Introduction

The assessment and diagnosis of personality and, in particular, of personality disorders (PDs) is a common practice of psychologists and psychiatrists. This practice is guided mainly by classical psychometric tools such as questionnaires and relies heavily on human judgment and expertise.

Recently, there has been an increasing interest in introducing novel computational methods for the assessment of personality and related disorders1,2,3,4,5,6,7,8,9. These methods involve “personality recognition” tasks consisting of “automatic classification of authors' personality traits that can be compared against gold standard labels” (1, p. 1). The methods used for computing personality apply Natural Language Processing (NLP) techniques and Machine Learning (ML) algorithms and use various features (e.g., n-grams) and resources [e.g., Linguistic Inquiry and Word Count10,] to classify the labeled data.

In this context, it was recently argued by Pianesi (7, p. 150) that “There is a tension between the invariance of personality traits and the natural variability of behavior in concrete situations that risks to seriously hamper current attempts at automatically predicting personality traits.” In other words, while personality is sometimes portrayed as invariant (e.g., John is an extrovert), situational context generates variability that washes out the personality's alleged stability. For instance, while John may be a typical extrovert in certain situations, such as within his close family circle, he may behave like a typical introvert in other situations.

The implication of Pianesi's argument is that an attempt to identify a canonical set of features and parameters for personality recognition may work well in a given context but may hold no validity in a different one. Therefore, Pianesi's criticism encourages a more flexible and context-dependent approach to personality assessment.

One possible way to address this challenge, in the specific context of text analysis, is by a priori characterizing a set of linguistic cues describing certain personality dimensions and then measuring the similarity of the text-in-context to each of these dimensions.

Following this logic, in this paper, we present a novel approach to personality assessment, which is based on the idea of vectorial semantics models (VSM)11,12, explained in the next section.

The idea of using a vectorial semantics approach to personality assessment emerged in the context of profiling political leaders for military intelligence and was recently and successfully used in the context of forensic psychiatry for the screening of potential mass-murderers. This study represents the first time in which this approach is validated against the “big-five” personality factors and by using thousands of essays written by students.

Vectorial semantics and personality assessment

Vector space models of semantics, suggest that the meaning of a word and the concept it represents can be identified by analyzing words co-occuring with our target word in a given context. For instance, if we want to understand the meaning of a Paranoid, we examine the adjectives co-located with it in texts. Using a corpus of the English language, we may find that the two adjectives most frequently co-located with Paranoid are: Suspicious (Frequency = 3) and Vengeful (Frequency = 6). This means that in the texts we have analyzed the word Paranoid is accompanied by the word Suspicious three times and by the word Vengeful six times. The shaded area in Table 1 presents these data.

Table 1 Adjectives co-located with Suspicious and Vengeful
Full size table

We can now consider Suspicious and Vengeful as two dimensions defining the semantic space of Paranoid. In this semantic space, the meaning of Paranoid is represented as a vector in a two-dimensional space defined by Suspicious and Vengeful, a point that is graphically represented in Figure 1.

Figure 1
figure 1

A graphical representation of table 1.

Full size image

Next, we find that Obsessive and Histrionic are two other words residing in this semantic space, as presented in Table 1.

Figure 1 is a graphical representation of Table 1. The X-axis signifies the dimension of Suspicious and the Y-axis signifies the Vengeful dimension. In this space, Paranoid is represented by the dashed vector whose coordinates are X = 3 and Y = 6. Along the same line, the bold vector represents Histrionic and the third vector Obsessive.

We can see that the vector of Histrionic is closer and therefore more similar to the vector of Obsessive than to the vector of Paranoid. Measuring the similarity of vectors is a simple but rigorous procedure that relies on the cosine between them.

In reality, the situation is much more complex than described as each word is accompanied by many other words that co-occur with it in a linguistic corpus. Therefore, instead of a simple two-dimensional space, as appears in Figure 1, we have to deal with a high-dimensional space. In addition, in some cases we prefer to measure the similarity of texts comprising many words rather than the semantic similarity of isolated words only. However, beyond the above complexities, the basic idea of representing the meaning of words as a vector in a high-dimensional semantic space and measuring the similarity between words/texts by measuring the distance between the vectors has been proved to be extremely powerful and may be used for personality assessment.

