In psychotherapy, the patient-provider interaction contains the treatment's active ingredients. However, the technology for analyzing the content of this interaction has not fundamentally changed in decades, limiting both the scale and specificity of psychotherapy research. New methods are required to "scale up" to larger evaluation tasks and "drill down" into the raw linguistic data of patient-therapist interactions. In the current article, we demonstrate the utility of statistical text analysis models called topic models for discovering the underlying linguistic structure in psychotherapy. Topic models identify semantic themes (or topics) in a collection of documents (here, transcripts). We used topic models to summarize and visualize 1,553 psychotherapy and drug therapy (i.e., medication management) transcripts. Results showed that topic models identified clinically relevant content, including affective, relational, and intervention related topics. In addition, topic models learned to identify specific types of therapist statements associated with treatment-related codes (e.g., different treatment approaches, patient-therapist discussions about the therapeutic relationship). Visualizations of semantic similarity across sessions indicate that topic models identify content that discriminates between broad classes of therapy (e.g., cognitive-behavioral therapy vs. psychodynamic therapy). Finally, predictive modeling demonstrated that topic model-derived features can classify therapy type with a high degree of accuracy. Computational psychotherapy research has the potential to scale up the study of psychotherapy to thousands of sessions at a time. We conclude by discussing the implications of computational methods such as topic models for the future of psychotherapy research and practice. Keywords: psychotherapy, topic models, linguistics I believe that some aspects of psychoanalytic theory are not presently researchable because the intermediate technology required . . . does not exist. I mean auxiliaries and methods such as a souped-up, highly developed science of psycholinguistics, and the kind of mathematics that is needed to conduct a rigorous but clinically sensitive and psychoanalytically realistic job of theme tracing in the analytic protocol (Meehl, 1978, p. 830). Advances in technology have revolutionized research in much of psychology and health care, including major developments in pharmacology, neuroscience, and genetics. Yet, the science of patient-therapist interactions-the core of psychotherapy process research-has remained fundamentally unchanged for 70 years. Patients fill out surveys, or human coders rate some aspect of the interaction. Thus, although psychiatric and psychological guidelines recommend psychotherapy as a first line treatment for a number of mental disorders (American Psychiatric Association, 2006), we still know relatively little about how psychotherapy works. As Meehl noted, existing research methods remain limited in their ability to explore the structure of verbal exchanges that are the essence of most psychotherapy. In the current article, we move toward an answer to Meehl's request for a "souped up mathematics" to mine the raw linguistic data of psychotherapy interactions. In traditional research on psychotherapy, human judgment and related behavioral coding are the rate-limiting factor. In this article, we introduce a computational approach to psychotherapy research that is informed by traditional methods (e.g., behavioral coding) but does not rely on them as the primary data source. The key innovation in this computational approach is drawing on methods from computer science and machine learning that allow the direct, statistical analysis of session content, scaling up research to thousands of sessions. Funding for the preparation of this article was provided by National Institute of Drug Abuse (NIDA) of the National Institutes of Health under award R34/DA034860, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) under award R01/AA018673, and a special initiative grant from the College of Education, University of Utah. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or University of Utah. The authors also thank Alexander Street Press for consultation related to the analysis of psychotherapy transcripts. Correspondence concerning this article should be addressed to Zac E. Imel, Department of Educational Psychology, University of Utah, 1705 Campus Center Drive, Room 327, Salt Lake City, UT 84112. E-mail: zac.imel@utah.edu This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Some estimates indicate that there are Ͼ400 different name brand psychotherapies (Lambert, 2013) ; each treatment offers a different approach to helping patients with psychological distress. Although the clinical rationales and approaches differ, it is not clear that actual practices of these psychotherapies are meaningfully distinct. Potential differences in the process and outcome of psychotherapies have been a focus of psychotherapy science for over a century. As a comparison, there are many different drug therapies. However, the unique ingredients of treatments are chemical (and patentable). Thus, the actual distinctiveness of treatments is known, even if the specific mechanism of action or relative efficacy is not. In psychotherapy, the treatment consists primarily of words, and although cognitive-behavioral oriented treatments (CBT) might focus strongly on patient behavior, the treatment is still verbally mediated (Wampold, 2007) . Accordingly, scientific classification