JMIR MEDICAL INFORMATICS

Li et alOriginal Paper

Automatic Construction of a Depression-Domain Lexicon Basedon Microblogs: Text Mining Study
Genghao Li1, MSF; Bing Li1, PhD; Langlin Huang1, BM; Sibing Hou2, MAFN
1

School of Information Technology & Management, University of International Business and Economics, Beijing, China

2

Graduate School of Art and Science, Columbia University, New York, NY, United States

Corresponding Author:
Bing Li, PhD
School of Information Technology & Management
University of International Business and Economics
Chaoyang District, Huixin East Street
Beijing, 100029
China
Phone: 86 1 343 978 8086
Email: 01630@uibe.edu.cn

Abstract
Background: According to a World Health Organization report in 2017, there was almost one patient with depression among
every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow
observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend toshare their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions
using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China
so far, the extraction of language features from web-related depression signals is still in a relatively primary stage.
Objective: The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This
lexicon will contain language features that could help identify social media users who potentially have depression. Our study
also compared the performance of detection with and without our lexicon.
Methods: We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label
propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was
obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection,
we considered six features, and we used five classification methods to test the detection performance.
Results: The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better
than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression andsupport vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy ofpotential depression detection.
Conclusions: Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing
linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detectionin people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.
(JMIR Med Inform 2020;8(6):e17650) doi: 10.2196/17650
KEYWORDS
depression detection; depression diagnosis; social media; automatic construction; domain-specific lexicon; depression lexicon;label propagation

Introduction
Background
Depression, one of the major reasons for suicide in recent years,
is a severe mental disorder characterized by persisting low mood
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states in the affected person. It is expected to be the largest
contributor to disease burden worldwide by 2030, especially in
China with a high-pressure lifestyle. According to a World
Health Organization (WHO) report in 2017 [1], China had morethan 54 million people with depression, which means that there
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was almost one patient with depression among every 20 people.
In addition, a national estimation based on China’s 2012 census
data shows that with an adult population size of 1.04 billion, an
estimated 258.41 million adults (24.79%) are at increased riskof depressive symptoms [2]. It has been reported that the suicide
rate among patients with depression is more than 20 times thatof the general population, and patients with depression accountfor more than half of those who have committed suicide [3].
Diagnosis of potential depression in an early stage can provide
more opportunities for those affected to receive appropriate
treatment and overcome the disease. However, owing to thelack of mental health knowledge, the lack of regular counseling,
and the fact that mental health diseases are greatly different
from physical diseases as there is no pain, many patients withdepression do not recognize it. Although some know a little
about depression, they are often reluctant to seek professional
help because of a sense of shame [4].
The traditional clinical diagnosis of depression mainly relies
on standardized assessments, which are highly accurate but have
limitations in detection efficiency [5]. The medical diagnosis
requires not only filling in a depression assessment scale, suchas the Self-rating Depression Scale, but also a one-to-one
interview and long-term observation [6], which involve highcosts. Patients tend to remain undetected until the disease
presents obvious symptoms, which also means that the optimaltreatment period has passed [7]. The whole diagnosis process
is highly passive, as doctors have to wait for patients to knockon their door.
Things are changing with the development of social media.
Nowadays, many methods combining machine learningalgorithms and text mining techniques have been developed todiagnose potential depression in an early stage [8-13]. Comparedwith traditional approaches, these methods have been provento be effective and inexpensive, and have been shown to reduce
limitations and assist in clinical diagnosis in a more flexible
way. At the same time, people are used to disclosing their innerfeelings on social media. The huge corpus provides abundanttext describing things like sadness, exhaustion, and breakdown,
which have the potential to reflect depression. Hundreds ofmillions of people in third or fourth tier cities and poor
mountainous areas in China have little chance to disclose theirmental conditions directly to experts, but they can provide theiraccounts and apply for social media methods. Experts can thenintervene and conduct more targeted treatments for users whoare potentially depressed. Another scenario involves teenagers
on campus, and teachers can pay more attention to the actual
mental status of students who are potentially depressed withthe help of forums and other web-based text. It is thus feasible
to detect users’ depressive mental states on a large scale onsocial media, and this provides convenience for expert
assessment.
Actually, when coping with textual depression data, word-based
features like frequency and embedding are commonly used anda domain lexicon might be valuable to understand the authorof the text [14]. Many research studies have achieved a lot interms of a depression lexicon, which is mainly in English[9,12,13]. In China, research about web-related depression
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Li et aldetection is just getting started, and we did not find any domain
lexicon research about depression in a public study. It would
not be proper to translate an English lexicon directly owing tocultural differences. Thus, a depression-domain lexicon in
Chinese is needed.
In this paper, based on a well-labeled depression data set on
Weibo, which is one of the largest Chinese user-generated
content platforms, we constructed a depression-domain lexicon
containing more than 2000 words. This lexicon can be used toassist in the early diagnosis of depression. We crawled morethan 144,000 microblog tweets of nearly 2000 users within atime span of 16 months to obtain depressed and nondepresseddata sets. Some manual screening was implemented to remove“fake” depression microblogs from the data sets, which isclarified in the “Data Preprocessing” subsection. We extracted
80 words as seeds and then built a semantic association graphwith the similarities between the seeds and candidate words andutilized the label propagation algorithm (LPA) to automaticallymark new words in the graph. The LPA is a good method insuch a construction, which has been further explained in the“Related Work” subsection. We then tested the effectivenessof this method and compared it with some baseline approaches.
We found that this autoconstruction of a depression-domain
lexicon performed the best and had the most stable performance
when parameters changed. For further research, this lexicon
was used as an input for machine learning algorithms, providing
insights into the depressive status of test subjects, so as toimprove detection accuracy. According to our research, thedetection models with lexicon features outperformed the models
without lexicon features by 2% to 9% in terms of evaluation
scores.
The main contributions are as follows: (1) We extracted a setof depressive words and constructed our domain lexicon in
Chinese, which is a good contribution to web-related depression
signal detection, to assist in identifying users who have thepotential to experience depression in an early period. We applied
an efficient semisupervised automatic construction method in
the depression domain. The lexicon was proven to be meaningful
in several detecting classification models in our study; (2) We
constructed a benchmark depression data set (some of the data
were used to construct the lexicon [our main research objective]
and the other data were used in the detection test) based on
microblogs, which could assist in further depression detection,
diagnosis, and analysis. Meanwhile, we released the data set
and lexicon together [15] to facilitate future web-related
depression diagnosis.