For example, assume we intend to assess the personality of a certain individual by analyzing a sentence he published on Twitter (e.g., I suspect that the CIA is responsible for this conspiracy) and determine whether it is indicative of a paranoid personality disorder. The vectorial semantics approach would propose to represent the relevant words in the sentence as a vector in a high-dimensional semantic space and to measure the distance between the vector comprised of these words and the vector of words representing the paranoid personality disorder. The closer the vectors are the more similar the sentence is to the personality vector and, therefore, our confidence increases in the hypothesis that the sentence represents the paranoid personality.

The first step in the vectorial semantics approach to personality assessment is therefore to identify words that are the best representatives of a certain personality trait. Our basic assumption is that experts can characterize personality types and specifically PDs by using a minimal set of words that grasp the essence of the disorder. For instance, while describing a paranoid personality disorder, the adjective “suspicious” emerges as a prototypical keyword (e.g.,13,14,15). Using a set of adjectives that describes a PD, we may automatically analyze the dimensions of a given text by simply representing it as a vector and measuring its similarity to pre-defined vectors of PDs. The above assumption has been intensively discussed in personality research16. Moreover, the assumption has been specifically prominent in the study of the Big Five personality traits that have been assessed through trait-descriptive adjectives17,18,19.

The vectorial semantics approach to personality assessment can be summarized as follows: (1) Based on theoretical and/or empirical knowledge select a set of words that represent a psychological trait. (2) Represent this set as a vector. (3) Choose a text you would like to assess and represent its words as a vector. (4) Measure the similarity between the vectors. The similarity score is indicative of the degree in which the personality trait is represented in the text.

The Five Factor Model and PDs

The Big Five or the five-factor model of personality (FFM)16,20,21 suggests that the taxonomy of personality can be described through five major traits. Extraversion (E) involves an “energetic approach” to the social and material world and includes traits such as sociability, activity and positive emotionality. Agreeableness (A) involves a pro-social and communal orientation and includes traits such as altruism, tender-mindedness, trust and modesty. Conscientiousness (C) describes socially prescribed impulse control and goal-directed behavior. Neuroticism (N) – sometimes referred to as its opposite pole, Emotional stability – involves negative emotionality and feeling anxious, sad and tense. Openness to experience (O) describes the breadth, depth and originality of the subjects' mental and experiential life.

The FFM has been of interest for studying various PDs and related behaviors (e.g. 22). Malouff et al.23 found that mood disorders were associated mostly with a higher level of N and lower levels of E, A and C. The study of24 further supports these associations by showing that depressive disorders are associated with high levels of A and low levels of C. In a recent study25, found that the presence and severity of depression among older adults was associated with a higher level of N and a lower level of E: Those who are neurotics and introverts are much more inclined toward depression.

In this study, we are interested in validating our vectorial semantics approach to personality assessment. To test the validity of our approach, we draw on the comprehensive meta-analytic review of the five-factor model and personality disorders26. This meta-analysis examines the relationships between each of the big-five personality dimensions and each of ten personality disorders.

We specifically draw on 23456Table 7 in Saulsman and Page26 (p. 1070). This table displays the binomial effect size for each personality disorder and five-factor personality dimension. The table shows “out of 100 people with a particular personality disorder, the number that would score high on a particular personality dimension and the number that would score low on the dimension”. For instance, it was found that, out of 100 paranoids, we should expect 64 to score High on the Neuroticism dimension and 36 to score Low. It means that a significantly higher proportion of Paranoids would be among those who are classified as Neurotics.

Table 2 Binomial effect size for the personality disorders and five-factor personality dimension
Full size table
Table 3 Cross-tabulation of the predicted vs. observed value of the O
Full size table
Table 4 Cross-tabulation of the predicted vs. observed value of the C
Full size table
Table 5 Cross-tabulation of the predicted vs. observed value of the A
Full size table
Table 6 Cross-tabulation of the predicted vs. observed value of N
Full size table
Table 7 Cross-tabulation of the predicted vs. observed value of the E
Full size table

In general, the meta-analysis reveals three general patterns: (1) that most PDs have “positive associations with neuroticism”, (2) that most PDs have “negative associations with agreeableness” and (3) that Extraversion is the more mixed and therefore “discriminating dimension”26 with positive and negative associations with the PDs.

