One of the key aspects in a psychotherapeutic conversation is the understanding of topics dynamics driving the dialogue. This may provide insights on the therapeutic strategy adopted by the counselor for the specific patient, providing the opportunity of building up artificial intelligence (AI) based methods for recommending the most appropriate therapy for a new patient. In this paper, we propose a method able to detect and track topics in real-life psychotherapeutic conversations based on Partially Labeled Dirichlet Allocation. Topics detection helps in summarizing the semantic themes used during the therapeutic conversations, and in predicting a specific topic for each talk-turn. The conversation is modeled by means of a distribution of ongoing topics propagating through each talk sequence. Tracking of topics aims at exploring the dynamics of the conversation and at offering insights into the underlying conversation logic and strategy. We present an alternative way to look at face-to-face conversations in conjunction with a new approach that combines topic modeling and transitions matrices to elicit valuable knowledge.
The theoretical and technological advances in several disciplines of linguistics,
computer science, and healthcare have made possible the recent investigation of
therapeutic conversation analysis as a growing field of research [
Therapeutic conversations methods such as Cognitive Behavior Therapy (CBT) refer to a range of therapies that can help treating mental health problems, emotional challenges, and some psychiatric disorders of patients by changing their ways of thinking and behave. Accordingly, these therapeutic methods create a new way of looking at severe psychological issues to help patients to move towards a solution and to gain a better understanding of themselves. The treatment is usually a face-to-face conversation where the counselor interacts directly with the patient to understand his feelings, e.g. confident, anxious, or depressed, as well as the causes of his feelings. During the conversation, counselor and patient create a sequence of spoken sentences, each characterized by a certain topic, creating in this way a thematic structure to the whole therapeutic conversation. By adopting a set of techniques and conversational strategies coming from clinical practice, the counselor aims at solving behavioral and psychological problems of the patient by properly reacting to the patient and redirecting the conversation towards certain thematics.
As a result, investigating and modeling the human-to-human dialogues in this kind
of context may serve as a guide for the development of AI-based dialoguing systems
able, e.g., at recommending the most appropriate therapeutic strategy to adopt by the
practitioner for a new patient [
In this paper we investigate patient-therapist interactions by modeling the
propagation of the topics addressed during a given therapeutic conversation by using Partially
Labeled Latent Dirichlet Allocation (PLDA) [
The study has been conducted over a dataset of 1729 real-life transcribed psychotherapeutic conversations, each made of different talk-turns, which we will describe further in Section 4. In our method we first identify the most common topics used within the psychology corpus. The PLDA model takes as input the given conversations and detects significant words for each topic. Secondly, the trained PLDA model is able to determine the potential topic addressed in each talk-turn. The talk-turns flow is then transformed into a sequence of potential topics within each conversation. Finally, the semi-supervised PLDA topic model is evaluated by computing its coherence over the most significant words for each topic. Our final aim is to find the quintessential patterns in therapeutic conversations and to understand the topic switches according to the adopted dialogue strategy and topics propagation dynamics. In our method we distinguish the topic changes driven by the counselor and the ones prompted by the patient, and two topic transition matrices are constructed accordingly. These matrices are able to characterize the conversation and to provide important hints towards the understanding of the topics propagation dynamics.
The remainder of the paper is organized as follows. In Section 2 we discuss the related works in the literature on automatic topic detection methods and therapeutic dialogue analysis, that help to establish the basis for the present work. In Section 3 we describe how topic modeling algorithms work, specifying their main characteristics. Afterwards we describe the data used for the experiments and the followed preprocessing steps in Section 4. Section 5 shows our approach on TDT by illustrating the developed methodology. Lastly, Section 6 discusses how to evaluate our model, while Section 7 ends the paper with conclusions and directions for future research. 2
Current trends in therapeutic conversations research focus on the digitalization of
spoken interactions and on the recommendation of the most appropriate treatments,
employing computational techniques such as NLP which offer the potential to extract
knowledge from consultation transcripts. Authors in [
Topic models are a family of probabilistic approaches that aim at discovering latent semantic structures in large documents. Based on the presumption that meanings are relational, they interpret topics or themes within a set of documents originally constructed from a probability distribution over words. As a result, a document is viewed as a combination of topics, while a topic is viewed as a blend of words.