of treatments is more nebulous. What is considered a "taxon" of cognitive-behavioral therapy may vary widely across experts and practitioners, with some definitions so broad as to include any scientifically justifiable intervention and others restricted to very specific psychological mechanisms (see Baardseth et al., 2013) . This ambiguity is quite old, reaching back to debates between Freud and his early followers and can be found in current research comparing various cognitive-behavioral psychotherapies and modern variants of psychoanalysis (e.g., psychodynamic psychotherapy; . Some have argued that differences between psychotherapies are cosmetic (like the difference between generic ibuprofen and Advil) and that the underlying mechanisms of action are common across different approaches (Wampold, 2001) . Meta-analyses generally suggest that most treatment approaches are of comparable efficacy (e.g., Benish, Imel, & Wampold, 2008; Imel, Wampold, Miller, & Fleming, 2008) , and process studies cast doubt on the relationship between treatment-specific therapist behaviors and patient outcomes (Webb, DeRubeis, & Barber, 2010) . One leading addiction researcher commented that, ". . . there is little evidence that treatments work as purported, suggesting that as of yet, we don't know much about how brand name therapies work" (Morgenstern & McKay, 2007, p. 87S). Are the 400 psychotherapies we have today unique, medical treatments? Or, are the different psychotherapies largely similar, distinguished by packaging that obscures what are mostly common components? Given that psychotherapy is a conversation between patient and provider, the distinctiveness of a therapy approach should be found in the words patients and therapists use during their sessions. Yet, this is precisely where we find a fundamental methodological gap in psychotherapy research. The source data and information are linguistic and semantic, but the available tools used to study psychotherapy are not. Research on the active ingredients of psychotherapy has primarily relied on patient or therapist self-report measures (e.g., see reviews of empathy and alliance literature; Elliott, Bohart, Watson, & Greenberg, 2011; Horvath, Del Re, Flűckiger, & Symonds, 2011) or on behavioral coding systems, wherein human "coders" make ratings from audio or video recordings of the intervention session according to a priori theoryspecific criteria (Crits-Christoph, Gibbons, & Mukherjeed, 2013) . Attempts at behavioral coding have varied in their depth from general, topographical assessments of the session such as those used in many cognitive-behavioral treatments (e.g., did the therapist ask about homework or set an agenda?) to highly detailed utterance-level coding systems (e.g., verbal response modes, Stiles, Shapiro, & Firth-Cozens, 1988; Motivational Interviewing Skills Code, Moyers, Miller, & Hendrickson, 2005). However, behavioral coding as a technology has not fundamentally changed since Carl Roger's first recorded a psychotherapy session in the 1940s (Kirschenbaum, 2004) , and coding carries a number of disadvantages. It is extremely time consuming, and reliability can be problematic to establish and maintain. In addition, there is no potential for human coding to scale up to larger applications (i.e., coding 1,000 sessions takes 1,000 times longer than coding 1 session; thus, monitoring the quality of psychotherapy in a large scale naturalistic setting is not feasible over time). There is little flexibility-coding systems only code what they code. They must be developed a priori and cannot discover new meaning not specified in advance by the researcher. More substantively, coding systems are by nature extremely reductionistic-reducing the highly complex structure of natural human dialogue to a small number of behavioral codes. Given these limitations, it is not surprising that the vast majority of raw data from psychotherapy is never analyzed and questions central to psychotherapy science remain either unanswered or impractical to address. Most content analyses of what patients and therapists actually discuss in psychotherapy are restricted to qualitative efforts that can be rich in content but by their nature are small in scope (e.g., Greenberg & Newman, 1996) . Although qualitative work remains important, the labor intensiveness of closely reading session content means that the vast majority of psychotherapy data are never analyzed. Consequently, the majority of psychotherapy studies are published without any detail as to what the specific conversations between patients and therapists actually entailed. Beyond the general theoretical description of the treatment outlined in manuals, what did the patients and therapists actually say? Are the different psychotherapies we have today linguistically unique? Or, do therapists who provide different name brand therapies say largely similar things? What specific therapist interventions, and in what combination are most predictive of good versus bad outcomes? These basic questions form the backdrop of every therapist's work, but have been impractical to consider given the current technology of behavioral coding and qualitative analysis. A critical task for the next generation of psychotherapy research is to move beyond the use of behavioral coding to mine the raw verbal exchanges that are the core of psychotherapy, including acoustic and semantic content of what is said by patients and therapists. The use of discovery-oriented machine learning procedures offer new ways of exploring and categorizing