Saulsman and Page decided that “effect sizes ≥ .20 would be considered meaningful”26. Although this cut-point is arbitrary, it is justified on practical reasons, which are elaborated in the paper. Following this decision, we decided to report only the meaningful effect sizes found in the meta-analysis. In addition, we focus only on PDs that we were able to translate into vectors and disorders such as “Borderline” that were difficult to conceptualize in terms of adjectives were not included in the study. Hence, Table 2 presents the meaningful effect sizes identified in the meta-analysis with seven PDs.

Research questions and hypotheses

Research question 1

In this study, we have defined PDs vectors and measured their similarity with essays written by students. The question is whether our results correspond with the three patterns identified in the meta-analysis26 and reported on, above. If our vectorial semantics approach to personality assessment can validly measure the degree in which a PD is evident in a text, then the relation between our PDs scores and the five-factors should reproduce the above patterns. Therefore, we hypothesized that:

  • Hypothesis 1: Subjects classified as N (i.e., Neurotics) will score higher than subjects classified as non-N on the Paranoid, Schizotypal, Avoidant and Dependent PDs scores.

  • Hypothesis 2: Subjects classified as non-A will score higher than subjects classified as A on the Paranoid, Schizotypal and Narcissistic PDs.

  • Hypothesis 3: Non-Extraverts will score higher on Schizoid, Schizotypal and Avoidant PDs and will score lower on the Histrionic and Narcissistic PDs.

Research question 2

While our main aim is to provide minimal evidence to the validity of our approach, our second question is: can one classify the subjects into the different values of their personality factors (Neurotic vs. non-Neurotic) based on the vectorial analysis we have developed? This question concerns the pragmatic value of using the vectorial semantics approach for personality assessment.

To address this challenge, we constructed vectors representing the five personality factors (i.e., A, E, C, N and O). Drawing on the list of adjectives provided by Trapnell and Wiggins18, we constructed vectors of the personality dimensions along the lines used for constructing the PDs. For each dimension we constructed one vector representing the existence of the dimension (e.g., E) and another vector representing its opposite (e.g., non-E or E-negative). The vectors are:

  • E-POSITIVE: dominant, assertive, authoritarian, forceful, assured, confident, firm, persistent

  • E-NEGATIVE: nervous, modest, quiet, forceless, afraid, shy, calm, indecisive

  • A-POSITIVE: Tender, Gentle, Soft, Kind, Affectionate Helpful Sympathetic Friendly

  • A-NEGATIVE: Cruel Unfriendly Negative Mean Brutal Inconsiderate Insensitive Cold

  • C-POSITIVE: Organized Orderly Tidy Neat Efficient Persistent Systematic Straight Careful Reliable

  • C-NEGATIVE: Distracted Unreliable Incompetent Wild Inefficient Disloyal Chaotic Confused Messy Disorganized

  • N-POSITVE: Worried Stressed Anxious Nervous Fearful Touchy Guilty Insecure Restless Emotional

  • N-NEGATIVE: Balanced Stable Confident Fearless Calm Easy_going Relaxed Secure Comforted Peaceful

  • O-POSITIVE: Philosophical Abstract Imaginative Curious Reflective Literary Questioning Individualistic Unique Open

  • O-NEGATIVE: Narrow-minded Concrete Ordinary Incurious Thoughtless Ignorant Uneducated Common Conventional Restricted

The similarity of each vector to the essay was calculated using the same procedure described for each of the nine PDs. These similarity scores, which are meant to be indicative of the extent in which the personality factor is evident in the text, were titled the Personality Factor Scores.

Results

Research question 1

Regarding Hypothesis 1; it was found that, when the PD scores of Neurotics and non-Neurotics were compared, the Mean Rank of N is significantly higher for the Paranoid (z = 3.50, P = .000), Avoidant (z = 4.36, P = .000) and Dependent (z = 3.78, P = 001) PDs. The difference was not statistically significant for the Schizotypal PD, although N scored higher than non-N. These results largely confirm our hypothesis. Regarding Hypothesis 2; using the same procedure, it was found that non-As scored higher with regard to the Paranoid (z = 2.09, P = .05), Schizotypal (z = 4.23, P = .000) and Narcissitic (z = 2.84, P = .002) PDs. These results fully support the hypothesis. Regarding Hypothesis 3; the direction of the difference was in line with the meta-analysis results of26 only for the Schizoid (z = 2.16, P = .016) PD where non-E scored higher than E.

