based solutions.

Supportive resources

  • Provide referrals for help, connects users with support, for example, emotional and social support, treatment interventions for acute or chronic use, etc.


  • Serve recreational purposes, such as quotes, dark humor, wall papers and games.


  • Offer 2 or more of the above features.
Textbox 1. Definition of depression apps features.

Evidence suggests that mHealth apps can be effective for various mental health disorders, including depression [8-12]. Nevertheless, evidence remains scarce and incomplete. First, there are a limited number of studies and participants involved [9] and they rarely focus on the benefits and usefulness of specific features [6,7]. Second, the rapid evolution of these apps implies that the investment in clinical evaluations of specific apps may be short-lived; for instance, a study showed that 50% of the depression apps available were different after 130 days, with a new app for depression being available every 3 days [13].

Moreover, the clinical relevance seems to be unrelated to adoption [13]. App developers indicate that app stores are increasingly their favorite distribution channel, over health care providers, in line with the evolution of user decision patterns toward increased patient empowerment [14]. Young users, in particular, often openly reject being told what app to use [15]. Thus, adoption of mHealth apps is increasingly driven by patient attitudes about and experiences with the apps than by clinical evidence and professionals’ prescriptions. Therefore, to better address user needs and improve the adoption and use of adequate apps, we need to better understand the experiences, behaviors, and attitudes of the users of these mHealth apps [16].

App reviews are one key source of information on apps. App users are invited by app stores to rate the apps they have installed and write free text reviews about them. Users willingly and spontaneously contribute to these reviews. Although only a fraction of users choose to do so, these reviews aggregate into a large data set of mostly authentic and publicly available data on user experience with depression apps.

These app reviews are important because of several reasons. First, app reviews can help identify bugs, user requirements, feature requests, user experience with specific app features [17,18] and whether the existing features meet user expectations [19]. Consumer knowledge also reflects different types of knowledge that may be expressed in the reviews, such as knowledge of attributes, the topic, or the buying process [20].

Second, user reviews inform us about the experiences and mental states of the users. For instance, the choice of words in web-based blogs by people with mental health issues helped identify young adults’ suspicions toward the mental health system [4].

Finally, user reviews influence the prospective users’ decision to choose a health care service or not [21]. Specifically, the number of user reviews and user ratings of mental health apps influence adoption [22] and the expression of emotions in user reviews, notably negative emotions, influences how people interpret the reviews [23].

As the scope of human endeavors supported by technology continues to broaden and become more intimate, emotions and values tend to play an increasing role in explaining the adoption and use of a technology [24,25]. Users’ emotional reactions can reflect their assessment of an app, their future propensity to use them, and also reflect some of the impacts of the apps on the users’ condition and behavior.


Considering the importance of user emotions and user reviews in understanding user decisions, this study investigates the emotional reactions expressed in user reviews of depression health apps and analyzes how they relate to app features.

Data Collection

Depression apps and their reviews were scraped from Google Play Store and Apple App Store through 42Matters, a third-party application programming interface provider, based on all apps worldwide that included the root depress- either in the title or in the description. The search was conducted in March 2018. The data set was then cleaned manually by a researcher to remove non–English-language app reviews or apps and apps unrelated to the mental condition (eg, depression used as a geological term) and apps with missing data (Figure 1). Screening of the app reviews resulted in a final data set containing 3261 app reviews associated with 61 apps.

Figure 1. Diagram of app reviews selection process.
View this figure

Each app was categorized using functional categories defined in other studies on depression apps [6,7]. For each app, a researcher read the description of the app, installed the app if necessary, and coded the feature or features provided by the app (refer to Textbox 1 for the coding scheme).

Statistical Analysis

In app reviews, users report and document their experiences in an unstructured and nonmethodical manner. The volume, variety, velocity, and veracity of user reviews also contributed to making them difficult to analyze.

As a first step, we created a word cloud of the content of the app reviews by functional category. This provided an intuitive, unmediated idea of some of the themes and concepts reflected in the reviews. We used the statistical and data management software R to remove common English stop words (plus depression,depressed and app) and used the 50 most frequently used words for each group of app reviews (refer to Multimedia Appendix 1 for the list and frequencies of the most frequently used words by feature).