psychotherapies based on the actual text of the patient and therapist speech. The amount of data generated every day (e.g., digitized books, e-mail, video, newspapers, blog posts, twitter, electronic medical records, cell phone calls) has expanded exponentially in the last decade, with implications for business, government, science, and the humanities (Hilbert & Lopez, 2011 There is a small literature that demonstrates the utility of computational linguistic approaches for the analysis of psychotherapy data. The majority of these studies rely on human-defined computerized dictionaries in which a software program classifies words or sets of words into predefined categories. In an early study Reynes, Martindale, and Dahl (1984) found that "linguistic diversity" was higher in more productive sessions. In addition, Mergenthaler and his colleagues have published several studies demonstrating that emotion and abstraction word usage discriminates between improved and unimproved cases (e.g., Mergenthaler, 2008; see also Anderson, Bein, Pinnell, & Strupp, 1999) . Studies that have used dictionary-based strategies hold promise, but also have important limitations. First, perhaps because large corpora of psychotherapy transcripts are hard to find, these studies have generally been limited in scope (n Ͻ 100), reducing the value added of a computerized technology that can evaluate a large set of sessions (i.e., 1,000 or 10,000) in a short amount of time. Second, computerized dictionaries are limited by the categories created by humans-the computer cannot "learn" new categories. Finally, dictionaries cannot generally accommodate the effect of context on semantic meaning (e.g., "dark" may reference a mood or the sky at night). One specific text-mining approach that holds promise for psychotherapy transcript data are topic models (also called, Latent Dirichlet Allocation; Blei, Ng, & Jordan, 2003) . Topic models are data-driven, machine learning procedures that seek to identify semantic similarity among groups of words. Similar to factor analysis in which observed item values are functions of underlying dimensions, topic models view the observed words in a passage of text as a mixture of underlying semantic topics. An advantage of topic models is that they construct a linguistic structure from a set of documents inductively, requiring no external input, but can also be used in a supervised fashion to learn semantic content associated with particular codes or metadata (where metadata is any data outside of the text itself; . There is recent work using these models to explore the structure of National Institute of Health grant applications (Talley et al., 2011), publications from the Proceedings of the National Academy of Sciences (Griffiths & Steyvers, 2004) , articles from the New York Times (Rubin, Chambers, Smyth, & Steyvers, 2012) , and the identity of scientific authors (Rosen-Zvi, Chemudugunta, Griffiths, Smyth, & Steyvers, 2010). Perhaps more strikingly, topic models have been used in the humanities to facilitate "distant reading" in comparative literature such that hypotheses in literary criticism can be tested vis-à-vis the entire corpus of relevant work (e.g., exploring stylistic similarities in poems, see Kao & Jurafsky, 2012; Kaplan & Blei, 2007) . With a few exceptions, topic models have yet to be applied to psychotherapy data (see Atkins et al., 2012 and also Salvatore, Gennaro, Auletta, Tonti, & Nitti, 2012 who used a derivative of latent semantic analysis-a forerunner to topic models; Landauer & Dumais, 1997). However, similar to the news articles, novels, and poems noted above, the words used during psychotherapy sessions by patients and therapists can be viewed as a large collection of text with a complex topical structure. The number of words generated during psychotherapy is quite large. A brief course of psychotherapy for a given patient may consist of 5-10 hr of unstructured dialogue including 12,000 -15,000 words per hour (approximately 60,000 to 150,000 words, longer courses of treatment Ͼ1 million words). In 2011, a PubMed search revealed 932 citations for psychotherapy clinical trials (out of 10,698 across all years). As a conservative estimate, if we consider 500 studies per year, 50 participants per study, 5 sessions per participant, and 10,000 words per session, this leads to an estimate of 125 M words of psychotherapy text per year from clinical trials alone. Regardless of the specific estimate, it is clear that a huge amount of psychotherapy data is generated every year and that this number is likely to increase. The use of discovery-oriented text-mining procedures such as topic models could facilitate new ways of exploring and categorizing psychotherapies based on the actual content of the patient and therapist speech (rather than labels established by schools of psychotherapy). To evaluate the potential of topic models to "learn" the language of psychotherapy, we applied two different types of topic models to transcripts from 1,553 psychotherapy and psychiatric medication management sessions. Our first goal was to verify that topic models would estimate clinically relevant semantic content in our corpus of therapy transcripts. Second, we determined if semisupervised models could identify semantically distinctive content from different treatment approaches and interventions (e.g., therapist "here and now" process comments about the therapeutic relationship within a session). A third aim was to explore the overall linguistic similarity and distinctiveness of sessions from different