Overall, there seems to be a good match between the patterns presented in the meta-analysis of26 and our results. This match provides empirical support for the validity of our PDs measures. The results concerning the E factor were partially supported, although mixed results were clearly evident with regard to this personality dimension and associated PDs.

Research question 2

The classification model we have used classifies subjects into the value of each of the five factors by using the personality vectors automatically extracted from their essays. For example, the model classifies a certain subject as Neurotic or non-Neurotic based on his PDs Scores and Personality Factor Scores. Cross-tabulating the value of each of the five personality vectors (e.g., E vs. non-E) by their predicted values according to the classification procedure, the results were found statistically significant for all of the five factors. Tables 3,4,5,6,7 present the cross-tabulation for each of the big five personality factors. Table 8 presents the accuracy of the classification procedures and the χ2 for each cross tabulation. All results were found statistically significant (P = .000). Across personality dimensions and on average, our analysis gained 60% accuracy, which is slightly higher than the average best performances reported by27 (i.e., 57.09%).

Table 8 Accuracy of the classification procedure
Full size table

The classification procedure includes a sub-procedure that allows us to rank the independent variables according to their normalized importance to the model. Here we can see in a descending order the three most important predictors that were found for each personality factor:

E: N-Negative, C-Positive, Paranoid

N: N-Positive, Depressive, Avoidant

A: C-Negative, N-Negative, O-Negative

C: N-Negative, Schizoid, Histrionic

O: O-Negative, Histrionic, Schizotypal

Discussion

The vectorial semantics approach merges the idea of VSM with personality assessment. To the best of our knowledge, this is a novel approach that is different from the other approaches used for automatic personality assessment. Using this approach for measuring the level of each PD in a text, our results largely agree with the most comprehensive meta-analysis that examined the relationships between the big five factors and PDs. While this agreement provides empirical support for our approach, it cannot be considered as a final validation but only as a first and primary step in providing empirical support for a minimal level of validity. Our secondary aim was to test our approach in a classification task. We must emphasize that, although we did not aim to compete with various ML approaches for classifying the “Big 5” of personality as done in a recent workshop1, our approach has gained a higher level of accuracy when compared with the results gained elsewhere27. Fusing our approach with various ML algorithms described in (1) may improve current performance on this task. In addition, our analysis has been constrained by the binary nature of the dependent variables and the predictive strength of our approach may be increased by considering the Big-5 factor scores dimensionally i.e. as continous scores. Our approach may also have various practical applications. For instance, the approach may be used for automatically assessing the personality disorders of people who find it difficult to gain access to mental health services. These subjects may write a short essay that can then be analyzed through our approach and their PDs scores may be compared to a benchmark of essays written by healthy subjects. Subjects who score significantly higher on certain PDs may be advised to approach a mental health expert for an in-depth diagnosis. This form of automatic screening procedure, which has been proposed in various contexts such as in the automatic diagnosis of depression28, will assist both patients and mental health authorities struggling with overload and lack of resources. However, the specific applications of the approach presented in this study must be better planned and elaborated. We may therefore conclude our paper with an invitation for further research and collaboration.

Methods

Data

We used the Essays dataset provided to the participants of the “Workshop on Computational Personality Recognition: Shard Task”1. As explained by the organizers of the workshop, this is a corpus of 2468 stream-of-consciousness texts that was produced by29 and labeled with personality classes of the FFM.

The essays were written by students who were also assessed through a standard inventory of30 for their score on each of the big five personality factors. The personality scores obtained from the Big5 test have been normalized by27 and turned into nominal classes by1 as this workshop focused on a classification task of the big five through a ML approach. The labels in the dataset are provided as categorical variables with a balanced frequency of around 50% in each category. The percentage of subjects in each personality dimension is presented in Table 9. It is important to remember that the Big-5 varibles are usually conceptualized in the literature as dimensional, as opposed to categorical (i.e., one is not “Open” or “Not-open,” but instead falls somewhere along a continuum with regard to the personality characteristic). However, the nature of the data set we have used and the methodlogical approach necessitated a binary approach to these characteristics for purpose of the present study.