We then used a text-mining tool to analyze the content of the reviews. Natural language processing involves techniques to analyze large data sets of natural (ie, not codified) language and has been critical for understanding consumer attributes and behaviors [26,27]. Sentiment analysis is a subset of natural language processing that investigates thoughts, emotional reactions, and feelings regarding a specific subject or topic or simply identifies the overall polarity of a topic [21]. It operates by extracting and retrieving information from unstructured raw text and extracting words or grammatical patterns that reflect emotions or thought processes. In health care, these analytical methods have been used, for instance, to interpret textual information about patient experience [21,28] or patient satisfaction [29].

App reviews were analyzed using Linguistic Inquiry Word Count (LIWC) 2015 (Pennebaker Conglomerates Inc). LIWC is a well-established application that analyzes natural language text segments and counts the frequency of words reflecting different emotions, thinking styles, social concerns, and other dimensions [30-32]. LIWC is a lexicon-based approach to semantic analysis, which is based on a predefined dictionary. Although LIWC was not specifically developed to investigate app reviews, lexicon-based approaches to sentiment analysis of consumer reviews do not significantly differ based on the context being analyzed [33]. LIWC has been used in prior studies to extract depression-related linguistic cues from web-based forums [31] and analyze mobile app reviews [19,34].

LIWC codifies more than 92 different aspects of language. It assesses the valence of a text in 4 dimensions (authenticity, clout, analytic, tone) by analyzing the linguistic style. Authenticity measures the presence of features associated with true and false stories [32,35]. False stories, for instance, tend to use more motion words and more negative emotion words but fewer first-person pronouns [35]. LIWC then provides a rating from 0 to 100, 50 being neutral. For example, the following review was scored 99 on authenticity, 73.64 on (emotional) tone, but only 1 on analytic and clout, reflecting that the quote talked more about the user’s experience than about the app:

I already knew this but now I can physically see that I am and I can’t even tell my own parents wow. I don’t know how to get better I really don’t and it said I have server depression.

In contrast, the following review was rated 64.27 on analytic, 98.93 on clout, reflecting how the user was analyzing the features and trying to influence the designers but not saying much about his or her experience with the app:

Would Love to have Transparent Effect! And an idea for you, make one with Motivational thoughts and people would go CRAZY and Install your App.

LIWC also measures the frequency of certain lexicons, such as money, home, you or adverb, (not only these exact words but any words related to the theme). Categories were rated from 0 to 100 to reflect the valence in the linguistic feature, 0 indicating complete absence and 100 indicating that the fragment fully reflected the category. Owing to the purpose of the apps, the codes may reflect the user’s state of mind or the feature being assessed. “Best anxiety tool out there” and “It’s OK but too confusing” were both rated at 20 on anxiety but the first one reflected an analytical stance on anxiety while the other reflected the state of mind of the user. Thus, both meanings were included in the values and could not be disentangled by LIWC.

We coded the app reviews with LIWC, meaning that the complete app reviews were analyzed and rated rather than the individual sentences. Using R, we then performed 2-tailed t tests on the relevant dimensions to measure whether there were significant differences between the reviews associated with 1 feature and the depression app reviews overall. We focused on the 4 summary language variables (analytic, clout, authenticity, and emotional tone) [32]. Owing to its importance, instead of reporting the emotional tone directly, we reported its subcomponents positive emotion and negative emotion which have been associated with app adoption [3]. We also added anxiety, a subcomponent of negative emotion, which was directly related to depression. Negative emotion, positive emotion, and anxiety are lexicon dimensions that reflect frequency rather than valence. Therefore, their values were typically lower than those of the dimensions. Textbox 2 defines the variables that were retained.

Selected Linguistic Inquiry Word Count dimensions and their definitions.

Analytic (analytical thinking)

  • Degree to which people use words that suggest formal, logical, and hierarchical thinking patterns. People low in analytical thinking tend to write and think using language that is more narrative, focusing on the here-and-now and personal experiences.


  • Relative social status, confidence, or leadership skill that people display through their writing or talking.


  • When people reveal themselves in an authentic or honest manner, they are more personal, humble, and vulnerable.

Tone (emotional tone)

  • Includes both positive and negative emotion dimensions; the higher the number, the more positive the tone. Ratings below 50 suggest a more negative emotional tone. It was broken down into:

Positive emotion

  • The more that people use positive emotion words, the more optimistic they tend to be. If you feel good about yourself, you are more likely to see the world in a positive way.