One of the most widely used statistical language modeling for this end is Latent
Dirichlet Allocation (LDA) introduced by Blei et al. [
Labeled Latent Dirichlet Allocation (LLDA) [
Partially Labeled Latent Dirichlet Allocation(PLDA) [
For our experiments, we use a dataset consisting of a collection of psychotherapeutic transcripts available for research. The conversations have been transcribed and collected according to the guidelines of the American Psychological Association (APA)5. An approval to use the collection was granted by an Internal Committee of Biomedical Experiments (ICBE) of Philips after a review of the agreements, the consent procedures, and data handling plan by legal and privacy experts.
4.1
Our dataset consists of 1729 transcripts of 1:1 conversation with a total of 340,455 talk turns, 75,732 unique terms, and more than 9 million words. Each transcript has on average 200 talk-turns and eight words for talk-turn. They are also extended with meta-data consisting of the corresponding school of psychotherapy, counselors-patients information such as gender, age range, and sexual orientation as well as a table of topics discussed during the therapeutic conversation. The table of topics contains two different kinds of information: – Subjects: they are organized into three hierarchical levels. The top level is the most general whereas the other two are more precise. For example, the word Family could correspond to a top level topic, while Family violence and Child abuse would be associated to the second and third levels respectively. Up to 575 subjects have been used in the three levels in total. – Symptoms: there are 79 symptoms (e.g. Depression, Anger, Fear) as defined in the
DSM-IV6 manual. 4.2
Given the high number of items in the table of topics, we applied the following steps to merge similar topics and reduce the number of subjects and symptoms to 18 and 16 respectively: 1. Eliminate all the subjects and symptoms that occur in less than 3% of the dataset; 2. Group together all the subjects belonging to the same Wikipedia category7 regardless their position in the given hierarchical structure. 3. Assign a label to the new subject according to the psychology topics table from APA. For example Parent-child relationship and Family are mapped to a new subject from APA known as Parenting. 4. Reduce the number of symptoms by using the DSM-IV manual with the expert support of a counselor. In particular, we group symptoms with high-level correlation into a representative one. For example, Sadness and Hopelessness are merged into the symptom: Depression.
The final set is depicted in Figure 3. 4.3
Using the NLTK platform8 we applied a number of NLP pre-processing steps to the
dataset [
classifications
– removal of all punctuations, stop words, numbers, words that frequently appeared
in the text with less content information (e.g., ”mm-hmm”) and words that occurred
in less than five documents and keeping nouns, verbs and adjectives only. This was
achieved by using unigram part-of-speech tagger [
The proposed automatic TDT method has three phases; the detection of topics, assignment of topic labels to talk turns, and finally tracking of the propagation of topics over the conversation. 5.1
The topic detection was performed using a PLDA implementation based on the Stanford
Topic Modeling Toolbox9(TMT). The model requires the definition of parameters such
as the number of hidden topics to discover, the hyperparameters and (see Figure 2),
and a vast amount of short text as input for training purpose. To enlarge the number of
corpora, we defined each talk-turn as a document, and we associated each document
(talk-turn) with the corresponding topics from the table of topics of the corresponding
transcript because PLDA is useful in general only when each document has more than
one label associated to it. As a result, we obtained a broader set of documents with
higher word co-occurrences. More in detail, we needed to specify how many new topics
(different from those in table of topics) the model had to discover. Experimentally this
number was set to 20. As a further input, we fed the PLDA with the list of 34 topics and
the values of and were set to 0.01. In total 268,478 talk turns were obtained after
the preprocessing step. Moreover, we used the CVB0 algorithm [
The PLDA returns the topics and the associated terms for each talk turn (document). Based on the terms of each document, we can then determine how likely each document was associated with a topic. Table 1 shows a completion of the terms learned from the trained PLDA topic model. There we list the top ten terms for each topic. In the first column, one can see the discovered topic and its associated words whereas the second and third columns indicate two of the 34 topics already known and their related words. The terms in the same topic tend to be similar, particularly for subjects and symptoms. For example, Parenting carries the member of the family, such as mom, mother, dad, etc.. Moreover, Addiction includes the terms close to alcohol and drugs (drinking, smoke, etc.). On the other hand, the domain of the discovered topics has inherent interpretations and holds words that are not covered by the annotations in psychotherapy corpus. For example, the Topic-5 includes similar terms but their meaning (work) is far from any annotations in APA. For this reason, for the tracking task, we only used the 34 elements present in our table of topics.
