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

Authors: González, José Ángel | Hurtado, Lluís-F. | Pla, Ferran

Article Type: Research Article

Abstract: This paper describes our proposal for Sentiment Analysis in Twitter for the Spanish language. The main characteristics of the system are the use of word embedding specifically trained from tweets in Spanish and the use of self-attention mechanisms that allow to consider sequences without using convolutional nor recurrent layers. These self-attention mechanisms are based on the encoders of the Transformer model. The results obtained on the Task 1 of the TASS 2019 workshop, for all the Spanish variants proposed, support the correctness and adequacy of our proposal.

Keywords: Twitter, sentiment analysis, transformer encoders

DOI: 10.3233/JIFS-179881

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2165-2175, 2020

Authors: Chen, Dengbo | Rong, Wenge | Zhang, Jianfei | Xiong, Zhang

Article Type: Research Article

Abstract: This paper proposes a sentiment analysis framework based on ranking learning. The framework utilizes BERT model pre-trained on large-scale corpora to extract text features and has two sub-networks for different sentiment analysis tasks. The first sub-network of the framework consists of multiple fully connected layers and intermediate rectified linear units. The main purpose of this sub-network is to learn the presence or absence of various emotions using the extracted text information, and the supervision signal comes from the cross entropy loss function. The other sub-network is a ListNet. Its main purpose is to learn a distribution that approximates the real …distribution of different emotions using the correlation between them. Afterwards the predicted distribution can be used to sort the importance of emotions. The two sub-networks of the framework are trained together and can contribute to each other to avoid the deviation from a single network. The framework proposed in this paper has been tested on multiple datasets and the results have shown the proposed framework’s potential. Show more

Keywords: Sentiment analysis, multi-label classification, ranking

DOI: 10.3233/JIFS-179882

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2177-2188, 2020

Authors: Calvo, Hiram | Gutiérrez-Hinojosa, Sandra J. | Rocha-Ramírez, Arturo P. | Moreno-Armendáriz, Marco A.

Article Type: Research Article

Abstract: In this work we experiment with the hypothesis that words subjects use can be used to predict their psychological attachment style (secure, fearful, dismissing, preoccupied) as defined by Bartholomew and Horowitz. In order to verify this hypothesis, we collected a series of autobiographic texts written by a set of 202 participants. Additionally, a psychological instrument (Frías questionnaire) was applied to these same participants to measure their attachment style. We identified characteristic patterns for each style of attachment by means of two approaches: (1) mapping words into a word space model composed of unigrams, bigrams and/or trigrams on which different classifiers …were trained (Naïve Bayes (NB), Bernoulli NB, Multinomial NB, Multilayer Perceptrons); and (2) using a word-embedding based representation and a neural network architecture based on different units (LSTM, Gated Recurrent Units (GRU) and Bilateral GRUs). We obtained the best accuracy of 0.4079 for the first approach by using a Boolean Multinomial NB on unigrams, bigrams and trigrams altogether, and an accuracy of 0.4031 for the second approach using Bilateral GRUs. Show more

Keywords: Psychological attachment, autobiography, text classification, bilateral gated recurrent units, anxiety-avoidance attachment model

DOI: 10.3233/JIFS-179883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2189-2199, 2020

Authors: Piryani, Rajesh | Piryani, Bhawna | Singh, Vivek Kumar | Pinto, David

Article Type: Research Article

Abstract: In recent times, sentiment analysis research has achieved tremendous impetus on English textual data, however, a very less amount of research has been focused on Nepali textual data. This work is focused towards Nepali textual data. We have explored machine learning approaches and proposed a lexicon-based approach using linguistic features and lexical resources to perform sentiment analysis for tweets written in Nepali language. This lexicon-based approach, first pre-process the tweet, locate the opinion-oriented features and then compute the sentiment polarity of tweet. We have investigated both conventional machine learning models (Multinomial Naïve Bayes (NB), Decision Tree, Support Vector Machine (SVM) …and logistic regression) and deep learning models (Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM) for sentiment analysis of Nepali text. These machine learning models and lexicon-based approach have been evaluated on tweet dataset related to Nepal Earthquake 2015 and Nepal blockade 2015. Lexicon based approach has outperformed than conventional machine learning models. Deep learning models have outperformed than conventional machine learning models and lexicon-based approach. We have also created Nepali SentiWordNet and Nepali SenticNet sentiment lexicon from existing English language resources as by-product. Show more

Keywords: Lexicon-based sentiment analysis, Nepali language, Twitter sentiment analysis, Nepali SentiWordNet, Nepali SenticNet, deep learning, sentiment analysis

DOI: 10.3233/JIFS-179884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2201-2212, 2020

Authors: Sreeja, P. S. | Mahalakshmi, G. S.