Negative emotion

  • Use of negative emotion words is weakly linked to people’s ratings of anxiety or even neurotic. People who have had a bad day are more likely to see the world through negatively-tinted glasses. Words denoting anxiety (worried, fearful...) are a subset of negative emotion.
Textbox 2. Selected Linguistic Inquiry Word Count dimensions and their definitions.

Finally, we illustrated the analysis with samples of complete reviews to connect both the word clouds and the language variables with actual uses of the words, as the anecdotal context facilitated the understanding of the analytical process.

App Statistics

First, we present basic descriptive statistics on the number of reviews by year of publication (Table 1) and by word count (Table 2).

Table 1. Number of reviews by year of publication.
Year of reviewReviews, n
Table 2. Number of reviews by word count.
Word countReviews, n
6 to 101251
11 to 20807
21 to 50868
50 to 100292

Second, we analyzed the number of app reviews by category and the average review rating out of 5 stars (Table 3). No app that offered the feature of symptom management exclusively was identified, and therefore, that category was excluded from the remainder of the analysis. The app ratings for psychoeducation, therapeutic treatment, and multifeature apps were slightly but significantly above average, while those for medical assessment apps were significantly below average. The average character count was 134, which was slightly above the average of 117 observed in app reviews in general [36].

Table 3. App count, installation, and reviews by functional category.
App categoryExclusive feature app count (total apps with feature; N=61), n (%)App review count (N=3261), n (%)Rating, mean (SD)P valueaReview length in words, mean (SD)
Psychoeducationb17 (27.86)259 (7.94)4.2 (1.3).0618.9 (18.2)
Medical assessment12 (19.67)556 (17.05)4.0 (1.4)<.00114.8 (15.7)
Symptom management0 (0)0 (0)N/AcN/AN/A
Therapeutic treatment9 (14.75)293 (8.98)4.3 (1.3).6918.1 (19.7)
Supportive resources4 (6.56)138 (4.23)4.2 (1.4).3920 (24.0)
Entertainment7 (11.47)353 (10.82)4.4 (1.2).0414.9 (14.9)
Multifeature12 (19.67)1662 (50.96)4.4 (1.0)<.00125.8 (27.9)

aWelch 2-sample t test between all reviews and each category.

bReferences to these categories are italicized in the text.

cN/A: not applicable.

Word Clouds

Third, we report the word clouds of app reviews under each category in Multimedia Appendices 2-7. The word clouds represent the most common terms used in the app reviews. The more frequent a word, the bigger and more central its representation in the cloud. Generic words like help or like/love appeared across categories; also, category-specific words emerged, like test or result for medical assessment apps, game or quotes for entertainment apps, people or chat for supportive resources apps and journal or meditation for therapeutic treatment apps. However, some less predictable words also appeared and provided hints about the focus of the users. For instance, severe (ie, severe depression) appeared specifically at the top in the list of words for medical assessment apps or inspire in the list for entertainment apps, which included quotes apps.

Sentiment Analysis of User Reviews

The LIWC sentiment analysis by feature is reported in Table 4 and Figure 2. The P value of the t test compares feature-specific reviews with other app reviews. For instance, the analytical score of 52.2 for therapeutic treatment versus an average score of 43 (SD 36.3) for all reviews has P<.001, meaning that it is significantly above average.

Table 4. Key sentiment dimensions by categorya.
App categoryAnalytic ratingClout ratingAuthenticity rating

Value, mean (SD)P valueValue, mean (SD)P valueValue, mean (SD)P value
Psychoeducationb46.7 (36.4).0849.2 (33.4).00134.8 (36.6)<.001
Medical assessment41 (38.1).1829.5 (29.5)<.00148.9 (40.8).02
Therapeutic treatment52.2 (36.3)<.00145.3 (31.2).1536.8 (38.7)<.001
Supportive resources37.9 (35.0).0847.6 (34.0).0942.6 (40,2).43
Entertainment31.8 (34.7)<.00147 (34.0).0146.7 (40.0).50
Multifeature44.2 (35.6).6244.5 (32.5).0147.15 (39.5).11
All reviews43 (36.3)N/Ac42.8 (32.8)N/A45.3 (39.7)N/A

aWelch 2-sample t test between all reviews and each category.

bReferences to these categories are italicized in the text.

cN/A: not applicable.