Related Work About the Traditional Approach for
Depression Detection
For decades, there have been many ways to detect depression.
Beck [16] created the original Beck Depression Inventory fora quick self-testing measure that can briefly assess recent
depression symptoms. Thereafter, Beck et al [17] updated theapproach to Beck Depression Inventory II that can assess theseverity of self-reported depression symptoms by paper orelectronic format. Radloff [18] developed the Center for
Epidemiologic Studies Depression Scale, which focuses moreon the individual’s emotional experience and less on the physical
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Li et alcondition. Some other popular scales are the Zung Self-rating
Depression Scale [19] and Hamilton Depression Rating Scale[20].

developments. We can see the feasibility of detecting depression
on social media with a lexicon, and more efforts are needed toconstruct a better Chinese depression-domain lexicon.

Since the 21st century, new scales are continuously being
improved. Diagnostic and Statistical Manual of Mental
Disorders (DSM-IV) is a standard classification of mental
disorders used by mental health professionals, which was
improved by Hu [21]. In China, the Chinese Classification andDiagnosis of Mental Diseases 3rd edition (CCMD-3) is astandard for diagnosis.

Related Work About Research on Construction of a
Domain Lexicon

Overall, traditional ways of depression detection have beenhighly validated and accepted in the real world for decades.
However, they mainly rely on the scores of scales orquestionnaires, face-to-face interviews, and self-reports, andoften require a lot of labor and time [6]. The new trend might
be related to big data that are timely, rich, and easily accessible
on social networking sites like Facebook, Twitter, and Weibo.
These web-based methods can assist in large-scale early
detection, and experts can further conduct more precise
diagnosis and treatment.

Related Work About Depression Detection on Social
Media
In recent years, with abundant data on social media, someresearchers are attempting to detect depression by leveraging
web-based data. Park et al [8] explored the use of depressive
language from Twitter users and concluded that social networks
can provide meaningful data for capturing depressive moods.
Choudhury et al [9] were the first to diagnose and predict
depression via social media by extracting several features. Hasan
et al [10] proposed a new method with the circumplex model
to classify Twitter messages as depressed, happy, or otheremotions. Resnik et al [11] explored the use of supervised topic
models in an analysis of linguistic signals for detecting
depression. During such research, word-based features are ofgreat importance on social media [14].
Word-based features could be shown in a lexicon. Tsugawa etal [12] utilized positive and negative sentiment words in a
lexicon for recognizing depression. Choudhury et al [9] usedsemantic orientation pointwise mutual information (SO-PMI)and term frequency-inverse documentation frequency (TF-IDF)
to extract a depression lexicon from “mental health” in Yahoo!
Answers and set the Wikipedia page on “list of antidepressants”
as antidepressant words. Most recently, Guangyao et al [13]employed Word2Vec (W2V) to extract words of antidepressantsand depression symptoms from Twitter as a domain-specificlexicon.
Many previous studies achieved a lot with regard to a depression
lexicon, which can greatly help in diagnosis; however, most of
the words are in English. It is not proper to use the translated
version of an English lexicon to detect depression in a Chinese
corpus because of cultural differences. In addition, only PMI(mainly about co-occurrence frequency) and W2V (wordembeddings) techniques cannot keep up with today’s semantic

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Many methods have been used to efficiently construct a domain
lexicon. Das et al [22] and Krestel and Siersdorfer [23] used
SO-PMI as a useful tool for emotion lexicon construction. Yu
and Dredze [24], Tixier et al [25], and Zhengyu [26] leveraged
and improved the W2V method to construct a domain lexicon.
Chao et al [27] proposed a semisupervised sentiment orientation
classification algorithm based on W2V (SO-W2V) and obtained
a lexicon in different areas efficiently. The PMI method focuseson the co-occurrence frequency between words but ignores thecontext. However, W2V considers context with wordembeddings but in a relatively simple way compared with the
LPA shown below.
The LPA, which was first proposed by Zhu and Ghahramaniy[28], plays an important role in lexicon autoconstruction withsemisupervised methods. Researchers [29,30] used the LPA
starting with several labeled seed words to expand a lexicon forpolarity classification. Tai and Kao [31] built a framework toautomatically generate a lexicon by combining PMI and the
LPA. Hamilton et al [32] applied a label propagation frameworkwith domain-specific word embeddings to construct accurate
domain-specific lexicons. A new method combining W2V and
LPA was adopted by Giulianelli [33] and Pu et al [34], and it
performed much better than previous methods. In this way, therelationships between words and specific domain contexts are
considered.

