Affective Behaviour Analysis of On-line User Interactions:
Are On-line Support Groups more Therapeutic than Twitter?
Giuliano Tortoreto† , Evgeny A. Stepanov† , Alessandra Cervone† ,
Mateusz Dubiel⋆ , Giuseppe Riccardi†
†
Signals and Interactive Systems Lab, University of Trento, Italy
⋆
University of Strathclyde, Glasgow, UK
name.surname@unitn.it, name.surname@strath.ac.uk
arXiv:1911.01371v1 [cs.HC] 4 Nov 2019
Abstract
The increase in the prevalence of mental health
problems has coincided with a growing popularity of health related social networking sites.
Regardless of their therapeutic potential, online support groups (OSGs) can also have negative effects on patients. In this work we propose a novel methodology to automatically
verify the presence of therapeutic factors in
social networking websites by using Natural
Language Processing (NLP) techniques. The
methodology is evaluated on on-line asynchronous multi-party conversations collected
from an OSG and Twitter. The results of the
analysis indicate that therapeutic factors occur
more frequently in OSG conversations than in
Twitter conversations. Moreover, the analysis
of OSG conversations reveals that the users of
that platform are supportive, and interactions
are likely to lead to the improvement of their
emotional state. We believe that our method
provides a stepping stone towards automatic
analysis of emotional states of users of online
platforms. Possible applications of the method
include provision of guidelines that highlight
potential implications of using such platforms
on users’ mental health, and/or support in the
analysis of their impact on specific individuals.
1
Introduction
Recently, people have started looking at online forums either as a primary or secondary source of
counseling services (Vogel et al., 2007). McMahon (2016) reported that over the first five years of
operation (2011-2016), ReachOut.com – Ireland’s
online youth mental health service – 62% of young
people would visit a website for support when going through a tough time. With the expansion of
the Internet, there has been a substantial growth
in the number of users looking for psychological
support online.
The importance of the on-line life of patients
has been recognized in research as well. AmichaiHamburger et al. (2014) stated that the online life
of patients constitutes a major influence on their
self-definition. Furthermore, according to Back
et al. (2010), the social networking activities of
an individual, offer an important reflection of their
personality. While dealing with patients suffering from psychological problems, it is important
that therapists do not ignore this pivotal source of
information which can provide deep insights into
their patients’ mental conditions.
Acceptance of on-line support groups (OSG)
by Mental Health Professionals is still not established (Andersson, 2017). Since OSG can have
double-edged effects on patients and the presence
of professionals is often limited, we argue that
their properties should be further studied. According to Barak et al. (2008) OSG effectiveness is
hard to assess, while some studies showed OSG’s
potential to change participants’ attitudes, no such
effect was observed in other studies (see Related
Work Section for more details). Furthermore the
scope of previous work on analysis of users’ behaviour in OSG has been limited by the fact that
they relied on expert annotation of posts and comments (Mayfield et al., 2012).
We present a novel approach for automatically
analysing online conversations for the presence of
therapeutic factors of group therapy defined by
Yalom and Leszcz (2005) as “the actual mechanisms of effecting change in the patient”. The
authors have identified 11 therapeutic factors in
group therapy: Universality, Altruism, Instillation
of Hope, Guidance, Imparting information, Developing social skills, Interpersonal learning, Cohesion, Catharsis, Existential factors, Imitative behavior and Corrective recapitulation of family of
origin issues. In this paper, we focus on 3 therapeutic factors: Universality, Altruism and Instillation of Hope (listed below), as we believe that
these can be approximated by using established
NLP techniques (e.g. Sentiment Analysis, Dialogue Act tagging etc.).
1. Universality: the disconfirmation of a user’s
feelings of uniqueness of their mental health
condition.
2. Altruism: others offer support, reassurance,
suggestions and insight.
3. Instillation of Hope: inspiration provided to
participants by their peers.
The selected therapeutic factors are analysed in
terms of illocutionary force1 and attitude2 . Due to
the multi-party and asynchronous nature of on-line
social media conversations, prior to the analysis,
we extract conversation threads among users – an
essential prerequisite for any kind of higher-level
dialogue analysis (Elsner and Charniak, 2010).
Afterwards, the illocutionary force is identified using Dialogue Act tagging, whereas the attitude by
using Sentiment Analysis. The quantitative analysis is then performed on these processed conversations.