treatment types (e.g., psychodynamic vs. humanistic/ experiential). Our final goal was to classify treatment types of new psychotherapy sessions automatically, using only the words used during the session. The data for the current proposal come from two different sources: (a) a large, general psychotherapy corpus that includes sessions from a diverse array of therapies, and (b) a set of transcripts focused on motivational interviewing (MI), a specific form of cognitive-behavioral psychotherapy for alcohol and substance abuse. General psychotherapy corpus. The general corpus holds 1,398 psychotherapy and drug therapy (i.e., medication management) transcripts (approximately 2.0 million talk turns, 8.3 million word tokens including punctuation) pulled from multiple theoretThis document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 3 COMPUTATIONAL PSYCHOTHERAPY ical approaches (e.g., cognitive behavioral; psychoanalysis; MI; brief relational therapy). The corpus is maintained and updated by the "Alexander Street Press" (http://alexanderstreet.com/) and made available via library subscription. In addition to transcripts, there is associated metadata such as patient ID, therapist ID, limited demographics, session numbers when there was more than a single session, therapeutic approach, patient's primary symptoms, and a list of subjects discussed in the session. The list of symptoms and subjects was assigned by publication staff to each transcript, and no interrater reliability statistics were available. All labels were derived from the DSM-IV and other primary psychology/psychiatry texts. Many sessions were conducted by prominent psychotherapists who developed particular treatment approaches (e.g., James Bugental, existential; Albert Ellis, rational emotive; Carl Rogers, person-centered; William Miller, MI), and hence may serve as exemplars of these treatment approaches. To facilitate analysis we categorized each psychotherapy session into one of five treatment categories, (a) Psychodynamic (e.g., psychoanalysis, brief relational therapy, psychoanalytic psychotherapy), (b) Cognitive behavioral therapy (e.g., rationale emotive behavior therapy, MI, relaxation training, etc.), (c) Experiential/Humanistic (e.g., person-centered, existential), (d) other (e.g., Adlerian, Reality Therapy, Solution-Focused, as well as group, family, and marital therapies), and finally (e) Drug therapy or medication management. However, in some cases, when a label was missing or more than one treatment label was assigned to a session, collateral information in the metadata was used to assign a single specific treatment label (i.e., a well known therapist associated with a specific intervention, reported use of specific interventions, and/or inspection of the raw transcript). If there was no collateral information or an appropriate label could not be determined, the first listed intervention was chosen as the treatment name or the treatment label and category was left missing. In addition to treatment category, analyses used one subject label, "counselor-client relations." This session-level label (i.e., applied to an entire session) was assigned to a transcript when there was a discussion about the patienttherapist relationship or interaction during the therapy. MI corpus. We supplemented the general corpus above with a set of MI sessions (Miller & Rollnick, 2002 ; n ϭ 148, 30,000 talk turns, 1.0 million word tokens). Transcripts are a subset of sessions from five randomized trials of MI for drug or alcohol problems, including problematic drinking in college freshman (Tollison, Lee, Neighbors, & Neil, 2008) , 21st birthdays and spring break (Neighbors et al., 2012) , problematic marijuana use (Lee et al., 2014) , and drug use in a public safety-net hospital (Krupski et al., 2012) . Each study involved one or more in-person treatment arms that received a single session of MI. Sessions were transcribed as part of ongoing research focused on applying text-mining and speech signalprocessing methods to MI sessions (see, e.g., Atkins, Steyvers, Imel, & Smyth, in press). The linguistic representation in our analysis consisted of the set of words in each talk turn. A part-of-speech tagger (Toutanova, Klein, Manning, & Singer, 2003) was used to analyze the types of words in each talk turn. We kept all nouns, adjectives, and verbs and filtered out a number of word classes such as determiners and conjunctions (e.g., "the," "a") as well as pronouns. The resulting corpus dramatically reduces the size of the corpus to 1.2M individual words across 223K talk turns. We applied a topic model with 200 topics to this data set, treating each talk turn (either patient or therapist) as a "document." In the topic modeling literature, the document defines the level at which words with similar themes are grouped together in the raw data. We could define documents in a number of ways (e.g., all words in the session or all words from a specific person), but we have found in previous research within clinical psychology that defining documents by talk turns enhances the interpretability of the resulting topics. In a topic model, each topic is modeled as a probability distribution over words and each document (talk turn) is treated as a mixture over topics. Each topic tends to cluster together words with similar meaning and usage patterns across talk turns. The probability distribution over topics in each talk turn gives an indication of which semantic themes are most prevalent in the talk turn. For further details on topic models, see Atkins et