Text Analysis of User-Generated Contents for Health-care
Applications
Case Study on Smoking Status Classification
Deema Abdal Hafeth, Amr Ahmed and David Cobham
School of Computer Science, University of Lincoln, Lincoln, U.K.
Keywords: Smoking Status Classification, Text Mining, User-Generated Contents.
Abstract: Text mining techniques have demonstrated a potential to unlock significant patient health information from
unstructured text. However, most of the published work has been done using clinical reports, which are
difficult to access due to patient confidentiality. In this paper, we present an investigation of text analysis for
smoking status classification from User-Generated Contents (UGC), such as online forum discussions. UGC
are more widely available, compared to clinical reports. Based on analyzing the properties of UGC, we
propose the use of Linguistic Inquiry Word Count (LIWC) an approach being used for the first time for such
a health-related task. We also explore various factors that affect the classification performance. The
experimental results and evaluation indicate that the forum classification performs well with the proposed
features. It has achieved an accuracy of up to 75% for smoking status prediction. Furthermore, the utilized
features set is compact (88 features only) and independent of the dataset size.
1 INTRODUCTION
The increasing availability of text collections allows
researchers to apply text classification for predictive
purposes relating to topics, opinions, moods,
diseases and personalities. One of the active research
areas in text classification is that of smoking status
classification. Smoking status identification is the
process of discovering, or distinguishing, the
smoking status of the author of a given particular
text from a set of predefined categories. Interest in
automatic smoking status classification started in
2005 particularly for clinical records. This type of
data is difficult to obtain, due to the lengthy
approvals process that protect patient confidentiality.
In addition, prior to this study no specific features
have been identified as being a standard for such a
classification. These issues pose additional
challenges for a researcher undertaking further
studies using this more traditional source of data.
Web 2.0 has facilitated many forms of interactive
collaboration mediated over the internet. In addition,
people consider themselves to be more informed and
empowered and find it a supportive environment as
they can exchange information and experiences from
others in the same situation and receive emotional
support from them. This new medium is also
beneficial for health professionals, as it offers
exciting new research avenues with regard to
theories of psycho-social support and how people
manage their conditions. In this paper, the source
data is derived from online forum discussions a
source that is more widely and readily available than
other types of text such as clinical reports.
In this paper, we present an investigation of
smoking status classification from UGC, and
introduce more relevant feature sets. The technique
used in this paper utilizes the LIWC dictionary
(Linguistic inquiry and word count, October 2013).
This selection is based on the properties observed in
the UGC data, as discussed in Section 3.
The rest of the paper is organized as follows:
Section 2 presents the key work relevant to this
investigation, The properties of forums are studied
and discussed in Section 3, then the proposed
features set is introduced in Section 4. The proposed
classification framework is discussed in Section 5,
followed by the experimentation setup, results and
evaluation in Section 6. Finally, the paper is
concluded in Section 7.
242
Abdal Hafeth D., Ahmed A. and Cobham D..
Text Analysis of User-Generated Contents for Health-care Applications - Case Study on Smoking Status Classification.
DOI: 10.5220/0005080502420249
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2014), pages 242-249
ISBN: 978-989-758-048-2
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2 RELATED WORKS
Automated classification of documents is one of the
common tasks in text analysis and Natural Language
Processing. Examples of applications include opinions
classification (Kaiser and Bodendorf, 2012; Pang et al.,
2002), and mood inference (Leshed and Kaye, 2006).
In the smoking status inferring field, the i2b2 (the
Informatics for Integrating Biology and the Bedside)
challenge was designed and facilitated by the National
Centre for Biomedical Computing (Informatics for
integrating biology and the bedside, October 2013).
The challenge required participants to explore text
analysis for the automatic classification of patients in
relation to their smoking status, based on clinical
reports. The challenge focused on text analysis as a
powerful tool to classify clinical records and detect a
patient’s smoking status. Different methods were
proposed to achieve this task (Uzuner et al., 2008). We
review the work that is most closely related to our
research.