Ideally, the analysis would require experts to annotate each post and comment on the presence of
therapeutic factors. However, due to time and cost
demands of this task, it is feasible to analyse only
a small fraction of the available data. Compared
to previous studies (e.g. (Mayfield et al., 2012))
that analysed few tens of conversations and several thousand lines of chat; using the proposed approach – application of Dialogue Acts and Sentiment Analysis – we were able to automatically
analyse approximately 300 thousands conversations (roughly 1.5 million comments).
The rest of the paper is structured as follows. In
Section 2 we introduce related work. Next, in Section 3 we describe the pre-processing pipeline and
the methodology to perform thread extraction on
asynchronous multi-party conversations. In Section 4 we provide the describe the final dataset
used for the analysis, and in Section 5 we present
the results of our analysis. Finally, in Section 6 we
provide concluding remarks and future research
directions.
1
The illocutionary force of an utterance is the speaker’s
intention in producing that utterance according to Loos
(2003).
2
“The attitude may be either his or her affective state,
namely the emotional state of the author when writing, or the
intended emotional communication, namely the emotional
effect the author wishes to have on the reader” Gala et al.
(2014).
2
Related Work
On-line support groups have been analyzed for
various factors before. For instance, Chung (2013)
analysed stress reduction in on-line support group
chat-rooms, and the effects of on-line social interactions. Such studies mostly relied on questionnaires and were based on a small number of users.
Nevertheless, in Chung (2013), the author showed
that social support facilitates coping with distress,
improves mood and expedites recovery from it.
These findings highlight that, overall, on-line discussion boards appear to be therapeutic and constructive for individuals suffering alcohol-abuse.
Application of NLP to the analysis of mental health-related conversation has been studied as
well (e.g. (Ghosh et al., 2017; Stepanov et al.,
2018)). Mayfield et al. (2012) applied sentimentanalysis combined with extensive turn-level annotation to investigate stress reduction in on-line support group chat-rooms, showing that sentimentanalysis is a good predictor of entrance stress
level. Furthermore, similar to our setting, they
applied automatic thread-extraction to determine
conversation threads.
Kissane et al. (2007) have shown that online support group therapy increased the quality of life of patients with metastatic breast cancer. Since many original posters reported the
benefits of group therapy on patients (McDermut et al., 2001; Amichai-Hamburger et al., 2014;
Tartakovsky, 2016; Espie et al., 2012; Gary and
Remolino, 2000; Yalom and Leszcz, 2005), we
evaluate the effect of the user interaction using
sentiment scores of comments in on-line support
groups.
According to Mayfield et al. (2012), users with
high incoming stress tend to request less information from others, as a percentage of their time, and
share much more information, in absolute terms.
In addition, high information sharing has been
shown to be a good predictor of stress reduction
at the end of the chat (Mayfield et al., 2012). Regarding information sharing, we rely on Dialogue
Acts (Austin, 1975) to model the speaker’s intention in producing an utterance. In particular, we
are interested in Dialogue Act label that is defined
to represent descriptive, narrative, or personal information – the statement.
Dialogue Acts have been applied to the analysis of spoken (Stolcke et al., 2000; Cervone et al.,
2018) as well as on-line written synchronous con-
versations (Forsythand and Martell, 2007). We apply Dialogue Act tag set defined in Forsythand and
Martell (2007) to the analysis of our on-line asynchronous conversations. We argue that Dialogue
Acts can be used to analyse user behaviour in social media and verify the presence of therapeutic
factors.
3
Methodology
We select the three therapeutic factors – Universality, Altruism and Instillation of Hope – that can be
best approximated using NLP techniques: Sentiment Analysis and Dialogue Act tagging. We discuss each one of the selected therapeutic factors
and the identified necessary conditions. The listed
conditions, however, are not sufficient to attribute
the presence of a therapeutic factor with high confidence, which only can be obtained using expert
annotation. Our analysis focuses on the structure
of conversations; though content plays an important role as well.
Universality consists in the disconfirmation of
patients’ belief of uniqueness of their disease.
This therapeutic factor is shown to be a powerful
source of relief for the patient, according to Yalom
and Leszcz (2005). From this definition, we can
draw the following conditions that are applicable
to our environment:
1. improvement of original poster’s sentiment:
we hypothesize that the discovery that other
people passed through similar issues leads to
a higher sentiment score;
2. posts containing negative personal experiences: to disconfirm the belief of uniqueness
users have to share their story;
3. comments containing negative statements: to
disconfirm the patient’s feelings of uniqueness, the commenting user must tell a similar negative personal experience. This condition requires two sub-conditions: high presence of statements in comments and the presence of negative comments replying to negative posts.