Data Collection
In order to build a depression-domain lexicon for further
detection via social media, we constructed two data sets of users
with depression and without depression based on data from
Weibo microblogs, which is very popular in China. Weibo has
462 million monthly active users according to a report in 2018[35], and it is the most popular social media website in China.
Equivalent with Twitter, people are getting used to sharing theirideas and moods on Weibo.
In light of the fact that depression is a long-standing illness, thetext of users should not be collected from only one microblog.
Thus, our data sets contained all Weibo microblogs within ayear published by the same users. In addition, personal profile
information like comments, number of follows, and number offollowers was also included.

Depressed Data Set D1
Based on Weibo microblogs from January 2017 to April 2018,
we used the keywords “I’m diagnosed with depression”[13,36,37] to construct a depressed data set D1. In this way, we
finally identified 965 users with depression and 58,265
microblogs (Table 1).

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Li et alTable 1. Details of the collected data sets from Weibo microblogs.
Data set

Users

Total posts

MeanStandard deviation

Skewness

Kurtosis

Time span

Depressed data set D1

965

58,26560.37431.327−0.451

1.788

January 2017-April 2018
(16 months)

Nondepressed data set
D2

903

52,78763.69730.086−0.615

2.066

January 2017-April 2018
(16 months)

Nondepressed Data Set D2
If a user never posted any text with a depression-related word
like “depress,” the user was labeled as nondepressed. In thisway, we constructed a nondepressed data set D2. To match D1,
we selected a similar number of microblogs (one user without
depression can have up to 100 posts) under the same time span.
In this way, we identified 903 users without depression and
52,787 microblogs (Table 1).

Data Preprocessing
Before the experiment, we found that there were some unrelated
microblogs, irregular words, and emoji in our data sets. These
noisy texts can affect the accuracy of our model, so we adopted
the following preprocessing procedures: (1) Emoji processing.
Emoji and emoticons are common in social media. However,they can cause some unexpected troubles like encoding problems
during algorithm running and text analysis, so we removed
emoji. We will take them into account separately in further
research; (2) Unrelatedmicroblog processing. In addition todepression-domain microblogs that we focused on mostly, manyusers posted plenty of daily microblogs, including red packets
snatching, game sharing, advertisements, etc. In addition, some“fake” depression microblogs like depression scientific articles
and content talking about friends with depression, instead ofusers, are also useless and can be misleading. By manual
screening, we obtained a list of unrelated keywords in daily
microblogs and “fake” microblogs, and then, we removed them
all with regular expression; (3) Irregular words preprocessing.
New words keep appearing, and language habits are quite
different on the internet. These cause trouble during text
analysis. Therefore, we added a general dictionary of internet
words.

Methods
Construction Overview
Domain adaptability is always a difficult problem in natural
language processing. Therefore, a domain-based lexicon canhelp us perform analysis in a more accurate and deeper way.

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For example, “excitement,” “life,” and “forever” are common
words in our daily life, but they can be abnormal signals of apatient with depression. Thus, through our study, we will try
our best to determine which words used on the internet indicate
depression and which do not indicate depression.
There are many ways to construct a domain-based lexicon
according to a survey [38], which can mainly be divided into
knowledge base, corpus base, and these two combined.
Knowledge base includes traditional methods, such as word
relation extension [39] and annotation extension [40]. Corpus
base refers to conjunction syntax [41] and word co-occurrence[42]. In fact, a survey [38] showed that the class of methods
adopted more widely is the automatic method combining
existing knowledge and corpus base. In this view, approaches
involving semisupervised construction on relationship graphs
like the LPA, bootstrapping, and word embedding are popularand effective methods mentioned in the subsection Related
Work About Research on Construction of a Domain Lexicon.
Inspired by Hamilton [20], Giulianelli [33], and Pu et al [34],
we applied automatic construction, a semisupervised methodthat combines W2V and LPA on a lexical semantic relationshipgraph, to obtain a depression-domain lexicon containing
depressed feature words. Our construction can be divided into
the following four steps: (1) Extraction of seed words. Extracta few words that are most important and valuable in the domain;(2) Extending new words. Use the W2V model to learn word
vectors on the corpus and then extend with similarity between
seeds and new words; (3) Setting labels for the new words. If
the cosine similarity of a word and a seed is greater than thethreshold, an edge will be formed, and the weight of the edge
is the similarity. Through such iteration, a graph is obtained.
After that, the LPA is used on the semantic graph to obtain thelabels of all candidate words; (4) Obtaining the domain-based
lexicon. Finally, by simple manual arrangement, thedepression-domain lexicon is formed. We then used it as an
input for detection models and found that the models performedmuch better than before. The method is described as a detailed
framework in Figure 1.