The earliest smoking status classification research
was done by Sordo and Zeng (Sordo and Zeng, 2005),
when they classified clinical reports to find the effect of
training set size on classification results. They found
that the size of the training set and the classification
accuracy are in fact correlated. This method classified
clinical reports into one of four smoking statuses
(current smoker, past smoker, never smoker, denies
smoking) by using Support Vector Machine (SVM),
Naïve Bayes (NB) and other machine learning
classifiers. In this research, word frequency of unigram
and bi-gram were used as features. The approach
achieved results of considerable accuracy (86.8%) and
concluded that the SVM algorithm is more suitable for
classifying larger corpora for smoking status
classification.
Clark et al (Clark et al., 2008) developed the best
performing system in the i2b2 challenge. They used the
binary presence of unigram and bigram word features
of document with SVM classifier algorithm and 10
cross-validation techniques. This method achieved an
accuracy of 82%. The system performance was then
improved by using additional clinical report data and
filtering unrelated smoking sentences before
classification.
Supervised and unsupervised methods were
suggested by Pedersen (Pedersen, 2006) for predicting
the smoking status of patients from clinical reports.
This involved testing a number of learning classifiers
for supervised approaches such as SVM and NB.
Furthermore, the frequencies of unigram, bigram and
trigram words that appeared in the training set were
used as features. The classification method was
achieved accuracy of 82%.
In this research, we differ from previous methods in
the type of data (UGC) and the relevant features to be
used in the smoking status prediction. This is expanded
upon in Sections3 and 4.
On the other hand, a rule based system to infer
patient smoking status was developed (Wicentowski
and Sydes, 2008). It removed all smoking related
terms from the training set and created a “smoke
blind” set by depending on general information in the
document for predicting a smoking label. In this
system, a NB classifier algorithm was applied with
the bigram word features. The shared objective of this
work with ours is the dependency upon general text
information for smoking status classification, instead
of smoke-related information.
Other approaches have made good progress
towards extracting and classifying sentences related to
smoking only, and ignoring others as noisy data, by
utilising rule-based systems (Liu et al., 2012; Savova
et al., 2008; Zeng et al., 2006; Aramaki et al., 2006;
Szarvas et al., 2006; Cohen, 2008).
Unfortunately, these methods used in these
previous studies, in terms of extracting sentences
related to smoking only and classifying each one
individually, are not suitable for online forum
discussion data. There are two reasons behind that.
First, a forum is written by the users themselves and
sentence boundaries are not guaranteed due to poor
use of punctuation. Second, forums have fewer words
and a smaller number of sentences per post in
comparison with clinical reports.
All the above methods designed to predict
smoking status, share a common data type, namely
the clinical reports corpus. These methods are may
not be directly applicable to forum posts due to the
different nature of the text in clinical reports. In
addition to that, clinical reports have limited
availability and are difficult to access. Furthermore, it
can be seen that smoking status classification utilising
online forum discussion has received little attention
from researchers. Consequently, no specific standard
features have been confirmed or recommended for
smoking status classification in previous literature.
In this work, we address the above issues by
investigating smoking status classification on a
different type of data source and identify relevant
features to use in smoking status classification. The
main focus is on UGC data such as online forum
discussions. In the next section, the comparison in
writing style and text properties for online forum
discussion and clinical reports is presented.
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3 FORUM LANGUAGE
PROPERTIES
The style of online forum discussion is different to
other types of texts, such as clinical reports. The nature
and the properties of the language in forum and clinical
reports are presented in this section.
The text in forums is less focused and directed than
that in clinical reports. It contains thoughts, everyday
experiences, feelings, opinions, and social status.
Furthermore, as it is written by the users themselves, it
is less grammatically and syntactically accurate than
clinical reports. It enjoys almost universal public
access, with no pre-determination involved in terms of
criteria for specific readers. The text has the advantage
of being written in colloquial language and unedited.
This complexity of text motivates us to search for the
best features that capture smoking status. Furthermore,
the styles of writing are able to reflect the person’s
situation and are useful in research avenues such as
person’s personality, emotions and social state.
A summary of the language properties, of both
forums and clinical reports, are shown in Figure 1. A
high percentage of usage of first person singular
pronouns, positive emotion and the present tense
concur with the forum corpus, in contrast with clinical
reports. First person singular pronouns hold a dominant
position in the poster’s writing, because this data is
written by the users themselves. Likewise, as the events
or everyday activities are immediately reported, the
present tense is used widely. Various subjects in
addition to health related issues are also covered due to
the authors feeling free to include them in their posts.