Instillation of Hope is based on inspiration provided to participants by their peers. Through the
inspiration provided by their peers, patients can increase their expectation on the therapy outcome.
Yalom and Leszcz (2005) in several studies have
demonstrated that a high expectation of help before the start of a therapy is significantly corre-
lated with a positive therapy outcome. The author
states that many patients pointed out the importance of having observed the improvement of others. Therefore, the three main conditions are the
following:
1. improvement of original poster’s sentiment:
we hypothesize that instillation of hope leads
to a higher sentiment score;
2. posts containing negative personal experiences: hope can be instilled in someone who
shares a negative personal experience;
3. comments containing positive personal experiences: in order to instill hope, commenting posters must show to original posters an
overall positive personal experience. To detect positive personal experience, we require
the presence of statements in comments and
a positive sentiment of comments replying to
negative posts.
Altruism consists of peers offering support, reassurance, suggestions and insight, since they
share similar problems with one another (Yalom
and Leszcz, 2005). The experience of finding that
a patient can be of value to others is refreshing
and boosts self-esteem (Yalom and Leszcz, 2005).
However, in the current study we focus on testing
whether commenting posters are altruists or not.
We do not test whether the altruistic behavior leads
to an improvement on the altruist itself. For these
reasons, we define three main conditions:
1. improvement of original poster’s sentiment:
we hypothesize that supportive and reassuring statements improve the sentiment score
of the original poster;
2. posts contains negative personal experiences:
users offer support, reassurance and suggestion when facing a negative personal experience of the original poster;
3. comments containing positive statements: either supportive or reassuring statements show
by definition a positive intended emotional
communication. Thus comments to the post
should consist of positive sentiment statements.
Consequently, a conversation containing the
aforementioned therapeutic factors should satisfy
the following conditions in terms of NLP: Sentiment Analysis and Dialogue Acts.
1. original posters have a higher sentiment score
at the end of the thread than at the beginning;
2. the original post consists mostly of polarised
statements;
3. the presence of a significant amount of statements in comments, since both support and
sharing similar negative experiences can be
represented as statements;
4. both negative and positive statements in comments lead to higher final sentiment score of
the original poster.
4
Datasets
We verify the presence of therapeutic factors in
two social media datasets: OSG and Twitter. The
first dataset is crawled from an on-line support
groups website, and the second dataset consists
of a small sample of Twitter conversation threads.
Since the former consists of multi-threaded conversations, we apply a pre-processing to extract
conversation threads to provide a fair comparison
with the Twitter dataset. An example conversation
from each data source is presented in Figure 2.
4.1 Twitter
We have downloaded 1,873 Twitter conversation
threads, roughly 14k tweets, from a publicly available resource3 that were previously pre-processed
and have conversation threads extracted. A conversation in the dataset consists of at least 4
tweets. Even though, according to Paul and
Dredze (2011), Twitter is broadly applicable to
public health research, our expectation is that it
contains less therapeutic conversations in comparison to specialized on-line support forums.
4.2 OSG
Our data has been developed by crawling and preprocessing an OSG web forum. The forum has a
great variety of different groups such as depression, anxiety, stress, relationship, cancer, sexually
transmitted diseases, etc. Each conversation starts
with one post and can contain multiple comments.
Each post or comment is represented by a poster,
a timestamp, a list of users it is referencing to,
thread id, a comment id and a conversation id.
The thread id is the same for comments replying to
each other, otherwise it is different. The thread id
is increasing with time. Thus, it provides ordering
3
https://github.com/Phylliida/Dialogue-Datasets
among threads; whereas the timestamp provides
ordering in the thread.
Each conversation can belong to multiple
groups. Consequently, the dataset needs to be processed to remove duplicates. The dataset resulting
after de-duplication contains 295 thousand conversations, each conversation contains on average 6
comments. In total, there are 1.5 million comments. Since the created dataset is multi-threaded,
we need to extract conversation threads, to eliminate paths not relevant to the original post.
4.2.1 Conversation Thread Extraction
The thread extraction algorithm is heuristic-based
and consists of two steps: (1) creation of a tree,
based on a post written by a user and the related
comments and (2) transformation of the tree into a
list of threads.
The tree creation is an extension of the approach
of Gómez et al. (2008), where first a graph of conversation is constructed. In the approach, direct
replies to a post are attached to the first nesting
level and subsequent comments to increasing nesting levels. In our approach, we also exploit comments’ features.