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Li et alFigure 1. An illustration of the framework. DT: decision tree; LR: logistic regression; NB: naive Bayes; RF: random forest; SVM: support vector
machine; TF-IDF: term frequency-inverse documentation frequency.

Extraction of Seed Words
Seed words are those that can be representative of a specificdomain. In order to extract the key seed words in the depressedand nondepressed data sets, we leveraged the TF-IDF algorithm,
which is a widely used feature extraction algorithm in natural
language processing. Salton and Yu [43] first proposed the
TF-IDF algorithm, and Salton et al [44] demonstrated its validity
in information retrieval. Term frequency (TF) refers to thenumber of times a term or word occurs in a document, andinverse document frequency (IDF) is related to the frequency
of a term appearing in all documents, which measures specificityof the term over the entire corpus.
TF and IDF can be formulated as follows:then extracted words with the highest TF-IDF values in our
corpora.

Extending New Words With W2V
Now that we had the seeds S, we could leverage the wordembedding model to extend new words. Word embeddings,
which help map the vocabulary to vectors, are popular tools fornatural language processing. We adopted W2V, an efficientalgorithm for learning embeddings using a neural language
model, to generate the vectors. W2V is an open source modelby Mikolov et al [45] at Google, and its main idea is to use deeplearning technology on a specific corpus and then to map eachword into a multidimensional real vector space, where thedistance between words that have a higher semantic similarity
is small.
In this paper, cosine similarity was used to calculate thesimilarity between words. When a word whose similarity withthe seed words in the training corpus was greater than the given
threshold, we extracted it as a new word and added it as a
candidate word to the candidate word set C. If Si and Cj
represent the vectors of a seed word and candidate word,
respectively, the similarity between them can be formulated by
SIM(Si, Cj) as follows:

where ni,j is the word i in document j, k is the number of words
in j, N is the number of documents containing word i, D is thesize of the documents, and DF(i) is the number of documents
in which the word i occurs at least once. Additionally, tfidf canbe formulated as follows:

Setting Labels With Label Propagation

Intuitively, this calculation of TF-IDF will show us how
important and special a given word is in our depression domain.
Words with a higher tfidf value tend to have a greater relevancein a document. In our research, we regarded the data sets D1
and D2 as two corpora and every microblog as a document. We
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The LPA is a common semisupervised approach on a graph[28]. It has been applied to many fields, such as community
detection [46], personal social relation extraction [47], anddictionary construction. Using a graph model to construct alexicon can capture the global relations among all words,
overcome the dependence on seeds, and provide a better resultin the case of limited labeled data.

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The LPA builds a graph based on the similarity between nodes,
which are the words in our study. After the initialization of thegraph, the nodes in the graph can be divided into labeled nodes
and unknown nodes. The basic idea of LPA is to predict thelabel of unknown nodes based on information from labeled
ones, and labels are propagated mainly by the weight on theedge between the nodes. In the process of label propagation,
unknown nodes can update their own labels through theinformation of adjacent known labels. If the similarity of theadjacent node is large, the influence of its label will be large.
In our algorithm, the seeds S are taken as the labeled nodes andthe extended candidate words C are taken as the unknown nodes.
The semantic graph is constructed as follows: If the seed word
i is extended by W2V to get a new word j, there is an edge
between i and j, and the weight of the edge is the similarity ofthe two words. Thus, all of the seed words and candidate words
will form a semantic graph as shown in Figure 2.
Assuming that there are n nodes in total, then an n-dimensionaltransition probability matrix can be constructed. Let SIM(wi,wj)
represent the similarity between wi and wj, which is calculated
by cosine similarity. T[i][j]represents the similarity transfer
probability from word i to word j, which is calculated as follows:

Li et alseed word with the label “+1,” and the labels of the rest of thecandidate words are unknown (given an initial value of 0), theinitial labels of all nodes can be represented by the vector V asfollows:

The label of each unknown candidate word is obtained by
iteratively applying the transition matrix on the initial labels of
the words. The calculation method is as follows:

where Label represents the label possibility of node j after theiteration, T[i][j] represents the transfer probability in thesimilarity matrix of node i to node j, and V[i] represents theinitial Label of node i before the iteration.
In each iteration, the labels of the seeds should remain the same.
When the labels of all words in the graph no longer change after
continuous iteration, the iteration is over. At the end of theiteration process, the final candidate words are those words
whose absolute value of label probability is greater than a certain
threshold. In this way, we obtained a well-labeled domain
lexicon. The previous algorithms can be concluded as the steps
in Textbox 1.

If there are 10 nodes in the graph, in which i1 and i2 aredepression seed words with the label “−1,” i3is a nondepression
Figure 2. A simple structure of a semantic graph. i: seed word; j: candidate word.