As a result, more words expressing positive emotion
are used rather than words that express negative
emotion. Those characteristics require new types of
features that can help determine the smoking status
class. In the following section, the selected features for
this investigation are explained in detail.
Furthermore, to explore the feasibility of applying
this proposed approach to other types of UGC,
language properties for blog, email and online forum
texts were compared. To the best of our knowledge, the
comparison in linguistic style, for these types of UGC,
has not been investigated.
A group of fifteen psycholinguistic features for
blog and email have been produced by (Gill, Nowson,
and Oberlander, 2006), most features were extracted
from linguistic and psychological main categories. The
features belonging to blog and email were compared
with the same features in forum and clinical records.
The comparison shows that email and blog features are
almost in line forum data but differ from clinical
reports.
In conclusion, this approach of smoking status
classification could be generalised on data of UGC
sources, as they share common psycholinguistic
properties.
4 THE PROPOSED FEATURE
SET
In text classification task, the features selection has a
crucial role to play in the results in text classification
tasks. In the course of analyzing the nature of the
online forum’s text, in Section 3 we have
demonstrated that the psycholinguistic features of
writing forums are different from other sources of text
(clinical reports). Therefore, psycholinguistic features
are proposed to apply in this research to represent
feelings, personal activities and thoughts that are
included in forum text.
Pronouns Emotional processes Tense
Figure 1: Psycholinguistic features in clinical reports and forum data corpora.
I
39%
WE
3%
SELF
41%
YOU
13%
OTHER
4%
Forum
POSITIVE
EMOTION
51%
POSITIVE
FEELING
7%
OPTIMISM
14%
NEGATIVE
EMOTION
18%
ANXIETY
3%
ANGER3%
SADNESS
4%
Forum
PAST
27%
PRESENT
65%
FUTURE
8%
Forum
I
6%
WE
1%
SELF
7%
YOU
12%
OTHER
74%
ClinicalRecords
POSITIVE
EMOTION
26%
POSITIVE
FEELING
5%
OPTIMISM
5%
NEGATIVE
EMOTION
44%
ANXIETY
8%
ANGER
2%
SADNESS
10%
ClinicalRecords
PAST
60%
PRESENT
34%
FUTURE
6%
ClinicalRecords
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LIWC dictionary has been selected as a feature set for
smoking status classification. LIWC counts the
appearance of words or word-stems belonging to pre-
defined psychological and linguistic categories. For
example, the term “hunger” captures the words hungry,
hungrier, hungriest. Furthermore, one of the major
strengths of the LIWC is that the dictionary has been
rated and evaluated by independent judges (Tausczik
and Pennebaker, 2010).
The selected 88 LIWC features were grouped into
four types:
 Standard linguistic features (e.g., total word count,
pronouns);
 Psychological features (e.g., cognitive, emotional
processes);
 Personal concerns features (e.g., occupation,
leisure activity);
 Paralinguistic features (e.g., non-fluencies, fillers
words).
5 THE SMOKING STATUS
CLASSIFICATION
FRAMEWORK
The methodology of evaluating online forum
discussion for smoking status classification involved
executing 3 experiments. The aim of experiment 1 was
to evaluate the results of classifying forum data with
baseline results (that was dependent on clinical
reports). In the second experiment, the performance of
using a LIWC features set was compared with other
features type. The aim of experiment 3 was to explore
the other factors that affected the classification results.
Three fundamental steps were conducted as a part
of smoking status classification framework, as depicted
in Figure 2. These steps involved:
 Pre-processing phase: includes obtaining the
data from the web and cleaning them. For
example, removing repeated and empty posts;
 Feature Extraction phase: converts each posting
into a corresponding features vector. This
changes the input data from unstructured text
space into features vector space;
 Model building phase: includes the use of a
suitable machine learning algorithm classifier
that produces a useful classification model. The
extracted model is then tested and evaluated in
terms of having the best results.
6 EXPERIMENTS, RESULTS, AND
DISCUSSION
In this section the experiments' setup, datasets, results
and evaluations are detailed.
6.1 Experiment Setup
To evaluate the proposed framework, various
experiments were carried out over online forum
discussion corpus and clinical reports datasets. The
evaluations included comparison with baseline method,
applying different types of features and exploring
factors that affect the framework results.
