scholarly journals SIMPA: Statement-to-Item Matching Personality Assessment from text

2022 ◽  
Author(s):  
Matej Gjurković ◽  
Iva Vukojević ◽  
Jan Šnajder

Automated text-based personality assessment (ATBPA) methods can analyze large amounts of text data and identify nuanced linguistic personality cues. However, current approaches lack the interpretability, explainability, and validity offered by standard questionnaire instruments. To address these weaknesses, we propose an approach that combines questionnaire-based and text-based approaches to personality assessment. Our Statement-to-Item Matching Personality Assessment (SIMPA) framework uses natural language processing methods to detect self-referencing descriptions of personality in a target’s text and utilizes these descriptions for personality assessment. The core of the framework is the notion of a trait-constrained semantic similarity between the target’s freely expressed statements and questionnaire items. The conceptual basis is provided by the realistic accuracy model (RAM), which describes the process of accurate personality judgments and which we extend with a feedback loop mechanism to improve the accuracy of judgments. We present a simple proof-of-concept implementation of SIMPA for ATBPA on the social media site Reddit. We show how the framework can be used directly for unsupervised estimation of a target’s Big 5 scores and indirectly to produce features for a supervised ATBPA model, demonstrating state-of-the-art results for the personality prediction task on Reddit.

Data ◽  
2021 ◽  
Vol 6 (5) ◽  
pp. 52
Author(s):  
Maria Nefeli Nikiforos ◽  
Yorghos Voutos ◽  
Anthi Drougani ◽  
Phivos Mylonas ◽  
Katia Lida Kermanidis

Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.


The social and collaborative nature of requirements elicitation process bases its core dependency on aptitude, attitudes, and personality characteristics of its participants. The participant’s personality characteristics are directly related with their personality traits, which can be categorized using different personality assessment models. The MBTI personality assessment model has been used successfully for the assessment of personality of software engineers since last few decades. In this article, the personality traits for requirements elicitation teams have been predicted using MBTI personality assessment model, on the basis of their industry demanded job descriptions/tasks and major soft skills. The article presents a complete personality prediction process using a systematic approach based on major soft skills mapping with job descriptions, personality attributes and personality traits. The obtained results show that extraversion and feeling personality traits are the most suitable personality traits for requirements analysts/engineers who are assigned the task of requirements elicitation. The obtained results are very much aligned with the already published scholar’s work for software engineer’s personality assessment and development team composition.


Author(s):  
Quan Li

Since the invention of Word2Vec by a Google team in 2013, natural language processing (NLP) techniques have been increasingly applied in the private sector, by government agencies across countries, and in the social sciences. This chapter explains NLP’s basic analytical procedure from preprocessing of raw text data to statistical modeling, reviews the most recent advances in NLP applications in political science, and proposes a new scaling approach for measuring political actors’ spatial preferences along with potential application in decision-making research. It argues that with a greater focus on explaining behavioral mechanisms and processes, which is a goal shared by artificial intelligence/computational modeling and cognitive science, NLP can help improve behavioral political science by its ability to integrate micro-, meso-, and macro-level analyses. Critical and reflexive use of NLP techniques, combined with big data, will lead to obtain better insights on political behavior in general.


10.29007/dlff ◽  
2019 ◽  
Author(s):  
Alena Fenogenova ◽  
Viktor Kazorin ◽  
Ilia Karpov ◽  
Tatyana Krylova

Automatic morphological analysis is one of the fundamental and significant tasks of NLP (Natural Language Processing). Due to special features of Internet texts, as they can be both normative texts (news, fiction, nonfiction) and less formal texts (such as blogs and texts from social networks), the morphological tagging has become non-trivial and an actual task. In this paper we describe our experiments in tagging of Internet texts presenting our approach based on deep learning. The new social media test set was created, that allows to compare our system with state-of-the-art open source analyzers on the social media texts material.


2022 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Hai-Yan Yao ◽  
Wang-Gen Wan ◽  
Xiang Li

Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, computer vision, and audio processing. We propose an end-to-end transformer network embedded with random deviation queries for pedestrian trajectory forecasting. The self-correcting scheme can enhance the robustness of the network. Moreover, we present a co-training strategy to improve the training effect. The whole scheme is trained collaboratively by the original loss and classification loss. Therefore, we also achieve more accurate prediction results. Experimental results on several datasets indicate the validity and robustness of the network. We achieve the best performance in individual forecasting and comparable results in social forecasting. Encouragingly, our approach achieves a new state of the art on the Hotel and Zara2 datasets compared with the social-based and individual-based approaches.


Author(s):  
K Sobha Rani

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.


2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.


Author(s):  
Eduardo Manzano Moreno

This chapter addresses a very simple question: is it possible to frame coinage in the Early Middle Ages? The answer will be certainly yes, but will also acknowledge that we lack considerable amounts of relevant data potentially available through state-of-the-art methodologies. One problem is, though, that many times we do not really know the relevant questions we can pose on coins; another is that we still have not figured out the social role of coinage in the aftermath of the Roman Empire. This chapter shows a number of things that could only be known thanks to the analysis of coins. And as its title suggests it will also include some reflections on greed and generosity.


2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.


2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.


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