Figure 2. Key sentiment dimensions by single-feature app category. LIWC: Linguistic Inquiry Word Count.
View this figure

Psychoeducation app reviews were significantly higher than the average on clout, but were less authentic (34.8), suggesting that the reviews were more focused on influencing others than on sharing personal experiences and that the users were more confident. This was illustrated by reviews such as the following:

No help at all. Lots of information that is easily available from a single Internet search.
I found this helpful but it needs more information for another star, specially I felt that this app has no information about recovery & medical advice (verified doctors).

Medical assessment app reviews had less clout (29.5) and more authenticity (48.9), but also significantly more negative emotions (6.6). This was illustrated by reviews such as, “Kinda lame ask general questions not really a benefit all ready knew answer” or “Only 20% of it was true about me in my test.” Others leaned toward personal disclosure:

I got 25. I am 13 and i literally don’t want my life anymore! I wonder how have i not suicided yet!

Therapeutic treatment app reviews were more analytical (52.2) and less authentic (36.8), suggesting that the reviews were more focused on the actual functions. This was illustrated by reviews such as the following:

Brilliant app I particularly liked the progressive explanation of the cognitive distortions and how to address each accordingly.
So helpful and easily accessible. You can pull the app out whenever you need it.

Supportive resources app reviews exhibited less negative emotions (2.3) and anxiety (0.06) than the average. Supportive resources reviews focused on the process of connecting to other people as illustrated by, “This a great app to connect with people dealing with similar situations!” or on the app features, illustrated as follows:

I have been a member for a while now, and I love we got a mobile app. Wish we could take chats on it though. But awesome non the less.

Entertainment app reviews exhibited feedback that went beyond entertainment concerns, such as the following:

This game is so amazing, I’ve been struggling with depression and self harm since I was 9, this game had such an emotional impact on me, I hope more people discover this game soon, yeah, its pretty laggy, but I think the over all message equals it out.

Moreover, some were very negative, such as the following:

As a clinical counselor I would say this app is likely to lead suffers down a dark path.

Principal Findings

Findings pointed to differences in the emotional experiences of users based on the app features.

Medical assessment apps specifically received highly negative reviews. Their app ratings and positive emotions were significantly lower than the average of the depression app reviews, while the negative emotions were higher than the average. A possible explanation is that unlike other categories, medical assessment apps provide users with feedback and insights into their own personal conditions and whether they are depressed or not. Research suggests that people who disagree with personal feedback may respond with distress and exhibit strong and long-lasting feelings [37]. These emotions may translate into resentment and negative reactions against the quality and the validity of the app. The high level of authenticity also suggests that the users reveal more about themselves. For developers, this suggests the need for caution before introducing medical assessment features in their apps, as they may antagonize their users and possibly distress them. It also raises the question of the impact of medical assessment on users. Distress caused by the outcome may lead the users to seek expert opinion for confirmation or disconfirmation, but it may also lead them to draw negative conclusions about what to expect from medical professionals.

In contrast, supportive resources apps generate fewer negative emotions. As these apps mostly connect the users to other people, the emotional response may focus more on the people or the services connected to them than on the apps themselves. Therapeutic treatment apps generate more positive emotions and anxiety but are also much more analytical, possibly because they focus on the users’ attention on their actions to mitigate their condition. This suggests that both are safer features for developers to offer, at least to begin with. To a certain extent, psychoeducation apps also generate more positive reactions (although not significantly). Their low authenticity level can be explained by the impersonal informational and educational dimensions and it also makes them less risky to implement, to the extent that they do not mislead the users with incorrect information. Higher clout level is associated with higher confidence and social status. Users of psychoeducation apps may require sufficient self-confidence to believe that simply getting access to information is sufficient than more prescriptive features. Thus, these apps may cater to a different, more autonomous population.

Entertainment apps offer an ambivalent picture. They insignificantly generate more negative emotions, but their users also express significantly less anxiety. Entertainment could be an alternative way to engage people with depression who are anxious about dealing directly with their condition. This confirms findings from prior research obtained from focus groups, suggesting that people with mental health issues, especially male adolescents, value entertainment features in mental health management apps [15].


This study has limitations. The app marketplace is continuously evolving. The study was based solely on the information available in Google Play Store and Apple App Store. This information is subject to the inclusion criteria put in place by the app stores and the developers. The authenticity of the reviews included in this study was not validated, and the issue of illegitimate or fraudulent reviews is widespread [38]. These carry special risks regarding mental health apps, as they could lead the users to make decisions that may be detrimental to their health. User socio-demographic information was unavailable, even though this is a standard practice with recent studies involving sentiment analysis of app reviews [17,39,40].

