H ebrew Psychological Lexicons (2021)

Abstract: We introduce a large set of Hebrew lexicons pertaining to psychological aspects. These lexicons are useful for various psychology applications such as detecting emotional state, well being, relationship quality in conversation, identifying topics (e.g., family, work) and many more. We discuss the challenges in creating and validating lexicons in a new language, and highlight our methodological considerations in the data-driven lexicon construction process. Most of the lexicons are publicly available, which will facilitate further research on Hebrew clinical psychology text analysis. The lexicons were developed through data driven means, and verified by domain experts, clinical psychologists and psychology students, in a process of reconciliation with three judges. Development and verification relied on a dataset of a total of 872 psychotherapy session transcripts. We describe the construction process of each collection, the final resource and initial results of research studies employing this resource.

## introduction a lexicon is the vocabulary of a domain of knowledge, and can be a valuable tool in the analysis of many psychological tasks. for example, in detecting clients' mental states, emotions and symptoms @xcite . lexicons are especially advantageous when data is scarce. often in psychotherapy research, few samples are available in clinical trials, and confidentiality limits sharing of data. scarcity of data is particularly challenging in less common languages like hebrew. recent data-hungry models are not practical in such cases where data is small, while other approaches, applying the use of lexicons, are more effective for predictive abilities. moreover, lexicons can be shared across studies and serve as clinical markers (e.g., @xcite . additionally, through their simplicity, lexicons enable easy interpretation of results. they can be elaborate for indicating psychological states within text, e.g., in accordance to the frequency of specified terms within a passage @xcite . lexicons are widely used in research and industry due to their proven effectiveness and ease of use. there are several psycho-linguistic lexicons, amongst them the linguistic inquiry and word count (liwc) @xcite , @xcite , nrc-sentiment-emotion-lexicon (mohammad and turney, 2013), mrc @xcite , and dlatk @xcite , however no valid psycho-linguistic lexicon for hebrew exists. 1 several approaches are generally employed for developing lexicons. one prevalent method involves judging collected words with domain experts @xcite or with crowdsourcing @xcite . there are also various methods for translating existing lexicons from other languages (e.g., triangulation-based, machine translation and then manual fine-tuning). however lexicon translation tends to be impractical since direct translation leads to incomplete or wrong results @xcite . in particular, the hebrew language poses many word-level translation obstacles due to its morphologically-rich form and ambiguous orthography (as outlined in section 2). we describe the development of a collection of hebrew psychological lexicons that were created between the years 2018 and 2021. we utilize a base dataset of 872 psychotherapy sessions, described in section 3, to either validate or extract words for the lexicons. the first set of lexicon collections (section 4) are devised by domain experts, and verified using the base dataset. the word lists in the second set (section 5) are fully automatically generated based on the dataset, and mainly serve for textual analysis of psychotherapy sessions. section 6 combines domain experts and automatic methods for the preparation of lexicons. for each of the lexicon collections and methods, we provide a use-case in the clinical psychotherapy domain, illustrating their usefulness and effectiveness. see most of the lexicons freely available, 2 which will facilitate further research on hebrew clinical psychology text analysis. the methods described may also aid in the establishment of additional lexicons in hebrew and in other languages. ## challenges with lexicon translation while methods for translating existing lexicons from other languages have been exploited before, lexicon translation yields wrong categorization of words @xcite . this is particularly the case when involving morphologically rich languages, and is also due to word ambiguity and cultural influence on languages. in hebrew, like in other semitic (e.g., arabic) and indo-european languages (e.g., spanish, dutch), there are inflections and verb conjugations 2 https://github.com/natalieshapira/ hebrewpsychologicallexicons . as liwc is commercial, we cannot publicly release the translated lexicons described in section 6.1 that have no direct conversion in english. van wissen and boot (2017) address the problem by converting each word in a lexicon to its lemma (i.e., canonical form) and then using an existing list to expand to the various linguistic conjugations. in hebrew it is possible to retrieve all the different inflections and verb conjugations for many words using specialized linguistic lexicons, such as the mila lexicon @xcite . 