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Li et alTextbox 1. Algorithms of the procedure.
Input: Corpus of data set (Corpus=D1∪D2), seeds S, and the threshold Tc for candidate words C.
Output: One depression-domain lexicon L with depressive words Ld and nondepressive words Ln.
Procedure:
1) Initialize the lexicon and candidate words. C=∅, Ld=∅, Ln=∅.
2) Preprocess the corpus and learn the word embedding with Word2Vec.
3) For every seed, Si∈S:
For a word Wj in Corpus, if SIM(Si,Wj) ≥Tc, then C=C∪Si∪Wj. Record the similarity calculated by equation (4).
4) After obtaining all the extended candidate words C and the similarity matrix between words through step 3), the transition probability matrix ofsimilarity in C can be constructed according to equation (5). Then, the semantic relationship graph is obtained.
5) In the whole graph, Label of unknown words is calculated according to formula (7) given the initial label V.
6) Reset the labels of the seeds in Label to its initial value. Then, let V=Label.
7) Repeat steps 5) and 6) until the labels of C in the graph do not change anymore.
8) Obtain the final Label. For Ci∈C, if LabelCi <0 and |LabelCi| >0.5, then Ld=Ld∪Ci; For Cj∈C, if LabelCj >0 and |LabelCj| >0.5, then Ln=Ln∪Cj.
9) Combine Ld and Ln and finally obtain the depression-domain lexicon L after manual work.

After our lexicon was automatically built, we labeled it
depressed or nondepressed for further evaluation. Three
volunteers, who had carefully read the depressed microblogs
and research articles, were invited to perform the lexiconlabeling job [48]. Thus, every word in the lexicon was labeledthree times. If there was a labeling disagreement, voting was
adopted to obtain the ground truth.

depression dictionary in public resources. To solve thesegmentation problem, we downloaded “Dictionary of
Psychology” and “Dictionary of Neuropsychiatry” from the
CNKI Tool library [50] (there is no depression lexicon yet, so
we chose the dictionary of psychology and psychiatry; CNKI
is one of the largest Chinese knowledge discovery web-based
platforms), downloaded “Weibo Dictionary” from BosonNLP[51] (a dictionary automatically constructed from millions ofannotation data points from microblogs, forums, and other data
sources), and used a manually collected antidepressantdictionary [26] (words like amitriptyline and paroxetine in ourdata sets were replaced with antidepressant as a data reduction
method) from web-based pharmacies and science articles. The
work of Chinese domain word segmentation was inspired by
Fang [26] and Cheng [49]. The final domain dictionary
contained 122,594 words after eliminating duplicate words. We
then used jieba (built to be the best Python Chinese word
segmentation module) [52] as our segmentation module, which
adopted the unigram model and hidden Markov model.

Word Segmentation

Large Word Embedding

Chinese word segmentation has a great influence in lexicon
construction, especially when it comes to Weibo microblogs
and the depression domain. In order to segment Chinese words
properly in Weibo text, we used the following three steps tosegment words as accurately as possible: (1) domain dictionary;(2) large word embedding; and (3) incorrect word removal.

A richer corpus is associated with more precise wordembedding. Instead of using our collected data, which wererelatively rare, we leveraged the W2V models by Shen et al[53], which are trained on 5 million Weibo microblogs and 223
million Chinese Wiki tokens, for word embeddings.

Domain Dictionary

We planned to remove incorrect words from our lexicon.
Actually, after evaluation, we found that the error rate was lessthan 2% to 3%. Among 2385 words in our depression-domain
lexicon, there were 64 errors.

Results
Experiment Setup
We employed our data set to construct a depression-domain
lexicon. We needed two types of microblogs combining users
with depression or those without depression to extract domain
seed words and to finish the automatic construction with the
LPA. Our original data crawled from Weibo had some noise,
especially in D1, so manual preprocessing (detailed description
in the “Data Preprocessing” subsection) was necessary to clean
the data into D1 and D2.

When coping with mental disease, especially depression, overthe internet, some depression-domain words like paroxetine(“帕罗西汀”), which is a common antidepressant, and self-rating
scale (“自评量表”), which is a tool for individuals to measure
depression, were difficult to recognize. Other words like MLGB(“马勒戈壁”), which means damn it, and Yali (“鸭梨”), whichmeans pressure, were network vocabularies that could be
confusing for the computer. Domain-specific segmentation
should combine a domain dictionary [49]; however, there is no
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Incorrect Word Removal

Evaluation Metrics
During our experiments, we constructed the depression-domain
lexicon with an automatic method, compared our method withsome baseline approaches, and analyzed key parameters likenumber of seeds and threshold in the model.
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For the evaluation metrics, we employed precision, recall, andF1 measure (F1) in equations (8), (9) and (10), respectively, toevaluate the performance of our model and the baseline
approaches. We used area under the curve (AUC) to evaluate
the model of unbalanced data. In terms of the number of words
in the lexicon, we also compared the numbers under different
circumstances. The equations are as follows:

Li et al
where TP represents true positive, which means depressed words
are correctly detected as depressed; FN is false negative, whichmeans depressed words are incorrectly determined as
nondepressed; and FP is false positive, which means that
nondepressed words are incorrectly detected as depressed.
Figure 1 provides an entire picture of the experiment.

