A sentiment-aware deep learning approach for personality detection from text

Ren, Zhancheng; Shen, Qiang; Diao, Xiaolei; Xu, Hao

doi:10.1016/j.ipm.2021.102532

Cited by 101 publications

(48 citation statements)

References 16 publications

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“…This ability is a consequence of embedding methods' adroitness in meaning acquisition and representation. In a study which has set out to detect personality based on text content analysis, Ren et al [14] have investigated a novel multi-label personality prediction learning model which combines emotional and semantic features. In particular, they have leveraged a Bidirectional Encoder Representation from Transformers (BERT), to generate sentence-level embeddings for extracting semantic features from text, as well as a sentiment dictionary for the sake of text sentiment analysis purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

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Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Ramezani,

Feizi-Derakhshi,

Balafar

2022

Preprint

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How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short * m

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Section: Literature Reviewmentioning

confidence: 99%

“…In the last few years, we have witnessed a considerable rise in text-based APP, that have used embedding methods to transfer the text elements to a more meaningful space (rather than character space), in favor of a better exploitation of computational methods [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Ramezani,

Feizi-Derakhshi,

Balafar

2022

Preprint

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“…There are many other future research directions. The effect of hyperparameter selection on different attributes [77] than the ones here and different ML models other than NMF should also be explored by future work in the context of this domain. In conjunction, the exploration of metrics other than accuracy could be explored, such as diversity, novelty, fairness, or impact.…”

Section: H Strengths Limitations and Future Workmentioning

confidence: 99%

The Effect of Hyperparameter Selection on the Personification of Customer Population Data

Jansen

Jung

Salminen

2021

IJECER

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We explore the effects of hyperparameter selections on the personification accuracy of customer analytics data from a corporate YouTube channel with an audience in the hundreds of thousands and customer interactions in the tens of millions. Using non-negative matrix factorization, we generate personas sets from 5 to 15 using the customer analytics data, with the number of personas being the changing hyperparameter. We then compare the gender, age, nationality, and topical interests of the personas across each of the 11 persona sets using the average of the 110 generated personas as the baseline. This analysis shows that hyperparameter selection significantly alters the personification of the analytics data, with the effect most apparent with age representation. The set of 10 personas provides one of the most accurate representations across all attributes, indicating that this may be a good default hyperparameter for personification. Future research can explore other personification attributes with other customer analytics datasets.

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“…One of the recent APP methods combined semantic and emotional features in order to determine personality trait from multi-text [69]. On the semantic side, BERT is deployed to vectorize texts and using a self-attention mechanism, sentence-level representation is generated.…”

Section: Plm-based Appsmentioning

confidence: 99%

Text-based automatic personality prediction: A bibliographic review

Feizi-Derakhshi,

Ramezani

et al. 2021

Preprint

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Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three; pretrained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets.

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A sentiment-aware deep learning approach for personality detection from text

Cited by 101 publications

References 16 publications

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

The Effect of Hyperparameter Selection on the Personification of Customer Population Data

Text-based automatic personality prediction: A bibliographic review

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