al. (2012) . Results First, we used topic models to explore what therapists and patients talk about. As noted earlier, topic models estimate underlying dimensions in text, which ideally capture semantically similar content (i.e., the underlying "topics"). Thus, in applying topic models to psychotherapy transcripts, an initial question is whether the models extract relevant semantic content? Table 1 presents 20 selected topics (of 200 total) from an unsupervised topic model applied to all session transcripts (i.e., these topics were generated inductively without any input from the researchers). It is clear that the words in each topic provide semantically related content and capture aspects of the clinical encounter that we might expect therapists and patients to discuss. We have organized topics into four areas-(a) Emotions/ Symptoms, (b) Relationships, (c) Treatment, and (d) Miscellaneous. Similar to factor analysis, all labels were supplied by the current authors-the model itself simply numbers them. The top 10 most probable words for each topic are provided along with author-generated topic labels to aid interpretation. For example, the emotion category includes several symptom-relevant topics. Topic 15 (Depression) includes many of the specific symptom criteria for depression (e.g., sadness, energy, hopelessness; the word "depression" is the 16th most probable word), and topic 149 (Anxiety) includes words relevant to the discussion of a panic attack. The relationship category illustrates how a topic model can handle differences in meaning depending on context. Topic 146 (Sex) and 60 (Intimacy) include derivatives of the words relationship and sex. In Topic 60, these words occur in the context of words such as closeness, intimacy, connection, and open, suggesting these words had different implications than when they occur in Topic 60, which includes words such as desire, This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. enjoy, and satisfied. The basic topic model can infer differing meaning of identical words (e.g., play used in reference to theater vs. children) as long as the documents that the words occur in have additional semantic information that would inform the distinction (Griffiths, Steyvers, & Tenenbaum, 2007) . In the treatment category, topic 196 includes a number of medication names and is clearly related to discussions of psychopharmacological treatment. Topic 198 (Behavior Patterns) includes words that might be typical in the examination of behavior/thought patterns (e.g., irrational, pattern, behavior, identify). We considered labeling this topic "CBT," given words that might be found in an examination of thoughts in cognitive therapy. However, we found that this topic was actually more prevalent in psychodynamic sessions as compared with CBT sessions. This finding highlights the complexities of topic models. While the model returns a cluster of words, the researcher must infer what the cluster means. To demonstrate the utility of a topic model in the discovery of language specific to different approaches to psychotherapy, we used a "labeled" topic model wherein the model learns language that is associated with a particular label-in the present case a session-level label that identifies the type of psychotherapy (e.g., CBT vs. Psychodynamic). We used the output from this model to identify specific therapist talk turns that were statistically representative of a given label. In the general psychotherapy corpus, there were no labels or codes for talk turns, only for the session as a whole. Given the labels for each session and the heterogeneity of word usage across sessions, the model "learns" which talk turns were most likely to give rise to a particular label for the entire session. In Table 2 , we provide four highly probable talk turns for six different treatments. The depicted statements are what might be considered prototypic therapist utterances for each treatment. Client-centered talk turns appear to be reflective in nature, while utterances in rationale emotive behavior therapy have a quality of identifying irrational thought patterns. Brief relational interventions focus on here and now experiences, and the selected talk turns for MI were those typical for the brief structured feedback session that therapists were trained to provide in several of the MI clinical trials included in the corpus. Table 2 presents results from a labeled topic model using psychotherapy type as the label categorizing a session. We explored whether the model could learn more nuanced, psychological labels, focusing on "client-counselor relations"-a code that was used to label sessions that included discussions between client and therapist about their relationship/interaction. As with the identification of therapist talk turns, the client-counselor relations code was assigned to an entire transcript. Consequently, the model must learn to discriminate between language in these sessions that is irrelevant to the label (e.g., general questions, scheduling, pleasantries, other interventions, etc.) and language that involves the client and therapist talking about their relationship. Table 3 provides the five most probable therapist talk turns associated with the client-counselor relations label. Each talk turn is clearly related to Table 1
For example, in REF the authors use topic models to analyse psychotherapy and medication therapy transcripts.
Computational Psychotherapy Research: Scaling up the Evaluation of Patient-Provider Interactions
Psychotherapy
Psychotherapy
Psychology Medicine