Journal of Intelligent & Fuzzy Systems - Volume 39, issue 2

Authors: Calvo, Hiram | Figueroa-Nazuno, Jesús | Mandujano, Ángel

Article Type: Research Article

Abstract: Natural Ontologies are presented in this work as a useful tool to model the way in which concepts are organized inside the human mind. In order to be compared, ontologies are represented as matrices and an elastic matching technique is used. For this purpose, a distance measure called Modern Fréchet is proposed, which is an approximation to the NP-Complete problem of elastic matching between matrices. An applied case of study is presented in which human knowledge is compared among different groups of people in the Computer Science domain.

Keywords: Natural ontologies, modern fréchet, ontology elicitation, elastic matching, dynamic time warping

DOI: 10.3233/JIFS-179891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2291-2303, 2020

Authors: Vázquez González, Stephanie | Somodevilla García, María

Article Type: Research Article

Abstract: This work presents a method for data gathering to construct a corpus related to speech disorders in children; such corpus will serve as the base to generate some semi-automatic ontologies, in order to become a computational model to support therapists for diagnosis and possible treatment. Speech disorders, phonemes and some additional information are classified using taxonomies obtained from speech disorders specialized literature. Based on the obtained taxonomies, the ontologies, which structure and formalize concepts defined by the main topic authors, are developed. The ontologies are constructed following some parts of classic methodologies and their subsequent validation is made through competency …questions. The development of the model is based on Natural Language Processing (NLP) and Information Retrieval (IR) techniques. Integration of the ontologies is made to be able to make a classification based in problematic phonemes; this is suggested as a complement to the diagnostic tool in the model. Show more

Keywords: Corpus building, ontology, speech disorders, problematic phonemes

DOI: 10.3233/JIFS-179892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2305-2315, 2020

Authors: Gomez-Montalvo, Jorge | Lopez, Melchor | Curi, Fernando | Moo-Mena, Francisco | Menendez, Victor

Article Type: Research Article

Abstract: In this paper, we introduce a Platform for Non-Intrusive Assistance (named PIANI), as an assistance platform for elderly people able to do activities in outdoor environments without strict supervision. PIANI includes an ontology used to characterize outdoor activities of interest (activities to be observed). PIANI also defines a risk level of the activity that an elderly person is currently doing out of his home by comparing such activity to its characterization. In addition, the proposed platform uses the smartphone of the person in order to collect geographic and time information, which is used by PIANI to infer activity risk and …send alert notifications based on semantic knowledge base. An experimental test was developed as a proof of concept about the utilization of PIANI to identify outdoors activities of elderly people, compute a level of risk and finally send non intrusive alert notification to the user. Show more

Keywords: Ambient assisted living, outdoor activity recognition, ontologies

DOI: 10.3233/JIFS-179893

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2317-2329, 2020

Authors: Abascal-Mena, Rocío | López-Ornelas, Erick

Article Type: Research Article

Abstract: In the context of digital social media, where users have multiple ways to obtain information, it is important to have tools to detect the authorship within a corpus supposedly created by a single author. With the tremendous amount of information coming from social networks there is a lot of research concerning author profiling, but there is a lack of research about the authorship identification. In order to detect the author of a group of tweets, a Naïve Bayes classifier is proposed which is an automatic algorithm based on Bayes’ theorem. The main objective is to determine if a particular tweet …was made by a specific user or not, based on its content. The data used correspond to a simple data set, obtained with the Twitter API, composed of four political accounts accompanied by their username and tweet identifier as it is mixed with multiple user tweets. To describe the performance of the classification model and interpret the obtained results, a confusion matrix is used as it contains values like accuracy, sensitivity, specificity, Kappa measure, the positive predictive and negative predictive value. These results show that the prediction model, after several cases of use, have acceptable values against the observed probabilities. Show more

Keywords: Naïve Bayes classifier, authorship detection, social network analysis, Twitter, confusion matrix

DOI: 10.3233/JIFS-179894

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2331-2339, 2020

Authors: Lai, Mirko | Patti, Viviana | Ruffo, Giancarlo | Rosso, Paolo

Article Type: Research Article

Abstract: Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users’ opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users’ opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role …of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance. Show more

Keywords: Stance detection, Twitter, brexit, NLP, community detection

DOI: 10.3233/JIFS-179895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2341-2352, 2020

Authors: Ashraf, Muhammad Adnan | Adeel Nawab, Rao Muhammad | Nie, Feiping

Article Type: Research Article

Abstract:  The aim of the author profiling task is to automatically predict various traits of an author (e.g. age, gender, etc.) from written text. The problem of author profiling has been mainly treated as a supervised text classification task. Initially, traditional machine learning algorithms were used by the researchers to address the problem of author profiling. However, in recent years, deep learning has emerged as a state-of-the-art method for a range of classification problems related to image, audio, video, and text. No previous study has carried out a detailed comparison of deep learning methods to identify which method(s) are most suitable …for same-genre and cross-genre author profiling. To fulfill this gap, the main aim of this study is to carry out an in-depth and detailed comparison of state-of-the-art deep learning methods, i.e. CNN, Bi-LSTM, GRU, and CRNN along with proposed ensemble methods, on four PAN Author Profiling corpora. PAN 2015 corpus, PAN 2017 corpus and PAN 2018 Author Profiling corpus were used for same-genre author profiling whereas PAN 2016 Author Profiling corpus was used for cross-genre author profiling. Our extensive experimentation showed that for same-genre author profiling, our proposed ensemble methods produced best results for gender identification task whereas CNN model performed best for age identification task. For cross-genre author profiling, the GRU model outperformed all other approaches for both age and gender. Show more