The tree creation is performed without processing the content of comments, which allows us to
process posts and comments of any length efficiently. The heuristic used in the process is based
on three simplifying assumptions:
1. Unless there is a specific reference to another
comment or a user, comments are attached to
the original post.
2. When replying, the commenting poster is always replying to the original post or some
other comment. Unless specified otherwise,
it is assumed that it is a response to the previous (in time) post/comment.
3. Subsequent comments by the same poster are
part of the same thread.
To evaluate the performance of the thread extraction algorithm, 2 annotators have manually
constructed the trees for 100 conversations. The
performance of the algorithm on this set of 100
conversations is evaluated using accuracy and
standard Information Retrieval evaluation metrics
of precision, recall, and F1 measure. The results
are reported in Table 1 together with random and
majority baselines. The turn-level percent agreement between the 2 annotators is 97.99% and Cohen’s Kappa Coefficient is 83.80%.
A LICE : I want to tell him that if he can’t have a real conversation with me then don’t talk to me, because
it hurts more to feel like I’m an obligation.... I don’t want anyone to ever get close to me but I don’t
want to be alone.
B OB to feel like an obligation is really disheartening and takes a stab at the self esteem. What about the
conversations make you feel like a an obligation? Have you talked to him about this?
A LICE He doesn’t have a conversation, I know he doesn’t mean to, he’s just always busy now... I don’t
want to make him feel bad.
B OB Just remember that your needs matter too!
...
A LICE @Bob Thank you :)
Figure 1: An example of conversation thread extracted from OSG.
C AROL : lol my best friend at the time got cheated on, we wrote on the guys truck..he started chasing
us, i tripped and broke my ankle #justmyluck
DAVE wtf
C AROL it was ridiculous and i drove with my foot hanging out the window all f***ed up
DAVE when was this i’m so confused
Figure 2: An example conversation thread extracted from Twitter.
Approach
Majority Baseline
Random Baseline
Our Approach
Acc
0.92
0.87
0.97
P
0.46
0.14
0.79
R
0.46
0.14
0.80
F1
0.46
0.14
0.80
Table 1: Performance of the thread extraction algorithm on a set of 100 manually constructed trees.
4.3 Data Representation
For both data sources, Twitter and OSG with
extracted threads, posts and comments are tokenized4 and sentence split. Each sentence is
passed through Sentiment Analysis and Dialogue
Act tagging. Since a post or a comment can contain multiple sentences, therefore multiple Dialogue Acts, it is represented as as a one-hot encoding, where each position represents a Dialogue
Act.
For Sentiment Analysis we use a lexicon-based
sentiment analyser introduced by Alistair and Diana (2005). For Dialogue Act tagging, on the other
hand, we make use of a model trained on NPSChat
corpus (Forsythand and Martell, 2007) following
the approach of Lan et al. (2008).5
4
5
NLTK sentence tokenizer
The model achieves 80.21% accuracy.
5
Analysis
As we mentioned in Section 3, the presence of
each of the therapeutic conditions under analysis is
a necessary for a conversation to be considered to
have therapeutic factors. In this section we present
the results of our analysis with respect to these
conditions.
5.1 Change in Sentiment score of Original
Posters
The first condition which we test is the sentiment
change in conversation threads, comparing the initial and final sentiment scores (i.e. posts’ scores)
of the original poster. The results of the analysis
are presented in Figure 3. In the figure we can observe that the distribution of the sentiment change
in the two datasets is different. While in Twitter
the amount of conversations that lead to the increase of sentiment score is roughly equal to the
amount of conversations that lead to the decrease
of sentiment score; the situation is different for
OSG. In OSG, the amount of conversations that
lead to the increase of sentiment score is considerably higher.
Figure 4 provides a more fine grained analysis, where we additionally analyse the sentiment change in nominal polarity terms – negative
and positive. In OSG, the number of users that
Figure 3: The percentages of threads in OSG and Twitter leading to the increase or decrease of the sentiment
score of the original poster.
changed polarity from negative to positive is more
than the double of the users that have changed the
polarity from positive to negative. In Twitter, on
the other hand, the users mostly changed polarity
from positive to negative. Results of the analysis suggest that in OSG , sentiment increases and
users tend to change polarity from negative to positive, whereas in Twitter sentiment tends to decrease. Verification of this condition alone indicates that the ratio of potentially therapeutic conversations in Twitter is lower.