Moreover, only the users who posted reviews of the apps were represented, which limits the sample’s representativeness of the population of mental health app users. Reviewers were willing to publicly associate their usernames with a depression app, which many users may be reluctant to do, considering the stigma associated with mental disorders. In addition, even though the apps mostly cater to a population with depression, we do not know whether the reviewers are people diagnosed with clinical depression. Future research may try to replicate these findings by actively selecting respondents with depression and asking them to review apps. Such results can then be compared with those from this study or those from the app stores.

Finally, an assumption of this study is that the reviews can be generalized to the app feature, but they may reflect the idiosyncrasies of the reviewed apps (eg, bugs or ill-designed apps), as the number of apps in each category varies from 4 to 20.

Comparison With Prior Work

This study contributes to the literature in multiple ways.

In addition to the studies that describe cognitive processes such as satisfaction or confirmation of expectations in mHealth app users [19], our findings suggest that depression apps also generate strong emotions. Emotions form a key element of mental health conditions and access to mental health care [3] and should be of concern to researchers and developers interested in improving the apps.

Depression app use is a health care behavior practiced by a large population with potentially serious mental health conditions [6]. The people routinely use depression apps to access information and assess, track, and manage their condition. Ultimately, they draw conclusions about their condition and take action (or maybe more problematically, do not take necessary action) based on feedback from these apps which could have critical impacts on their mental health if continued without clinical supervision. A major concern of researchers and clinicians regarding depression apps is the clinical validity of these interventions. Few of these apps are rigorously and clinically validated [5,12], and despite efforts to provide clinical evidence, the rapidly changing app environment and user behaviors do not suggest that use will be dominated by clinically validated apps in the near future [5]. Future studies could compare the user reviews of validated apps with those of nonvalidated apps.

Installations of depression apps vastly exceed the number of people accessing mental health care services, and therefore do not compete with traditional care as much as with not accessing care at all [6]. They are typically used as stand-alone self-help programs that are either poorly integrated or entirely not integrated with the continuum of care. How they fit in this continuum is a question by itself. Depression app features include clinically validated, inspired by sound research, alternative unproven approaches, or games with minimal or no clinical claims, several of which can be found in the same app. This indicates unclear differences between the apps used. As such, the use of mental health apps is of interest, both as a clinical intervention and as a common behavior performed by people with mental health conditions.

The reviews of entertainment apps, for instance, suggest that a nonclinical approach may provide help and relief to people with depression, which could lead them to acknowledge their condition, gain confidence in the value of external support, and seek other features in the apps. Thus, entertainment apps may be a stepping stone that does not require the users to recognize their condition and their need for help, considering the stigma and emotional barriers associated with it. They could then serve as a gateway to recognizing the value and seeking professional care for people with serious mental health issues. In contrast, apps may act as a deterrent, either because the users feel that the apps are sufficient or even better than traditional care or because bad app experiences, such as an early and disturbing virtual depression assessment, would cause skepticism toward the value of medical expertise. This requires research on the pathways that patients follow between using depression apps and accessing traditional health care services. In line with approaches that follow up web-based health behaviors of specific groups of people [41], future research could longitudinally follow up depression app user behaviors and decision-making processes to identify these pathways. Such behavior patterns may help tailor apps to diverse populations.

Our findings also highlight the need to discriminate mHealth app use based on the features offered. Typically, researchers categorize apps according to the disease or condition addressed by the apps [6,7]. The features offered by the apps are important. Providing information about a condition and helping the users track their symptoms every day are very different services and our findings show that users react differently to them in their emotions and satisfaction. This suggests breaking down the study of mHealth apps based on the features they offer. This could come either in addition or as an alternative to studying mHealth apps based on the mental health condition that they address. Two mHealth apps that provide the same features for different diseases may have more in common than 2 apps that provide different features for the same disease. This study illustrates a novel way to investigate user beliefs and behaviors toward specific features. Although substantial efforts have been made to extract isolated reviews that specifically mention a feature [17-19], the size of the app ecosystem can make it possible to isolate apps that offer a single feature, thereby capturing reactions of the users who may not specifically mention the feature under consideration.