Table 9 Percentage of subjects in each of the personality dimensions (N = 2468)
Full size table

Defining the PDs vectors

We used the definition of Millon et al.14 for PDs and extracted the first main adjectives14 used to define each of nine PDs. The adjectives were checked against the Oxford English Dictionary and the Corpus of Contemporary American English31. In the case where an adjective has a synonym that perfectly preserves the original meaning while having a higher frequency in the population, the adjective has been replaced. In addition, two-word adjectives have been replaced with a one word description wherever possible. The following words were used to define Millon's PD Vectors:

  1. 1

    Schizoid: indifferent, apathetic, remote, solitary

  2. 2

    Depressive: sad, depressed, hopeless, gloomy, fatalistic

  3. 3

    Avoidant: shy, reflective, embarrassed, anxious

  4. 4

    Dependent: helpless, incapable, passive, immature

  5. 5

    Histrionic: dramatic, seductive, shallow, hyperactive, vain

  6. 6

    Narcissistic: selfish, arrogant, grandiose, indifferent

  7. 7

    Compulsive: restrained, conscientious, respectful, rigid

  8. 8

    Paranoid: cautious, defensive, distrustful, suspicious

  9. 9

    Schizotypal: eccentric, alien, bizarre, absent

Preprocessing

Each essay was analyzed through a Part-of-Speech tagger32 and only nouns, verbs, adjectives and adverbs were processed for further analysis. Next, we measured the similarity between each of the essays and each of the PDs vectors by using the term-to-context matrix developed by Turney33,34. More specifically, we drew on the dual-space model developed by Turney33,34. This model allowed us to measure the semantic similarity of words and texts by using the “dual space” and examining the “domain” similarity of words (i.e., their “topic” similarity) and their “function” similarity (i.e., similarity of rule or usage). In this paper, we used the “mono” similarity matrix33 that combines the two measures. By using this matrix, we measured the similarity between the vector of each essay and each of the personality factors. The result is a similarity score. The higher the score the closer the essay is to the personality vector. Next we classified each similarity score as follows: For each similarity measure (e.g., the similarity between the essay and the Paranoid vector) we found the .75 percentile, i.e., the score above which only 25% of the subjects scored. Essays that scored higher than the .75 percentile score were classified as “1” and the rest as “0”. The output of this procedure was, that for each essay/subject, we had nine binary scores indicating whether the essay/subject could be characterized by the PD or not. These scores are titled the PDs scores.

Procedure

To test hypotheses 1–3, we used the Mann-Whitney U Test with the personality vector (e.g., E versus non-E) as the grouping variable and the PDs scores as the test variables. For example, we compared the PDs scores of Neurotics vs. non-Neurotics. Given the ordinal scale of the test variables and the unique test ranking procedure, we considered it appropriate for our data analysis. Moreover, in the analysis, we applied a Monte Carlo Estimate to gain an unbiased estimate of the exact level of significance, by repeatedly sampling from our dataset. Specifically, we used a Monte Carlo procedure with 10,000 samples and a 95% confidence interval. All significance tests used in this study were one-tailed.

For the classification task, we used a tree-based classification model. As explained by Cosma Shalizi, “The basic idea is very simple. We want to predict a response or class Y from inputs X1;X2; : : :Xp. We do this by growing a binary tree. At each internal 1 node in the tree, we apply a test to one of the inputs. Depending on the outcome of the test, we go to either the left or the right sub-branch of the tree. Eventually we come to a leaf node, where we make a prediction. This prediction aggregates or averages all the training data points that reach that leaf” [www.stat.cmu.edu/~cshalizi/350/lectures/22/lecture-22.pdf]. A layman-friendly explanation of decision tree-based classification appears in Wikipedia [http://en.wikipedia.org/wiki/Classification_and_regression_tree]. Specifically, we used the Classification and Regression Tree (CRT) model, which splits the data into segments that are as homogeneous as possible with respect to the dependent variable. This is a common machine-learning approach to classification35, which is detailed in36. We used the factor value (e.g., Neurotic vs. non-Neurotic) as a dependent variable and the nine PDs scores and the ten five factor scores gained through our vectorial analysis as independent continuous variables. For example, we attempted to classify our subjects into Neurotics and non-Neurotics by using the above set of vectors. Overall, we ran the procedure five times, once for each of the five personality factors. For improving the validity of our results, we have used a ten-fold cross validation procedure also known as rotation estimation. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set) and validating the analysis on the other subset (called the validation set or testing set). The non-expert may find an easy explanation of this procedure in Wikipedia. The classification procedure produced a predicted value for each subject, for instance whether s/he is Neurotic or not. These predictions were cross-tabbed against the real observed score and a Chi Test was used to measure the significance of our classification model.