Seed Words
Before construction, we used the TF-IDF to extract the seedwords and obtained a list of the top 2000 words. The samplesof the TF-IDF of D1 are shown in Table 2.
By artificially screening the list, we could obtain some seedwords. Moreover, we added a few general sentiment words withhigh levels to our seed words and finally obtained a set of seedwords of 40 depressive seeds and 40 nondepressive seeds. Fromparameter sensitivity analysis, we noted that 80 seeds in total
will lead to a sufficiently large lexicon with high accuracy. The
samples of the 80 seeds are shown in Table 3.

Table 2. TF-IDF values of depressed D1 samples.

aDepressed D1

TF-IDFa value

Myself (自己)0.041383

Really (真的)0.032475

Depression (抑郁症)

0.024328

Hope (希望)0.013336

Life (生活)0.012043

Forever (永远)0.006965

Pain (痛苦)0.006871

Sad (难过)0.006756

Live (活着)0.006583

Mood (心情)

0.006386

Night (晚上)0.006347

Always (总是)0.005984

Hate (讨厌)0.005475

Exhausted (好累)0.005469

Fear (害怕)

0.005030

Lonely (孤独)0.004413

Idiot (傻逼)0.004380

Emotion (感情)0.004031

Insomnia (失眠)0.003950

Sorry (对不起)0.003867

Despair (绝望)0.003410

Antidepressant (抗抑郁药)0.002305

TF-IDF: term frequency-inverse documentation frequency.

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Li et alTable 3. Summary of the seeds.
Category

Seeds S

Nondepressive (40 words)

Stability, comfort, happy, happiness, successful, confidence, sunshine, struggle, positive, brave, enjoy, peace, enthusiasm,
healthy, satisfied, active, grow up, pride, good, admire, strong, perfect, praise, precious, progress, congratulate, love,
welcome, kindness, robust, earnest, agree, support, award, advantage, good deal, develop, warm, bright colored, andunderstand(稳定, 舒服, 高兴, 幸福, 顺利, 自信, 阳光, 奋斗, 积极, 勇敢, 享受, 平安, 热情, 健康, 满意, 活力, 成长, 骄傲, 优秀,敬佩, 完美, 称赞, 强大, 珍贵, 进步, 庆贺, 关爱, 欢迎, 强壮, 善良, 认真, 同意, 支持, 奖励, 优势, 划算, 发展, 温暖,鲜艳, 明白)Depressive (40 words)

Depression, collapse, stress, suicide, apastia, anxious, sad, tired, death, lonely, insomnia, bad, desperate, give up, low,leave, fear, danger, close, sensitive, lost, shadow, destroy, suspect, crash, dark, helpless, guilt, negative, frustration,
nervous, melancholy, rubbish, jump, forget, goodbye, cut wrist, edge, haze, and antidepressant(抑郁, 崩溃, 压力, 自杀, 绝食, 焦躁, 伤心, 疲惫, 死亡, 孤独, 失眠, 难受, 绝望, 放弃, 卑微, 离开, 恐惧, 危险, 封闭,
敏感, 茫然, 阴影, 摧毁, 怀疑, 崩塌, 黑暗, 无助, 愧疚, 负面, 沮丧, 紧张, 忧郁, 废物, 跳楼, 遗忘, 再见, 割腕, 边缘,阴霾, 抗抑郁药)Model Evaluation
In order to verify the effectiveness of the lexicon
autoconstruction method applied in this paper, we selected thefollowing methods as baseline approaches: (1) W2V [24-26]. A
common method of constructing a lexicon based on W2V, which
is used to learn word embedding vectors on a corpus. The
semantic similarity between words and seed words in the corpus
is then iteratively calculated. If the similarity is greater than acertain threshold, the new word is extended and has the samelabel as the seed word; (2) SO-W2V [27]. It is a semisupervised
sentiment orientation classification algorithm based on a word
vector. The basic idea is that through comparison with all
positive and negative seed words, an accurate orientation of theextended word will be obtained. It has versatility in different
areas for a Chinese corpus; (3) SO-PMI [9,22,23]. It calculates
the probability of the occurrence of both seed words andexpanded words in the text. A higher probability is associatedwith a closer correlation; (4) W2V-LPA. It is our method, which
considers both the word relationship and specific domain
context.

To obtain a fair comparison, we set the same parameters for all
methods where Tc was 0.5 and the size of seeds S was 80. For
W2V tools, we used the gensim package [54].
From Table 4 and Figure 3, we can see the evaluation results.
It is obvious that the W2V-LPA and W2V methods performedmuch better than the SO-W2V and SO-PMI methods. Moreover,when the size of seeds increased from 60 to 120, our method
was able to maintain a more stable and precise performance,
which was almost 1% to 6% higher than others (Figure 3),
whereas the value for SO-W2V declined quickly when the sizeof seeds became larger. Overall, SO-W2V takes all the otherseeds into account, but too many seeds combined will introduce
too much noise to some extent, as not all seeds are related toan extended word. W2V is a simple and general method, which
only considers the label of the first seed when extending new
words. Additionally, SO-PMI mainly takes word co-occurrence
frequency into account. What W2V-LPA did better is that it
only predicted labels through the semantic graph of related andsimilar words, and thus, the semantic context and word relation
were both considered. Therefore, we can say that W2V-LPA isa much better and more stable method for the autoconstructionof a domain lexicon.