5.2 Structure of Posts and Comments
Table 2 presents the distribution of automatically predicted per-sentence Dialogue Acts in the
datasets. The most frequent tag is statement in
both. In Table 3, on the other hand, we present
the distribution of post and comment structures
in terms of automatically predicted Dialogue Act
tags. The structure is an unordered set of tags
in the post or comment. From the table we can
observe that the distribution of tag sets is similar between posts and comments. In both cases
the most common set is statement only. However,
conversations containing only statement, emphasis or question posts and comments predominantly
appear in Twitter. Which is expected due to the
shorter length of Twitter posts and comments.
Figure 4: The sentiment polarity change in the two
datasets - Twitter and OSG. Stable segments are labeled
either as an increase (+), decrease (-) or no change in
polarity, including neutral comments. Pos2Neg and
Neg2Pos denote a nominal polarity change.
We can also observe that the original posters
tend to ask more questions than the commenting
posters – 19.83% for posts vs. 11.21% for comments (summed). This suggests that the original
posters frequently ask either for suggestion or confirmation of their points of view or their disconfirmation. However, the high presence of personal
experiences is supported by the high number of
posts containing only statements.
High number of statement tags in comments
suggests that users reply either with supporting
or empathic statements or personal experience.
However, 6.39% of comments contain accept and
reject tags, which mark the degree to which a
speaker accepts some previous proposal, plan,
opinion, or statement (Stolcke et al., 2000). The
described Dialogue Act tags are often used when
commenting posters discuss original poster’s point
of view. For instance, “It’s true. I felt the same.”
– {Accept, Statement} or “Well no. You’re not
alone” – {Reject, Statement}. The datasets differ
with respect to the distribution of these Dialogue
Acts tags, they appear more frequently in OSG.
Class
Statement
Emphasis
ynQuestion
Continuer
whQuestion
Reject
Emotion
Accept
Greet
nAnswer
yAnswer
Bye
Clarify
Other
Twitter
62.9
9.6
7.5
2.5
6.1
2.6
2.9
2.4
0.6
1.1
0.8
0.4
0.1
< 0.1
OSG
73.0
6.3
4.7
4.3
3.7
2.9
1.5
1.3
0.8
0.4
0.3
0.2
< 0.1
< 0.1
Table 2: The distribution (in percentages) of automatically predicted per-sentence Dialogue Act tags. Tags
are counted separately for each sentence in the multisentence posts and comments.
5.3 Sentiment of Posts and Comments
Table 4 presents the distribution of sentiment polarity in post and comment statements (i.e. sentences tagged as statement). For OSG, the predominant sentiment label of statements is positive
and it is the highest for both posts and comments.
However, the difference between the amounts of
positive and negative statements is higher for the
replying comments (34.5% vs. 42.5%). For Twitter, on the other hand, the predominant sentiment label of statements is neutral and the polarity
distribution between posts and comments is very
close. One particular observation is that the ratio
of negative statements is higher in OSG for both
posts and comments than in Twitter, which supports the idea of sharing negative experiences.
Further we analyze whether the sentiment of a
comment (i.e. the replying user) is affected by the
sentiment of the original post (i.e. the user being
replied to), which will imply that the users adapt
their behaviour with respect to the post’s sentiment. For the analysis, we split the datasets into
three buckets according to the posts’ sentiment
score – negative, neutral, or positive, and represent each conversation in terms of percentages of
comments (replies) with each sentiment label. The
buckets are then compared using t-test for statistically significant differences.
Table 5 presents the distribution of sentiment
labels with respect to the post’s sentiment score.
Tag Set
Statement
Emphasis
ynQuestion
whQuestion
Emphasis
Continuer
ynQuestion
whQuestion
Accept
Reject
Posts
Twitter OSG
64.12 38.79
3.01
1.31
4.79
2.94
4.00
1.43
Statement +
2.17
3.96
0.99
6.29
2.86
7.04
4.00
3.98
0.44
0.81
1.28
3.00
Comments
Twitter OSG
57.14 41.45
4.42
3.96
4.80
2.14
4.86
2.07
3.65
0.92
1.92
1.56
0.19
0.95
5.57
4.59
4.05
2.95
1.92
3.38
Table 3: The distribution (in percentages) of post and
comment structures represented as unordered set of Dialogue Act tags.
