Lexical-semantic resources: yet powerful resources for automatic personality classification (2018)

Abstract: In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.

## introduction automatic personality classification (apc) has been employed on user generated content (ugc), such as tweets, to collect the user personality for various personalized intelligent applications, including recommender systems @xcite , mental health diagnosis @xcite , recruitment and career counseling @xcite . especially, the recommender applications benefit from knowing the personality of real as well as fictional characters @xcite . for example, if a user is known to favor the personality traits displayed by the main characters of, say, terminator foot_0 and rambo 1 , then the system should automatically recommend movies with similar characters. currently, the performance of apc depends on how user personality is modeled and what types of personality features can be extracted. regarding the first factor, one well-known model called five factor model @xcite has been highly accepted as a standard model. it consists of five personality traits (i.e., extraversion, neuroticism, agreeableness, conscientiousness, openness to experience). the apc task is then formulated as a regular document classification on these five labels. to the second factor of feature extraction, the existing studies heavily depend on personality specific resources such as linguistic inquiry word count (liwc) @xcite . these resources, however, are rather time consuming and expensive to construct especially for minor languages @xcite . moreover, the resource construction requires expertise in both psychology and linguistic (e.g., liwc). in contrast, it is observed that lexical-semantic features which could be extracted from the publicly available lexical resources (e.g., wordnet @xcite ) can help to improve the performance of the apc task. however, their impact on real world ugc data for apc had been relatively unexplored. among lexical-semantic features, senselevel features were explored in previous works @xcite with varying conclusions. in this paper, we conduct extensive experiments, aiming at obtaining a more detailed understanding of whether or not the senses can be beneficial in certain cases compared to word-based fea-tures. broadly, we explore the use of word senses, supersenses, and wordnet sentiment features @xcite in personality classification. our main contributions are: the rest of this paper is organized as follows. section 2 presents the related work and our novel contributions, as well as background knowledge of the five factor model. section 3 describes the experimental datasets. our proposed framework and methodology are presented in section 4. experimental results and discussion are in section 5. section 6 concludes this paper. ## related work and background previous studies concerned the positive impact of sense-level features (i.e., using word-net based wsd) on the performance of document classification systems @xcite @xcite . though they had different focuses, they suggest that word senses are not adequate to improve text classification accuracy. @xcite report an improvement from 0.70 to 0.76 f-score while negative results have been reported by @xcite . this is why supersenses, the coarse-grained semantic labels based on wordnet's lexicographer files, have recently gained attention for text classification tasks. in this paper, we further explore the impact of these features in personality prediction. there have been many different attempts to automatically classify personality traits from texts. however, there were not any studies incorporating senses, supersenses, and sentiment features into the apc. some works @xcite @xcite start from the data and seek linguistic cues associated with personality traits, while other approaches @xcite @xcite make heavy use of external resources, such as liwc @xcite , mrc @xcite , nrc @xcite , sentistrength foot_2 , where they detect the correlations between those resources and personality traits. however, the resources require the efforts of experts in psychology and linguistics, e.g., liwc of @xcite , to construct. this constrains the available resources for apc, especially for minor languages. thus, we aim at broadly available resources (e.g., wordnet and sentiwordnet), to benefit apc. close to our work, mairesse et al. (2007) run personality prediction in both observer judgments through conversation and selfassessments using text via the five factor model. they also exploit two lexical resources as features, liwc and mrc, to predict both personality scores and classes using support vector machines (svms) and m5 trees respectively. as for personality prediction on social network data, @xcite use both linguistic features (from liwc) and social features (i.e., friend count, relationship status). recently, @xcite deal with the automatic personality classification based on users social media traces, which include three of the four datasets in our study. however, similar to other studies @xcite , they mainly use the personality specific resources. at the time of writing, the use of personality specific resources for apc has received much attention, while the impact of lexicalsemantic features has been neglected. the only existing work that explores sense-level features is from @xcite . they partially used sense-level features among others (i.e., lexical features, stylistic features, and word embedding features) for personality profiling of fictional characters. as a complement of the existing work on automatic personality classification, the novel contributions of this paper include: ## methodology we denote four kinds of features as @xmath0, sense, s sense, senti} where word is a set of word-level features, sense is a set of sense-level features, s sense is a set of wordnet supersense features, and senti is a set of sentiment features. (s sense) is extracted from wordnet supersenses as a complement to sense. regarding senselevel feature, we applied two different word-net based wsd algorithms, simlesk and mostfreq @xcite . correspondingly, instead of sense, we have two different feature sets wn-s-lesk and wn-mfs. thus, we finally have the feature list of @xmath1, wn-s-lesk, wn-mfs, s sense, senti} ## experiment and analysis we conducted extensive experiments to investigate the impact of different lexical-semantic features on the apc task. all the feature abbreviations we use are listed in ## conclusion this paper presents extensive experiments to explore the lexical-semantic resources on apc. especially, wsd is combined with semantic and sentiment information to pose an improved performance in apc. in summary, we draw the following major conclusions. firstly, using a dictionary (e.g., wordnet, wiktionaryen) to remove noise-features often works well in most datasets. secondly, applying wsd alone, in general, does not work in apc, especially on not-well-written ugc data. however, our proposed selective.wsd works better than a basic wsd. thirdly, applying wsd combining with semantic and/or sentiment features improve the performance for specific personal traits (i.e., cneu, cext). moreover, no personality specific resources are required in our method.