3 even so, it is not always the case that all forms of a word should be included in the same lexicon. for example, in the emotion variety lexicon collection (section 4.2), the word ‫רגוע‬ 'ragua' (relaxed) appears in the not-nervous lexicon and ‫תרגיע‬ 'targia' (calm down) appears in the not-guilty lexicon, sharing the same root form but having different semantic emotional classification. in addition, there may be situations of ambiguity in which words with completely different meanings are mapped to the same lemma, e.g., the words (1) ‫חימה‬ 'chema' (anger) and ‫חמה‬ 'chama' (sun) have the same orthographic lemma ‫;חמה‬ (2) ‫עדשות‬ 'adashot' (contact lenses) and ‫עדשי‬ 'adashim' (lentils) have the same orthographic lemma ‫עדשה‬ 'adasha', thus adding noise to the directly-translated lexicon. furthermore, when expanding a lexicon around a word, ignoring diacritics often yields ambiguous forms. for example, while the word ‫אחלה‬ 'achla' (cool) is in the positive emotion lexicon (section 4.1), without diacritics the optional base forms are ‫איחל‬ 'ichel' (wish), ‫חילה‬ 'chila' (to make ill), ‫אחלה‬ 'achla' (cool) and ‫חלה‬ 'chala' (to become ill), having different emotional polarity. then, each of these words is also expanded with all their inflections, e.g., ‫חליתי‬ 'chaliti' (i became ill), adding up to hundreds of words to the wrong lexicon. another problem is that there are lexicon types whose translation is not straightforward. for example, the i words lexicon in liwc is a small set of 12 distinct words (e.g., i, me, mine) (tausczik and pennebaker, 2010) and can be used to count the frequency of all the occurrences of first-person mentions in a given text passage in english. however, hebrew's morphological system preclude such word-counting method for seeking "i words" in the text passage, as the first-person status is often realized morphologically, and may appear on many word forms. hebrew words follow a complex morphological structure, with both derivational and inflectional elements, that can encode gender, number, tense, person, possessive and nouncompounding. for example, ‫אהבתי‬ 'ahavti' (i loved), ‫אוהב‬ 'ohav' (i will love), ‫אוהבת‬ 'ohevet' (ifeminine love/she loves), ‫אהובי‬ 'ahuvi' (my love), therefore, preprocessing of syntactic and morphological parsing is a critical phase for extracting the relevant details (e.g., the first person singular counts). lastly, the ambiguous interpretation in different languages makes out-of-context translation impossible. for example, the word 'dear' will be translated in hebrew to the word ‫יקר‬ 'yakar' , but ‫יקר‬ 'yakar' also means 'expensive'. while 'dear' in liwc is a word with positive polarity, 'expensive' is not. we cannot assume that if a resource is valid in language a, then its translation into language b will necessarily give us a valid resource in language b. relatedly, language is strongly culturally influenced, and a word may be categorized differently across languages and cultural context in terms of human psychology, especially around emotion or sentiment @xcite . for example, the color green, will refer to jealousy and envy in some cultures: "green-eyed monster" was first used by william shakespeare about jealousy. there are proverbs in hebrew that associate envy to the green color: "green with envy". in addition, in hebrew ‫ירוק‬ ('yarok' green) can be used as a mockery of a person with no experience in his or her field, like an unripe fruit, especially used in the military context-a recruit. in contrast, green serves as a religious/sacred symbol in islam as muhammad's favorite color. (see also cultural differences in a study that examined the relationship between colors and emotions by @xcite 3 base dataset description all our lexicons rely on a dataset foot_2 of a total of 872 psychotherapy session transcripts from 74 different client-therapist dyads (pairs) consisting of a total of about 5 million tokens-100 thousand word types (unique words). all sessions are labeled with psychological analysis information that assists in generating a lexicon and/or verifying one. we infer relevant session-level labels from questionnaires filled by the participants at each session: (1) clients self-reported their well-being, measured using the ors questionnaire @xcite , which is considered to be an indicator for progress in treatment; (2) therapists and clients reported on interpersonal relational events that occurred during a session, corresponding to tensions or breakdowns in their collaborative relationship (alliance ruptures), measured by the psq questionnaire @xcite ; (3) therapists and clients reported emotional states measured by the poms questionnaire @xcite . ## lexicons based on expert knowledge the approach employed for creating the following lexicons is inspired by that of @xcite , specifically via a three-judge (domain experts) reconciliation procedure for admitting words into a lexicon. ## data-driven word lists we next describe data-driven methods, applied on our base dataset, that extract lists of words for purposes of psychotherapetic analysis of session transcripts. ## lexicons based on expert knowledge and automatic methods this section describes lexicons that are automatically converted or expanded from existing expertbased lexicons. ## limitations the lexicons presented are based on a unique dataset of psychotherapy session transcripts. the language used by clients and therapists in these sessions do not necessarily reflect the language naturally occurring in other settings. additionally, the statistical demographics of the participants in the utilized sessions are not fully balanced in terms of gender, age, education and relationship status (see appendix a.1.1 for details). again, this may influence the overall language observed, and in turn, the computations performed throughout our work in generating and verifying the lexicons. ## conclusion we present a collection of novel hebrew lexicons, based on psychological data and domain expert knowledge. we describe a variety of lexicon development methods: expert-knowledge-based, datadriven using labeled data and unsupervised learning. we address levels of reliability-agreement between three judges (expert knowledge) versus automatic methods that are vulnerable to noise. we describe the importance of the lexicons for psychology research, as well as initial uses cases with results. the lexicons are released for the benefit of the community, contributing to psychological textanalysis research in hebrew and cross-lingual research in general. furthermore, we hope that the methods described will inspire the creation of additional lexicons in hebrew and in other languages.