Demystifying Topology of Autopilot Thoughts: A Computational Analysis of Linguistic Patterns of Psychological Aspects in Mental Health Bibekananda Kundu and Sanjay Kumar Choudhury Language Technology, ICT and Services Centre for Development of Advanced Computing, Kolkata E-mail: {bibekananda.kundu,sanjay.choudhury}@cdac.in Abstract nebaker et al., 2003). All psychological in- terventions rely on the power of language. The paper investigates topology of un- Psychotherapists rarely intervene directly in controlled dynamic depressive thoughts their client’s lives, they create changes in the which is popularly known as “autopi- thought process through conversation (Vil- lot” in the psychology domain. Per- latte et al., 2015). According to Relational sistent homology, a mathematical tool Frame Theory (RFT) (Greenway et al., 2010), from algebraic topological has been people use linguistic frames to understand the applied on Vector Space representa- world around them, and subsequently solve tion of tweets generated by users hav- problems. RFT has been suggested as an ing neurotic personality for determin- approach to understanding natural language ing the topological structure of autopi- systems. The theory lends itself well to as- lot thoughts. State-of-the-art machine sessment with Natural Language Processing learning techniques leveraging linguis- (NLP) precisely because it relies on under- tic resources akin to LIWC, WordNet- standing interaction between sensation, affect, Affect and SentiWordNet have been language, and behaviour. When someone uses applied for identifying neurotic per- language, they are labelling their experience. sonality from different Twitter users. For example, someone might tweet “I need to An initiative has been taken for em- escape this world before I get crushed.” indi- powering Neuro Linguistic Program- cating a fear based affective response. We are ming (Bandler and Grinder, 1975; Ban- planning to use NLP to assess this label (Pen- dler and Grinder, 1979; Bandler and nebaker et al., 2015). Simply labelling events Andreas, 1985) and other psychother- and their attributes as ‘positive’ or ‘negative’ apy techniques using Natural Language increases associated memories and emotional Processing in the domain of Mental salience. This type of relational network can Health. be evoked with any number of internal or ex- ternal stimuli, triggering the aforementioned 1 Introduction internal feedback loop, and leading to psy- “Wherever there are sensations, ideas, emo- chological distress. For example, describing a tions, there must be words” ‘negative’ event such as a trauma can evoke — Swami Vivekananda. intense fear and sadness and subsequent sob- We use language for thinking, experienc- bing (Miner et al., 2016; Althoff et al., 2016). ing, expressing, communicating and problem The person suffering from distress actually us- solving. So, to analyze one’s thought pro- ing a model of world which is very limited cess, language is a symbolic medium. In psy- and in this world he/she find no appropri- chotherapy, language is considered as a pri- ate choice from the options available to their mary tool to understand patients’ experiences model of world (Bandler and Grinder, 1975; and express therapeutic interventions (Pen-435 Bandler and Grinder, 1979; Bandler and An- S Bandyopadhyay, D S Sharma and R Sangal. Proc. of the 14th Intl. Conference on Natural Language Processing, pages 435–446, Kolkata, India. December 2017. c 2016 NLP Association of India (NLPAI) dreas, 1985). Therefore, there is a requirement of these semantically embedded words using of expanding the model of world i.e. impro- persistent homology (Zhu, 2013; Kaczynski et vise the model to a better model which has al., 2004). We have intuition that timeline of more options. Therefore, the therapeutic tech- neurotic person contains different topological nique would be somehow transforming the ex- structure than timeline of user having other isting model to a better model using a meta- personality. Studies say that neurotic per- model and transformational grammar. The son uses more first person pronoun, less so- linguistic theory plays a vital role to under- cial words, more negative emotion words (Pen- stand the client model and transform it us- nebaker, 2011). An introvert person uses sin- ing transformational grammar (Bandler and gle topic, discusses more regarding problem, Grinder, 1975). Therefore, one of the key con- uses few self-references, many tentative words, cerns of psychotherapy is to understand topol- many negation as compare to extrovert per- ogy of the maladaptive autopilot thoughts and son (Mairesse and Walker, 2007). Topological changing the topology of thought process us- data analysis using persistent homology has ing Mindfulness (Collins et al., 2009), Collab- been discussed in the section 5. In the next orative Empiricism (Beck and Emery, 1979; section, we will discuss our data collection pro- Kazantzis et al., 2013) and other talk ther- cedure from Twitter. apy techniques (Pawelczyk, 2011; Ebert et al., 2015; Mayo-Wilson and Montgomery, 2013; 3 How to Collect Tweets of Mohr et al., 2013). In this regards, un- Neurotic Persons derstanding of topology of uncontrolled dy- The proposed approach utilizes an ensem- namic depressive thought (known as “autopi- ble of state-of-the-art machine learning tech- lot thought” in psychology) is important for niques based on psycholinguistic features to evaluating mental health of patients. After a detect distress users (having neurotic person- brief discussion on psychological background ality) from their social media text. We have and motivation behind the work, we will un- used Twitter API to search in the Twitter derstand how we can represent topology of using some seed words/phrases like “awful”, thought in the next section. “terrible”, “lousy”, “hate”, “lonely”, “hope- less”, “helpless”, “crap”, “sad”, “miserable”, 2 How to Represent Topology of “tired”, “sleep”, “hurt”, “pain”, “kill”, “die”, Thought “dying”, “stressed”, “frustrated”, “irritated”, “depressed” etc. and name of some antide- We are considering written text as a sym- pression drugs like “Sertraline”, “Citalopram”, bolic representation of thoughts. To under- “Clonazepam”, “Propanol”, “Prozac”, “Zopi- stand the topology of “autopilot thoughts”, clone”, “Fluoxetine”, “Quetiapine”, “Hydrox- we have collected tweets of neurotic person- yzine” etc. Next, we have filtered out the ality from Twitter applying a hybrid approach tweets starting with RT to avoid considering combining Deep Learning based classification, retweets. We have also removed tweets con- KL-Divergence (Manning and Schütze, 1999) taining url. Thereafter, these tweets are sent based Timeline Similarity Analysis and Rule- to (in house developed) Psychological Anno- based sentiment analysis technique leveraging tation Interface for manual annotation. Fig- WordNet-Affect1 , SentiWordNet2 and psy- ure 1 shows screenshot of the interface along cholinguistic resource akin to LIWC3. Detailed with some examples of negative tweets. An data collection procedure has been discussed annotator can label a tweet considering three in the section 3 and 4 .We are interested to aspects Viz: study the representation of words used by neu- rotic persons in the Vector Space using Word (a) Personal/Impersonal Emotion Labelling Embedding (Mikolov et al., 2013; Mesnil et (b) Polarity Labelling al., 2013) and topology (Sizemore et al., 2016) (c) Psychological Annotation 1http://wndomains.fbk.eu/wnaffect.html 2http://sentiwordnet.isti.cnr.it Individual words in a tweet are annotated ac- 3http://liwc.wpengine.com/ 436 cording to Psychological Process as discussed Figure 1: Psychological Annotation Interface in (Pennebaker et al., 2015). Special care students used more first person singular pro- has been taken during annotation to find out nouns, more negative emotional words, and “Linguistic Marker of Depression” (Bucci and slightly fewer positive emotion words in their Freedman, 1981; Pyszcynski and Greenberg, essays about coming to college, relative to stu- 1987) in the tweet. Pronouns tell us where dents who had never experienced a depressive people focus their attention. If someone uses episode. These results are in line with (Pysz- the pronoun “I”, it’s a sign of self-focus. De- cynski and Greenberg, 1987) self-awareness pressed people use the word “I” much more theory. Therefore, in our Psycho-logical An- often than emotionally stable people (Pen- notation Interface, we have implemented a fea- nebaker, 2011; Ramirezesparza et al., 2008; ture to highlight tweets with red back ground Nguyen et al., 2014). Researchers have found containing First Person Personal Pronoun i.e. that people who frequently use first-person “I”. Focus on temporal orientation of people singular words like “I”, “me” and “myself” are that is how often they emphasize the past, more likely to be depressed and have more in- present and future is necessary because it af- terpersonal problems than people who often fects their health and happiness (Zimbardo say “we” and “us”. Using LIWC2001, (Stir- and Boyd, 2008). We are interested the pro- man and Pennebaker, 2001) found that sui- portion of a user’s tweets that the analytic cidal poets were more likely to use first per- finds evidence in: Insomnia and Sleep Dis- son pronouns (e.g., “I”, “me”, “mine”) and turbance which is often a symptom of men- less first plural pronouns (e.g., “we”, “ours”) tal health disorders (Weissman et al., 1996; throughout their writing careers than were De Choudhury