Sentiment analysis answers the need to rely on reactive vetting tools for mHealth apps (what Olff [5] refers to as postmarketing surveillance) in complement to randomized controlled trials. Researchers are increasingly recognizing the value of mining patient-generated web-based content and feedback [21] and this study is a step toward exploiting the potential of natural language content generated virtually by people with mental health disorders. Sentiment analysis of these data can help refine our understanding of how the users behave and react emotionally outside of clinical settings. How individuals communicate, what activities they engage in, and what language they use are potential indicators of mental health; users’ mental health conditions, such as depression, may reveal patterns of web-based behaviors through Twitter feeds [16]. Further studies could assess the extent to which app reviewers fit into this pattern by comparing them with other app reviewers. Word clouds complement the insights provided by sentiment analysis. They provide an unmediated representation of the words and lexicons used by the reviewers. In addition to the emotions that are conveyed, we can see that the users focus on looking for help on whether they like or lovegood apps. We can deduce the typical focus of their reviews, such as tests and results, for medical assessment. They provide face value and a topical complement to sentiment analysis. Other language analysis tools such as Latent Dirichlet Allocation or Structural Topic Modelling could also be used to provide further insights into the app reviews.

One of the major appealing features of depression apps is that they allow people to circumvent the stigma associated with mental health issues and access services privately. Beyond the privacy-conscious population, our findings suggest that many users are willing to publicly share their personal and intimate experiences about depression on public outlets such as app stores. This source of data can be used to improve individual apps, understand general patterns of use, and learn about the beliefs, behaviors, and emotions of patients. This is also a cautious reminder that the users may not realize that they are not just talking to the community of depression app users but are making a public statement through both a personal and public Google or Apple account that can be viewed by the broader community, including people to whom the users may not want their condition to be revealed. Further research is needed to investigate the extent to which the app reviewers are aware of what they are disclosing and to whom.


This study broadens our understanding of depression app use and user emotions and refines our knowledge of user experience based on the app features used. Users react with observably different emotions and sentiments depending on the features offered by the depression apps. This has implications for clinicians to better orient their patients to the proper apps and for developers to improve their design and handle the delicate and intimate aspects of a vulnerable population. It is also useful for users to better understand the risks and benefits of using mental health apps and for researchers to broaden their understanding of virtual behaviors of people with mental health.

Our understanding of the role of smartphones and other personal technologies, both as a cause of and as a solution to mental health disorders is still limited, and we need to broaden the scope of our investigations to include the emotions associated with these new behaviors in new and authentic data sources such as user reviews.


The authors would like to thank the Ted Rogers School of Management for financing the project.

Authors' Contributions

The first author (JM) designed the project, did the data collection and analysis, and wrote the paper. The second author (SO) wrote a first draft of the introduction and performed an initial data analysis on a subsample of the final data set.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Ranking and frequencies of the most frequent words by category of single-feature apps.

PDF File (Adobe PDF File), 431 KB

Multimedia Appendix 2

Word cloud of psychoeducation app reviews.

PNG File , 42 KB

Multimedia Appendix 3

Word cloud of medical assessment app reviews.

PNG File , 38 KB

Multimedia Appendix 4

Word cloud of therapeutic treatment app reviews.

PNG File , 25 KB

Multimedia Appendix 5

Word cloud of supportive resources app reviews.

PNG File , 25 KB

Multimedia Appendix 6

Word cloud of entertainment app reviews.

PNG File , 26 KB

Multimedia Appendix 7

Word cloud of multifeature app reviews.