The experiments have been performed using the
SVM machine learning algorithm, as it is one of the
best algorithms available in data mining tasks (Wu et
al., 2008). A 10-fold cross validation procedure was
used in training and testing each dataset by using
WEKA toolbox (The University of Weka to October
2013). The experiments ran on Intel Core i5- 2.30 GHZ
computer with 6 Gigabytes of RAM.
6.2 Datasets
The experiments have been performed using various
forum discussions datasets, a collection of 7000 posts,
from publically available forums. To obtain enough
covering data, a set of criteria was systematically
Figure 2: Framework of building and classifying UGC data with LIWC features set.
Online Forum
Discussion
Feature Vector Space
Tbl
SVM Classification Algorithm
Training & Evaluation
Classification Model
LIWC Features
Extraction
Experiment Results
Post Text
Feature Vector Space
New Forum
Post Text
LIWC Features
Extraction
Smoking Status Classification
Result
Building
Classification
Model
Testing
Classification
Model
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245

applied on the collected data e.g. the availability of
posts for those in-journey to stop smoking and
frequency of posting. Furthermore, the same criteria
used in identifying each class in the clinical reports
corpus has been used in selecting the forum corpus,
especially for the current smoker, past smoker and non-
smoker classes. Different smoking words were used to
retrieve online forum discussions such as; "smoker
forum", "stop smoking forum".
In order to post to the online forums that had been
selected, users must register. They have the option to
enter a profile with pre-set fields. Unfortunately, not all
profiles included a smoking status field. Also, not all
users provided and updated their current smoking
status. Furthermore, the smoking status extraction task
is challenging in this research as no smoking keywords
were relied on in classification. For these reasons,
forum's title was dependent on annotating each post
separately to be for past smoker, current smoker, non-
smoker or people in-journey to stop smoking. The
content of the posts was tokenised, converted to lower
case, cleaned for non-English, repeated and empty
posts. Online forum discussion corpus is freely
available on http://dcapi.blogs.lincoln.ac.uk/?p=341.
Two different datasets were generated from forum
discussions corpus for experimentation purposes, these
were:
 “Dataset A" includes the same number of posts as
the number of documents in the clinical reports
corpus with maintaining the balance of the same
number of documents in each smoking class similar
to the clinical reports corpus;
 “Dataset B” was used in evaluating the effect of
post length on the framework classification output.
Depending on statistical calculations in forum
corpus, in terms of the average of forum post
length (word number), all posts that have less than
40 words were filtered out from the corpus. From
the remaining forum posts, dataset B was generated
with same properties as dataset A.
A clinical report dataset was requested directly
from i2b2. It included four smoking classes (past
smoker, current smoker, smoker and non-smoker) after
filtering unknown classes from the corpus. After being
tokenised and converted to lower case these reports
were used in the experiments.
6.3 Results and Discussion
In this sub-section, we explain the experiments and
present the results and discussion. This sub-section
contains three experiments. The first is comparing the
performance of forum and clinical report data (in
baseline method), in terms of using binary word
feature. The second experiment tests the suitability of
the proposed feature type for classifying forum and
clinical data. The third experiment is designed for
examining the effect of post length, other classifier
algorithm and filtering features on smoking status
classification.
6.3.1 Compare with Baseline Method
The first experiment was performed to evaluate the
performance of online forum discussion against the
clinical reports (baseline method (Clark et al., 2008)) in
the smoking status classification problem. The
experiment used the baseline method, which achieved
the best results in the smoking status challenge (Uzuner
et al., 2008). The baseline method collected only binary
unigram and bigram features and applied SVM
classifier with 10-cross validation procedure to classify
clinical reports, described in detail in Section 2. Forum
dataset A, as described earlier, was used in this
experiment.
Figure 3 shows the accuracy for the clinical reports
and forum data over smoking status prediction. The
figure shows that the binary word features are effective
in classifying clinical records (82%) than forum data
(75.69%). In addition, with binary word features type
the size of the features vector space varies with the
dataset size. For example, the binary feature vector
length could be more than 20k, out of 390k words
(5.128%) in a clinical report dataset, unless appropriate
thresholding is applied. On the other hand, the binary
feature vector length for the forum dataset can be more
than 3K, out of 21K words (14.285%). When forum
feature set was reduced to 1500, by using systematic
method, the accuracy decreased to 60%. Furthermore,
if new testing data/posts include new words that are
unknown to the trained model, it will not be recognized
and will not contribute to the classification resulting in
potential reduction of accuracy.