In addition to the 54 topics (34 known and 20 discovered by PLDA) and their relevant terms, we aimed to know the likelihood of each topic in each talk-turn. As such, another output of the PLDA topic model consisted in the partition of the documents into a set of 54 topic proportions, called per-document topic distributions, where each talk-turn was represented as a combination of topics with different proportions. As already explained earlier, Figure 4 shows an example of the potential ongoing topics in each talk-turn within a therapeutic conversation from the dataset. The goal was to understand how the known topics propagate, are localized and change for each talker in conversations. We already used PLDA to identify a potential topic for each talk-turn from the table of topics converting the face-to-face conversation to a sequence of topics for each speaker. For the tracking topics task we added a new topic annotated as Meaningless talk which we associated to talk-turns that provide poor semantics contents or language, or non-verbal communication (e.g. ”Yahh!!, Mm-hmm”). We built two topics transitions matrices (TTMs) to understand how the topics change from one talk-turn to another one. Topic changes can be seen as a dynamic mechanism that frequently occurs inside a conversation where speakers move from one topic to another and can be contested by either speaker. We recognize three kinds of changes: 1. The counselor keeps talking about the same topic to the patient from the previous talk-turn, and vice versa; 2. The counselor moves to a new topic after the talk-turn of the patient; 3. The patient moves to a new topic after the talk-turn of the counselor.
More in detail we constructed patient-to-counselor TTM CPk that describes all the topics changes within the conversation k. In particular, CPk[i; j] is the number of times that topic i changes into j in the conversation k. We merged the CPk matrices together by summing up corresponding elements obtaining our final matrix CP . Similarly, we built a counselor-to-patient matrix P C by using the topics-change defined earlier by switching counselor and patient. The difference between the two matrices is illustrated in Figure 5, which, shows the engagement patterns between counselors and patients, providing a new way to describe one-to-one conversations. There are three possibilities depending on whether the resulting value is lower than -10 (black), between -10 and +10 (gray) and greater than 10 (white). The diagonal of the matrix in Figure 5 gives an idea about the first type of topics changes which corresponds to the “resistance level” on the same topic; it has 17 gray values which proves the fact that the speakers like continuing to talk on the same topic. It has also twelve black values and six white values. The former means that the counselor switches topics twice as the patient does. A possible explanation is that the counselor aims at searching for other correlated symptoms or subjects that would lead to a mental disease. The other values of the matrix describe the second and third type of topic changes; the number of white and black values are approximately equal, which means that the conversations, in general, are discussed without perceived tactics. Nevertheless, some rows and columns are mostly negative or positive, like Parenting, and indicate the use of some strategies. The counselor often switches the topic if the previous one was Mania, Medication or Patient-Counselor Relations. Instead, he/she frequently starts a new topic if the patient’s talk restrains less semantic contents (Meaningless Talk). Contrary, the patient often switches topics if the previous topic was related to Parenting, Friendship, Sexual dysfunction, Crying, or Stress-and-Work. Conclusively, TTM guides to a bright understanding of how and when topics are changing thus giving important insights to the counselor for CBT. 6
The evaluation of the performance of a topic model is not an easy task. In most cases,
topics need to be manually evaluated by humans, which may express different opinions
and annotations. The most common quantitative way to assess a probabilistic model
is to measure the log-likelihood of a held-out test set performing perplexity. However,
the authors in [
Constituting a crucial aspect for the analysis and modeling of counselor-patient conversations, the automatic TDT task in a psychotherapeutic conversation poses a significant challenge. In this paper we implemented a topic detector which efficiently understands therapeutic discussions in consultations. We exploited PLDA and state-of-the-art NLP techniques and topic coherence evaluation systems. Furthermore, we computed TTM to capture the dynamics of each ongoing topic in the conversations understanding how much each interlocutor is affected in the dialogue and when he/she prefers switching topics. Knowing how topics change and their propagations can be used by counselors to drive the discussion and to detect a patient emotional state during the therapeutic conversation. These aspects of interaction are critical for all mental health specialists as they are involved in patient’s health concerns. We conclude that PLDA and TTM may be of benefit to the therapeutic conversational speech analysis community, by having high potentials into real-life applications in psychotherapy.