PNG File , 22 KB

  1. Whiteford HA, Ferrari AJ, Degenhardt L, Feigin V, Vos T. The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010. PLoS One 2015;10(2):1-14 [FREE Full text] [CrossRef] [Medline]
  2. Mohr DC, Schueller SM, Montague E, Burns MN, Rashidi P. The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res 2014 Jun;16(6):e146 [FREE Full text] [CrossRef] [Medline]
  3. Mohr DC, Hart SL, Howard I, Julian L, Vella L, Catledge C, et al. Barriers to psychotherapy among depressed and nondepressed primary care patients. Ann Behav Med 2006 Dec;32(3):254-258. [CrossRef] [Medline]
  4. Marcus MA, Westra HA, Eastwood JD, Barnes KL, Mobilizing MR. What are young adults saying about mental health? An analysis of internet blogs. J Med Internet Res 2012 Jan;14(1):e17 [FREE Full text] [CrossRef] [Medline]
  5. Olff M. Mobile mental health: a challenging research agenda. Eur J Psychotraumatol 2015;6:1-8 [FREE Full text] [CrossRef] [Medline]
  6. Meyer J, Okuboyejo S. A functional analysis of depression apps user installs and rating. In: Proceedings of the Americas Conference on Information Systems. 2019 Presented at: Americas Conference on Information Systems; 2019; Cancun, Mexico   URL:
  7. Shen N, Levitan M, Johnson A, Bender JL, Hamilton-Page M, Jadad AA, et al. Finding a depression app: a review and content analysis of the depression app marketplace. JMIR Mhealth Uhealth 2015 Feb 16;3(1):e16 [FREE Full text] [CrossRef] [Medline]
  8. Harrison V, Proudfoot J, Wee PP, Parker G, Pavlovic DH, Manicavasagar V. Mobile mental health: review of the emerging field and proof of concept study. J Ment Health 2011 Dec;20(6):509-524. [CrossRef] [Medline]
  9. Donker T, Petrie K, Proudfoot J, Clarke J, Birch M, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res 2013 Nov 15;15(11):e247 [FREE Full text] [CrossRef] [Medline]
  10. Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, et al. CBT for depression: a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry 2013 Feb 07;13:49 [FREE Full text] [CrossRef] [Medline]
  11. Birney AJ, Gunn R, Russell JK, Ary DV. Moodhacker mobile web app with email for adults to self-manage mild-to-moderate depression: randomized controlled trial. JMIR Mhealth Uhealth 2016 Jan 26;4(1):e8 [FREE Full text] [CrossRef] [Medline]
  12. Giosan C, Cobeanu O, Mogoaşe C, Szentagotai A, Mureşan V, Boian R. Reducing depressive symptomatology with a smartphone app: study protocol for a randomized, placebo-controlled trial. Trials 2017 May 12;18(1):215 [FREE Full text] [CrossRef] [Medline]
  13. Larsen ME, Nicholas J, Christensen H. Quantifying app store dynamics: longitudinal tracking of mental health apps. JMIR Mhealth Uhealth 2016 Aug 09;4(3):e96 [FREE Full text] [CrossRef] [Medline]
  14. mHealth App Developer Economics Study 2015. Research 2 Guidance. 2016.   URL: [accessed 2021-11-10]
  15. Kenny R, Dooley B, Fitzgerald A. Developing mental health mobile apps: exploring adolescents' perspectives. Health Informatics J 2016 Jun;22(2):265-275 [FREE Full text] [CrossRef] [Medline]
  16. Coppersmith G, Dredze M, Harman C. Quantifying mental health signals in Twitter. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2014 Presented at: Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality; June, 2014; Baltimore, Maryland, USA p. 51-60   URL:
  17. Guzman E, Maalej W. How do users like this feature? A fine grained sentiment analysis of app reviews. In: Proceedings of the IEEE 22nd International Requirements Engineering Conference (RE). 2014 Presented at: IEEE 22nd International Requirements Engineering Conference (RE); Aug. 25-29, 2014; Karlskrona, Sweden p. 153-162   URL:
  18. Keertipati S, Savarimuthu B, Licorish S. Approaches for prioritizing feature improvements extracted from app reviews. In: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering. USA: ACM; 2016 Presented at: EASE '16: 20th International Conference on Evaluation and Assessment in Software Engineering; June 1 - 3, 2016; Limerick Ireland p. 1-6   URL:
  19. Patel P, Licorish S, Savarimuthu B, MacDonell S. Studying expectation violations in socio-technical systems: a case study of the mobile app community. In: Proceedings of the 24th European Conference on Information Systems (ECIS, 2016). 2016 Presented at: 24th European Conference on Information Systems (ECIS, 2016); 2016; Istanbul, Turkey   URL:
  20. Brucks M. A typology of consumer knowledge content. Adv Consum Res 1986;13:58-63 [FREE Full text]
  21. Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res 2013 Nov 01;15(11):e239 [FREE Full text] [CrossRef] [Medline]