Therefore, the use of the psycholinguistic features
set (LIWC) is proposed to classify forum data, given its
observed properties. This includes various categories
(Section 4) and captures feelings, thoughts, emotions
and experiences that arise in daily discussion for
smoking status classification.
Figure 3: The classification accuracy results of classifying
forum dataset A and clinical reports, when binary word
features was used.
82
75,69
40
60
80
ClinicalReports(baseline) ForumData
Classification
Accuracy%
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246

6.3.2 Feature Comparison
In order to analyse the selected feature set in classifying
the proposed data, the framework in Figure 2 was
executed in terms of using LIWC features set for
classifying forum dataset A. The result was then
compared with applying LIWC features on clinical
reports data set. A feature vector space that represents
the LIWC categories and subcategories values of each
text in the forum and clinical reports datasets was built
separately.
As shown in Figure 4, the forum data gives the
highest accuracy of up to 75.09% whilst classifying
with clinical report has a lower accuracy of 54%.
Moreover, the feature vector length is both compact (88
features) and independent from the dataset itself. It
mainly depends on the LIWC categories. This would
also explain the results achieved. For example, with
closer analysis we found that 70.99 % of forum's words
were identified by the LIWC dictionary, while only
38.93% words were recognized by the LIWC
dictionary in the clinical report dataset. This can be
attributed to the fact that online forum discussion is a
wider discussion area than clinical reports and includes
writing about feeling and emotions with discussions in
different subjects which could cover different
categories in LIWC dictionary.
Another observation is that using the LIWC feature
for forum data has achieved slightly less accuracy
compared to using binary word features on the same
data. Nevertheless, the proposed features set is highly
compact (only 88 features), independent of the dataset
and fixed in length. Moreover, the LIWC contains
psycholinguistic categories and covers different topics
that could capture more words in forum discussions.
Further analysis has been done to extract significant
LIWC categories that have been more positively
affected during the classification process. Using the
Principle Component Analysis (PCA) technique, main
LIWC features were extracted from the forum's feature
vector, as illustrated in Table 1. These features could be
utilized for additional classification or clustering
processes.
Another type of feature has been examined for
smoking status classification (POS taggers). It mainly
consists of 37 tags (The Stanford natural language
Figure 4: The classification accuracy results of classifying
forum dataset A and clinical reports sets, when a LIWC
features was used.
Table 1: Significant LIWC categories in forum’s feature
vector that impact positively on classification results, ranked
according to PCA.
Discrepancy Causatio
n
Insight Certainty
Sensory
process
Inhabitation Tentative Optimism
Negative
emotion
Positive
emotion
processing group. October 2013). POS taggers was
merged with LIWC features, thus the overall size of the
feature set is 125 categories. Based on this combined
new features set the accuracy increased slightly by
0.36%. Thus due to this small accuracy increase, we
utilized LIWC features only for the forthcoming
experiments, as it is also shorter (88 features). In
addition, it is used for the first time for smoking status
classification.
In general, the new proposed feature could form the
basis for further intensive investigation of a person’s
emotion and psychological state at various stages of the
stop smoking process (or a similar task).
6.3.3 Factors Affecting the Results
The purpose of this subsection is to study the effect of
different factors on the smoking status classification
performance. These factors include the post length,
applying different classifier algorithms and filtering
features. These experiments used the framework
explained in Figure 2, and datasets A and B, as
described earlier.
Post Length
The effect of post length on smoking status
classification was tested. Figure 5 represents the
accuracy of framework when forum dataset B (includes
posts with 40 words or more) was used as input data
against result of classifying forum dataset A. It shows
that when the post’s length was increased the
classification accuracy improved to 78.99%. This is
due to the selected posts including general discussion
and not specifically discussions related to smoking
only. Thus, classifying posts that have more words
gives more opportunity to extract valuable information
that could help in inferring the smoking status. For
example, in this experiment, forum dataset B with
longer post scores a higher percentage of words that are
identified by LIWC dictionary (72%) against forum
dataset A before filtering shorter posts (70.99%).
In the selected forum corpus, classifying longer
posts (in number of words) reflected higher accuracy.
However, this is not a generalization, as the extra
words should be relevant and recognizable by the
utilized dictionary.