Table 4. Performance of lexicon construction methods.

aConstruction method

Precision

RecallF1

Size of the lexicon

W2V-LPAa

0.880

0.906

0.893

2321

W2Vb

0.878

0.903

0.890

2321

SO-PMIc

0.879

0.877

0.877

2024

SO-W2Vd

0.854

0.877

0.862

2321

W2V-LPA: label propagation algorithm-Word2Vec.

b

W2V: Word2Vec.

c

SO-PMI: semantic orientation pointwise mutual information.

dSO-W2V: semantic orientation Word2Vec.

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Li et alFigure 3. F1 of methods when the seed size changed. LPA: label propagation algorithm; SO-PMI: semantic orientation pointwise mutual information;
SO-W2V: semantic orientation from Word2Vec; W2V: Word2Vec.

Parameter Sensitivity Analysis
Throughout our experiment, the size of seeds S and the extension
threshold Tc were two important parameters. More seeds or alower threshold will lead to a lexicon with more words but lower
accuracy, whereas fewer seeds and a high threshold will ensure
more precision but a poor lexicon. We balanced the trade-offs,
as we wanted to obtain a relatively accurate and abundant
lexicon that would be helpful for further depression diagnosis.
Figure 4 presents the size of the lexicon when the size of seeds
and threshold for candidate words changed.
First, we fixed Tc at 0.7 and then varied the size of seeds from60 to 120. If we have less than 60 seeds, the entire lexicon will
be so small that almost nothing will remain but seed words. A
size larger than 60 will not change the outcome, so 0.7 might
be a very high-level threshold. From Table 5, we can see that

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larger sizes of seeds like 100 and 120 partially jeopardized theperformance, and W2V-LPA performed nearly the same when
the sizes were 60 and 80.
We then fixed the size of seeds at 80 with varying Tc from 0.7
to 0.5. With a higher threshold, the performance was relatively
excellent, whereas the size of the lexicon started to fail at around1000 when Tc was 0.55. We believe a lexicon with 2000 words
and a Tc of 0.5 might have good balance.
Overall, it is pleasing that our W2V-LPA method performed
quite smoothly and steadily even when the parameters werechanged, so we believe that a high-quality lexicon can beconstructed. It is difficult to find an optimal solution, and given
D1 and D2, we will adopt a size of seeds of 80 and a threshold
Tc of 0.5 as a relatively proper approach.

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JMIR MEDICAL INFORMATICS

Li et alFigure 4. Size of the lexicon when the size of seeds and threshold for candidate words changed.

Table 5. Performance of the W2V-LPA method when S and Tc were changed.

a

Sa

Tc b

Precision

RecallF1

Size of the lexicon

60

0.5

0.882

0.911

0.896

1694

60

0.55

0.910

0.935

0.922

788

60

0.6

0.926

0.944

0.935

446

60

0.65

0.951

0.963

0.954

275

60

0.7

0.804

0.897

0.848

89

80

0.5

0.880

0.906

0.893

2321

80

0.55

0.916

0.937

0.926

1072

80

0.6

0.934

0.948

0.941

558

80

0.65

0.954

0.963

0.958

320

80

0.7

0.918

0.909

0.892

113

100

0.5

0.874

0.899

0.886

3070

100

0.55

0.906

0.924

0.915

1589

100

0.6

0.927

0.937

0.931

792

100

0.65

0.953

0.959

0.955

418

100

0.7

0.937

0.932

0.925

144

120

0.5

0.855

0.879

0.866

3696

120

0.55

0.889

0.904

0.896

1942

120

0.6

0.924

0.933

0.928

894

120

0.65

0.952

0.958

0.954

454

120

0.7

0.944

0.940

0.934

170

S: size of the seeds.

b

Tc: threshold for candidate words.

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JMIR MEDICAL INFORMATICS

Li et alDetection Performance

Data Collection

After construction of the depression-domain lexicon, we could
apply it to actual depression detection in a new Weibo microblog
data set to find out if our work would help existing detection
models perform better. The detection process included datacollection, feature selection, and classification methods.

In addition to our data set used for lexicon construction, wecollected 745 users who were depressed and 10,118 users who
were not depressed with their 1-year tweets as a new data set.
Data details are shown in Table 6.

Table 6. Details of the data set for depression detection.
Data set

Users

Total posts

MeanStandard deviation

Skewness

Kurtosis

Time span

Depressed data set D3

745

179,600

240.44486.28

6.21

56.32

January
2018-June2019 (18months)Nondepressed data set D4

10,1183,150,000

310.93327.72

3.50

48.52

January
2018-June2019(18 months)

Feature Selection
Features like topic-level keywords, posting behaviors, numberof tweets, first-person words, and linguistic style are meaningful
in detecting depression on the internet [11,13]. We also set ourdepression-domain lexicon as one feature to see whether it
would really contribute a lot after inclusion in the detection
model. The features were as follows: (1) Topic-level keywords.
We selected 30 topic-level keywords with the TF-IDF; (2)Posting behaviors. For each user, average length of tweets andtotal posting numbers were collected to represent web-related
posting behaviors; (3) First-person words. According tolinguistic inquiry and word count [55], we counted the numberof first-person pronouns like I, we, us, etc; (4) Linguistic style(200 dimensions). To approximately analyze linguistic style,we calculated the average vectors of every user with Word2Vec[56]. Finally, we constructed the depression-domain lexicon by
the previously mentioned process.