Posts
Comments
Posts
Comments
Sentiment Polarity
Negative Neutral Positive
OSG
32.1
33.5
34.5
25.8
31.7
42.5
Twitter
20.5
44.0
35.5
21.1
45.9
33.0
Table 4: The distribution (in percentages) of sentiment
in statement sentences of posts and comments.
The patterns of distribution are similar across the
datasets. We can observe that overall, replies tend
to have a positive sentiment, which suggests that
replying posters tend to have a positive attitude.
However, the ratio of positive comments is higher
for OSG than for Twitter.
The results of the Welch’s t-test on OSG data
reveal that there are statistically significant differences in the distribution of replying comments’
sentiment between conversations with positive and
negative starting posts. A positive post tends to
get significantly more positive replies. Similarly,
a negative post tends to get significantly more negative replies (both with p < 0.01).
Table 6 presents the distribution of the sentiment labels of the final text provided by the original poster with respect to the sentiment polarity
of the comments. The results indicate that OSG
participants are more supportive, as the majority
of conversations end in a positive final sentiment
regardless of the sentiment of comments. We can
Posts
Negative
Neutral
Positive
Negative
Neutral
Positive
Comments
Negative Neutral
OSG
27.25
14.87
21.37
23.49
22.79
19.62
Twitter
32.92
22.85
26.48
25.00
18.79
16.04
Positive
57.88
55.14
65.17
44.23
48.52
57.60
Table 5: The distribution (in percentages) of reply sentiment labels with respect to the post’s sentiment label.
Comments
Negative
Neutral
Positive
Negative
Neutral
Positive
Final Sentiment of OP
Negative Neutral Positive
OSG
28.98
18.27
52.75
25.20
25.70
49.10
22.60
18.01
59.38
Twitter
24.14
41.78
34.08
21.34
49.16
29.50
20.25
35.20
44.55
Table 6: The distribution (in percentages) of sentiment
labels of the final text of the original poster (OP) with
respect to the comment’s sentiment label.
also observe that negative comments in OSG lead
to positive sentiment, which supports the idea of
sharing the negative experiences, thus presence of
therapeutic factors. For Twitter, on the other hand,
only positive comments lead to the positive final
sentiments, whereas other comments lead predominantly to neutral final sentiments.
Our analysis in terms of sentiment and Dialogue
Acts supports the presence of the three selected
therapeutic factors – Universality, Altruism and
Instillation of Hope – in OSG more than in Twitter. The main contributors to this conclusion are
the facts that there is more positive change in the
sentiment of the original posters in OSG (people
seeking support) and that in OSG even negative
and neutral comments are likely to lead to positive
changes.
6
Conclusion
In this work, we propose a methodology to automatically analyse online social platforms for the
presence of therapeutic factors (i.e. Universality,
Altruism and Instillation of Hope). We evaluate
our approach on two on-line platforms, Twitter
and an OSG web forum. We apply NLP techniques of Sentiment Analysis and Dialogue Act
tagging to automatically verify the presence of
therapeutic factors, which allows us to analyse
larger amounts of conversational data (as compared to previous studies).
Our analysis indicates that OSG conversations
satisfy higher number of conditions approximating therapeutic factors than Twitter conversations.
Given this outcome, we postulate that users who
join support group websites spontaneously seem
to benefit from it. Indeed, as shown in Section
5, the original posters who interact with others by
replying to comments, have benefited from an improvement of their emotional state.
We would like to reemphasise that the conditions for the therapeutic factors are necessary but
not sufficient; since our analysis focuses on the
structure of conversations, being agnostic to the
content. NLP, however, allows us to strengthen
our approximations even further. Thus, the further extension of our work is also augmentation of
our study with other language analysis metrics and
their correlation with human annotation.
It should be noted that the proposed approach
is an approximation of the tedious tasks of annotation of conversations by experts versed in the
therapeutic factors and their associated theories.
Even though we can use Sentiment Analysis to detect the existence of therapeutic factors, we cannot differentiate between Altruism and Instillation
of Hope, as this requires differentiation between
emotional state of the user and the intended emotional communication. Thus, the natural extensions of this work are differentiation between different therapeutic factors and comparison of the
proposed analysis to the human evaluation.
Although we acknowledge that the proposed
methodology does not serve as a replacement of
manual analysis of OSG for the presence of therapeutic factors, we believe that it could facilitate
and supplement this process. The method can
serve as a tool for general practitioners and psychologists who can use it as an additional source
of information regarding their patients condition
and, in turn, offer a more personalised support that
is better tailored to individual therapeutic needs.
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