54
75,09
40
60
80
ClinicalReportsdata ForumData
Classification
Accuracy%
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247

Figure 5: The classification accuracy results of classifying
forum dataset A and B.
Different Classifiers
Different classifier algorithms were evaluated to predict
the smoking status for the forum dataset A. KNN (K-
nearest neighbours algorithm) and NB classifiers
algorithms were selected (they were used before in
literature for similar task) and trained on a LIWC
features vector. The results of using other classifiers
were compared with SVM output in Figure 6. They
show SVM classifier giving higher accuracy against
others. This result verifies (Sordo and Zeng, 2005; Wu
et al., 2008) conclusion, that SVM algorithm is
considered as one of the best machine learning
algorithms in data mining. In addition, it is more
accurate classifier algorithm to use for smoking status
classification in forum data than KNN and NB.
Figure 6: The classification accuracy results of classifying
forum dataset A with different classifier algorithms.
Filtering Features
To examine the proposed feature set, this method was
designed by removing redundant features that carry
little information and do not assist in smoking status
classification. Manual and systematic methods were
followed to remove part of the LIWC features from the
forum dataset A feature vector.
Manual feature selection method was performed by
progressively removing weakest features by using
Weka. The final selected group of features was 77
categories, whilst retaining the significant LIWC
categories set that were extracted in (Section 6.3.2,
Table 1). In the systematic method, the relation
between each feature in the feature vector space and the
smoking status classes was found. Potentially noisy
features were then removed by using Weka’s chi-
squared correlation weight. Figure 7 shows the effect
on accuracy after removing features (by using both
ways separately) and using a SVM classifier algorithm
with the 10 cross-validation techniques.
However, contrary to expectations, these methods
did not find a significant accuracy improvement against
their original values. Filtering features resulted
inreductions of their original value in about 0.19% and
2.39% in manual and systematic method respectively.
This is because online forums include general
discussions that involve most LIWC categories.
Therefore when we removed part of these features
from the LIWC feature set, the percentage of words
that was recognized by LIWC dictionary decreases of
1.69% and 1.73% of their original values in manual
and systematic methods respectively. In the systematic
selection, the accuracy result was less than manual
selection because there is more opportunity to remove
essential categories (Table 1) from LIWC features list.
This finding suggests that the full set of the
proposed features (88 categories and subcategories) is
important for understanding and classifying online
forum discussion.
Figure 7: The classification accuracy results of classifying
forum dataset A before and after filtering LIWC features
set.
7 CONCLUSION AND FUTURE
WORK
In this paper an investigation into the feasibility of text
analysis has been presented to detect the smoking
status in online forum discussions. Based on the
investigation performed in this study, the contribution
can be viewed from three aspects. The first contribution
analyzes online forum discussions that are widely
available and easier to obtain, compared to clinical
records. The second contribution is the utilization of
psycholinguistic (LIWC) features. To the best of our
knowledge, this is the first time that this feature set has
been used for smoking status classification. Finally, the
effects of post length, classifier algorithm and filtering
features on the framework of smoking status
classification were analyzed.
The experiments and results showed that, using the
proposed features, the classification accuracy on the
forum posts outperformed those on the clinical reports
with LIWC features. Secondly, the proposed LIWC
feature set has a fixed length, compact size (only 88
features), and is independent of the dataset size.
Thirdly, the result also established that an increase in
post length (or the number of words in post) contributes
75,09 78,99
40
60
80
forumdatasetwith
differentpost'slength
forumdatasetwith
longerposts
classification
Accuracy%
75,09
59,76
55,77
40
60
80
SVM KNN NB
Classification
Accuracy%
75,09
74,9
72,7
40
60
80
Fullfeatureset Featuresafter
manuallyfiltering
Featuresafter
systematicfiltering
Classification
Accuracy%
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to improving the classification accuracy. Although
good and promising results were achieved using the
proposed data type (online forum discussion) and
features (LIWC) many directions remain open for
development in this area of research. One of the
important aspects is the utilisation of the LIWC
dictionary for further analysis of a person’s emotional
and psychological status at various stages of the stop
smoking process (i.e. in journey to stop smoking).
REFERENCES
Linguistic inquiry and word count. October 2013 Available
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TextAnalysisofUser-GeneratedContentsforHealth-careApplications-CaseStudyonSmokingStatusClassification
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