Classification Methods
We chose naive Bayes (NB), decision tree, logistic regression(LR), random forest, and support vector machine [5,37] asclassification methods to detect users with depression. Frommodel performance, we obtained a quick picture about theimportance of our lexicon. When the depression-domain lexicon

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is selected as one feature, the method has the tag L. For example,
L-NB is a classification algorithm that has the feature of thedepression-domain lexicon, whereas NB does not have thisfeature. After including the depression-domain lexicon in themodels, we clearly found that each detection performance
improved when compared with before inclusion of the lexicon(Table 7). The performance of lexicon methods surpassed thatof corresponding methods without the lexicon by 2% to 9%,
which justifies the important role of our lexicon in depression
detection.
The model was based on a data set with 50% users who weredepressed and 50% users who were not depressed. When wevaried the scale of depressed users, the data set became
imbalanced and the AUC was more important to test theperformance. Figure 5 illustrates the trend of detecting
performance when setting different proportions of users who
were depressed in the L-LR method. This method achieved an
outstanding performance when the proportion of users withdepression was 50%. However, the AUC dropped sharply when
the data set was imbalanced.
In the real word, people with depression make up less than 10%of the population, and we will determine how to properly detect
depression with imbalanced data in a further study.

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JMIR MEDICAL INFORMATICS

Li et alTable 7. Detection model performance with the depression-domain lexicon.

aDetection model

Precision

RecallF1

Accuracy

NBa

67%

67%

67%

67%

Lb-NB

74%

73%

73%

73%

LRc

76%

76%

75%

76%

L-LR

77%

77%

77%

77%

RFd

68%

68%

68%

68%

L-RF

77%

77%

76%

77%

SVMe

65%

65%

65%

65%

L-SVM

74%

72%

72%

72%

DTf

67%

67%

67%

67%

L-DT

69%

69%

69%

69%

NB: naive Bayes.

b

L: depression-domain lexicon as a feature.

c

LR: logistic regression.

d

RF: random forest.

e

SVM: support vector machine.

f

DT: decision tree.

Figure 5. Scales of users who were depressed. AUC: area under the curve.

Discussion
Diagnosis of users with potential depression via social media
has attracted increasing attention because it is a morecost-effective and active approach dealing with massive valuable
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data than traditional diagnosis. In previous studies, most of theachievements about a lexicon involved an English corpus.
Instead of translating an English lexicon, this paper aimed toapply an automatic construction method for a Chinese
depression-domain lexicon based on the LPA. With Word2Vec
and a semantic relationship graph, the LPA was used to predict
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JMIR MEDICAL INFORMATICS
the label of candidate words in the graph, and finally, our lexicon
was constructed. Experiment results showed that our method
was superior to baseline construction methods and had good
performance and robustness. In addition, when our lexicon was
included as an input for the detection models, their performance
became more accurate and effective when compared with themodels without the depression-domain lexicon.
In the next step, experiments are expected to be carried out ona larger depression corpus, and more linguistic knowledge like

Li et alconjunction will be incorporated into our method to enlarge therange of the depression-domain lexicon. Meanwhile, more
complex construction methods like deep neural networks andhierarchical topic models will be adopted in further research.
We expect that our lexicon will act as a useful feature in
depression detection and will be able to provide more insights
for depression diagnosis in terms of advanced depression
detection among patients.

Acknowledgments
GL led the method application, experiment conduction, and result analysis. LH and SH participated in data extraction, preprocessing,and manuscript revision. BL provided theoretical guidance and revised the paper. This work was supported by the National Social
Science Fund Project, China (No. 16BTQ065) “Multi-source intelligence fusion research on emergencies in big data environment”
and the Foundation for Disciplinary Development of the School of Information and Technology in the University of International
Business and Economics.

Conflicts of Interest
None declared.

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Abbreviations
DT: decision tree
LPA: label propagation algorithm
LR: logistic regression
NB: naive Bayes
RF: random forest
SO-PMI: semantic orientation pointwise mutual information
SO-W2V: semantic orientation from Word2Vec
SVM: support vector machine
TF-IDF: term frequency-inverse documentation frequency
W2V: Word2Vec

Edited by T Hao, B Tang; submitted 31.12.19; peer-reviewed by Z Zhang, D Carvalho; comments to author 20.02.20; revised version
received 11.04.20; accepted 05.05.20; published 23.06.20
Please cite as:
Li G, Li B, Huang L, Hou S
Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining StudyJMIR Med Inform 2020;8(6):e17650
URL: http://medinform.jmir.org/2020/6/e17650/
doi: 10.2196/17650
PMID: 32574151

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23.06.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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