  22. Huang H, Bashir M. Users' adoption of mental health apps: examining the impact of information cues. JMIR Mhealth Uhealth 2017 Jun 28;5(6):e83 [FREE Full text] [CrossRef] [Medline]
  23. Kim J, Gupta P. Emotional expressions in online user reviews: how they influence consumers' product evaluations. J Busin Res 2012 Jul;65(7):985-992. [CrossRef]
  24. Dadgar M, Joshi K. The role of information and communication technology in self-management of chronic diseases: an empirical investigation through value sensitive design. J Assoc Inform Syst 2018 Feb;19(2):86-112. [CrossRef]
  25. de Guinea AO, Markus ML. Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Q 2009;33(3):433. [CrossRef]
  26. Abirami A, Askarunisa A. Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Inform Rev 2017 Aug 14;41(4):471-486. [CrossRef]
  27. Xiang Z, Du Q, Ma Y, Fan W. A comparative analysis of major online review platforms: implications for social media analytics in hospitality and tourism. Tourism Manag 2017 Feb;58:51-65. [CrossRef]
  28. Maramba ID, Davey A, Elliott MN, Roberts M, Roland M, Brown F, et al. Web-based textual analysis of free-text patient experience comments from a survey in primary care. JMIR Med Inform 2015 May 06;3(2):e20 [FREE Full text] [CrossRef] [Medline]
  29. Hopper AM, Uriyo M. Using sentiment analysis to review patient satisfaction data located on the internet. J Health Organ Manag 2015;29(2):221-233 [FREE Full text] [CrossRef] [Medline]
  30. Tausczik YR, Pennebaker JW. The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol 2009 Dec 08;29(1):24-54. [CrossRef]
  31. Ramirez-Esparza N, Chung CK, Kacewicz E, Pennebaker JW. The psychology of word use in depression forums in english and in Spanish: testing two text analytic approaches. In: Proceeedings of the International Conference on Web and Social Media. 2008 Presented at: International Conference on Web and Social Media; 2008; Seattle Washington USA p. 102-108   URL:
  32. Pennebaker J, Boyd R, Jordan K, Blackburn K. The development and psychometric properties of LIWC2015. University of Texas at Austin. 2015.   URL: [accessed 2019-02-07]
  33. Al-Natour S, Turetken O. A comparative assessment of sentiment analysis and star ratings for consumer reviews. Int J Inform Manag 2020 Oct;54:102132. [CrossRef]
  34. Evans AM, Stavrova O, Rosenbusch H. Expressions of doubt and trust in online user reviews. Comput Hum Behav 2021 Jan;114:106556. [CrossRef]
  35. Newman ML, Pennebaker JW, Berry DS, Richards JM. Lying words: predicting deception from linguistic styles. Pers Soc Psychol Bull 2003 May;29(5):665-675. [CrossRef] [Medline]
  36. Hoon L, Vasa R, Schneider J, Grundy J. An analysis of the mobile app review landscape: trends and implications. Faculty of Information and Communication Technologies, Swinburne University of Technology. 2013.   URL: https:/​/researchbank.​​file/​ba489b3c-a9bb-41f0-8b43-b6d1ff4d4829/​1/​PDF%20%2823%20pages%29.​pdf [accessed 2021-11-10]
  37. Sargeant J, Mann K, Sinclair D, Van der Vleuten C, Metsemakers J. Understanding the influence of emotions and reflection upon multi-source feedback acceptance and use. Adv Health Sci Educ Theory Pract 2008 Aug;13(3):275-288. [CrossRef] [Medline]
  38. Genc-Nayebi N, Abran A. A systematic literature review: opinion mining studies from mobile app store user reviews. J Syst Softw 2017 Mar;125:207-219. [CrossRef]
  39. Ranjan S, Mishra S. Comparative sentiment analysis of app reviews. In: Proceedings of the 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2020 Presented at: 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT); July 1-3, 2020; Kharagpur, India p. 1-7.
  40. Liang T, Li X, Yang C, Wang M. What in consumer reviews affects the sales of mobile apps: a multifacet sentiment analysis approach. Int J Electron Comm 2015 Nov 30;20(2):236-260. [CrossRef]
  41. Sillence E, Briggs P, Harris PR, Fishwick L. How do patients evaluate and make use of online health information? Soc Sci Med 2007 May;64(9):1853-1862. [CrossRef] [Medline]

LIWC: Linguistic Inquiry Word Count
mHealth: mobile health

Edited by G Eysenbach; submitted 14.11.19; peer-reviewed by M Rauws, D Leightley, B Chaudhry, Z Ma, T Muto, F Lanfranchi, C Fernández, T Ndabu; comments to author 14.09.20; revised version received 15.10.20; accepted 15.10.21; published 14.12.21


©Julien Meyer, Senanu Okuboyejo. Originally published in JMIR Formative Research (, 14.12.2021.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.