Keywords: Author profiling, deep learning, gender identification, ensemble methods, age identification, same-genre author profiling, cross-genre author profiling

DOI: 10.3233/JIFS-179896

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2353-2363, 2020

Authors: Rosas-Quezada, Érika S. | Ramírez-de-la-Rosa, Gabriela | Villatoro-Tello, Esaú

Article Type: Research Article

Abstract: Engaged customers are a very import part of current social media marketing. Public figures and brands have to be very careful about what they post online. That is why the need for accurate strategies for anticipating the impact of a post written for an online audience is critical to any public brand. Therefore, in this paper, we propose a method to predict the impact of a given post by accounting for the content, style, and behavioral attributes as well as metadata information. For validating our method we collected Facebook posts from 10 public pages, we performed experiments with almost 14000 …posts and found that the content and the behavioral attributes from posts provide relevant information to our prediction model. Show more

Keywords: Social media branding, impact analysis, data mining, features engineering, natural language processing

DOI: 10.3233/JIFS-179897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2365-2377, 2020

Authors: Ashraf, Muhammad Adnan | Nawab, Rao Muhammad Adeel | Nie, Feiping

Article Type: Research Article

Abstract: The task of author profiling aims to distinguish the author’s profile traits from a given content. It has got potential applications in marketing, forensic analysis, fake profile detection, etc. In recent years, the usage of bi-lingual text has raised due to the global reach of social media tools as people prefer to use language that expresses their true feelings during online conversations and assessments. It has likewise impacted the use of bi-lingual (English and Roman-Urdu) text in the sub-continent (Pakistan, India, and Bangladesh) over social media. To develop and evaluate methods for bi-lingual author profiling, benchmark corpora are needed. The …majority of previous efforts have focused on developing mono-lingual author profiling corpora for English and other languages. To fulfill this gap, this study aims to explore the problem of author profiling on bi-lingual data and presents a benchmark corpus of bi-lingual (English and Roman-Urdu) tweets. Our proposed corpus contains 339 author profiles and each profile is annotated with six different traits including age, gender, education level, province, language, and political party. As a secondary contribution, a range of deep learning methods, CNN, LSTM, Bi-LSTM, and GRU, are applied and compared on the three different bi-lingual corpora for age and gender identification, including our proposed corpus. Our extensive experimentation showed that the best results for both gender identification task (Accuracy = 0.882, F 1 -Measure = 0.839) and age identification (Accuracy = 0.735, F 1 -Measure = 0.739) are obtained using Bi-LSTM deep learning method. Our proposed bi-lingual tweets corpus is free and publicly available for research purposes. Show more

Keywords: Twitter, author profiling, roman-urdu, deep learning, bi-lingual, gender identification

DOI: 10.3233/JIFS-179898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2379-2389, 2020

Authors: Guzmán-Cabrera, Rafael

Article Type: Research Article

Abstract: In many areas of professional development, the categorization of textual objects is of critical importance. A prominent example is the attribution of authorship, where symbolic information is manipulated using natural language processing techniques. In this context, one of the main limitations is the necessity of a large number of pre-labeled instances for each author that is to be identified. This paper proposes a method based on the use of n-grams of characters and the use of the web to enrich the training sets. The proposed method considers the automatic extraction of the unlabeled examples from the Web and its iterative …integration into the training data set. The evaluation of the proposed approach was done by using a corpus formed by poems corresponding to 5 contemporary Mexican poets. The results presented allow evaluating the impact of the incorporation of new information into the training set, as well as the role played by the selection of classification attributes using information gain. Show more

Keywords: Authorship attribution, self-training, web corpora

DOI: 10.3233/JIFS-179899

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2391-2396, 2020

Authors: Neri-Mendoza, Verónica | Ledeneva, Yulia | García-Hernández, René Arnulfo

Article Type: Research Article

Abstract: The task of Extractive Multi-Document Text Summarization (EMDTS) aims at building a short summary with essential information from a collection of documents. In this paper, we propose an EMDTS method using a Genetic Algorithm (GA). The fitness function considering two unsupervised text features: sentence position and coverage. We propose the binary coding representation, selection, crossover, and mutation operators. We test the proposed method on the DUC01 and DUC02 data set, four different tasks (summary lengths 200 and 400 words), for each of the collections of documents (in total, 876 documents) are tested. Besides, we analyze the most frequently used methodologies …to summarization. Moreover, different heuristics such as topline, baseline, baseline-random, and lead baseline are calculated. In the results, the proposed method achieves to improve the state-of-art results. Show more

Keywords: Genetic algorithm, heuristics, unsupervised, extractive multi-document text, summarization

DOI: 10.3233/JIFS-179900

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2397-2408, 2020