Article Type: Research Article

Abstract: Poem is a spontaneous flow of emotions. There are several emotion detection systems to identify emotions from speech, gestures, and text (blogs, newspapers, stories and medical reports). Since such systems do not exist for poetry, we take the first step in building a system to recognize emotions in poetry by constructing a benchmark corpus, the PERC (P oem E motion R ecognition C orpus), of poems written by Indian poets in English. In this research a novel graphical method, Poem Emotion Trajectory System (PETS), is proposed to depict the flow of emotion in a poem. PETS is based on the …construction of a weighted directed graph as a means to represent the emotion flow among the verses of a given poem. The weights represent the transition probability among the emotion states considered. The significant advantage is that a dominant path for each emotion category is identified. Emotion flow along verses is analyzed using a graph-based approach. This method, applied to each emotion category, generalizes the emotion flow in each emotion class. This PETS can be applied in poetry therapy and to enhance creative thinking and writing. Show more

Keywords: Poem emotion recognition corpus, emotion recognition, emotion analysis, poem emotion trajectory system, poem emotion trajectory graph, dominant emotion flow trajectory, natural language processing, artificial intelligence

DOI: 10.3233/JIFS-179885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2213-2227, 2020

Authors: Ivanov, Vladimir | Solovyev, Valery

Article Type: Research Article

Abstract: Creation of dictionaries of abstract and concrete words is a well-known task. Such dictionaries are important in several applications of text analysis and computational linguistics. Usually, the process of assembling of concreteness scores for words begins with a lot of manual work. However, the process can be automated significantly using information from large corpora. In this paper we combine two datasets: a dictionary with concreteness scores of 40,000 English words and the GoogleBooks Ngram dataset, in order to test the following hypothesis: in text concrete words tend to occur with more concrete words, than with abstract words (and inverse: abstract …words tend to occur with more abstract words, than with concrete words). Using the hypothesis, we proposed a method for automatic evaluation concreteness scores of words using a small amount of initial markup. Show more

Keywords: Concreteness of words, bigrams, dictionary

DOI: 10.3233/JIFS-179886

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2229-2237, 2020

Authors: Rosso-Mateus, Andrés | Montes-y-Gómez, Manuel | Rosso, Paolo | González, Fabio A.

Article Type: Research Article

Abstract: Passage retrieval is an important stage of question answering systems. Closed domain passage retrieval, e.g. biomedical passage retrieval presents additional challenges such as specialized terminology, more complex and elaborated queries, scarcity in the amount of available data, among others. However, closed domains also offer some advantages such as the availability of specialized structured information sources, e.g. ontologies and thesauri, that could be used to improve retrieval performance. This paper presents a novel approach for biomedical passage retrieval which is able to combine different information sources using a similarity matrix fusion strategy based on convolutional neural network architecture. The method was …evaluated over the standard BioASQ dataset, a dataset specialized on biomedical question answering. The results show that the method is an effective strategy for biomedical passage retrieval able to outperform other state-of-the-art methods in this domain. Show more

Keywords: Biomedical passage retrieval, neural networks, question answering, deep learning

DOI: 10.3233/JIFS-179887

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2239-2248, 2020

Authors: Hernández-Illera, Antonio | Martínez-Prieto, Miguel A. | Fernández, Javier D. | Fariña, Antonio

Article Type: Research Article

Abstract: RDF self-indexes compress the RDF collection and provide efficient access to the data without a previous decompression (via the so-called SPARQL triple patterns). HDT is one of the reference solutions in this scenario, with several applications to lower the barrier of both publication and consumption of Big Semantic Data. However, the simple design of HDT takes a compromise position between compression effectiveness and retrieval speed. In particular, it supports scan and subject-based queries, but it requires additional indexes to resolve predicate and object-based SPARQL triple patterns. A recent variant, HDT++ , improves HDT compression ratios, but it does not retain …the original HDT retrieval capabilities. In this article, we extend HDT++ with additional indexes to support full SPARQL triple pattern resolution with a lower memory footprint than the original indexed HDT (called HDT-FoQ). Our evaluation shows that the resultant structure, iHDT++ , requires 70 - 85% of the original HDT-FoQ space (and up to 48 - 72% for an HDT Community variant). In addition, iHDT++ shows significant performance improvements (up to one level of magnitude) for most triple pattern queries, being competitive with state-of-the-art RDF self-indexes. Show more

Keywords: HDT, RDF compression, triple pattern resolution, SPARQL, linked data

DOI: 10.3233/JIFS-179888

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2249-2261, 2020

Authors: Huetle-Figueroa, Juan | Perez-Tellez, Fernando | Pinto, David

Article Type: Research Article

Abstract: Currently, the semantic analysis is used by different fields, such as information retrieval, the biomedical domain, and natural language processing. The primary focus of this research work is on using semantic methods, the cosine similarity algorithm, and fuzzy logic to improve the matching of documents. The algorithms were applied to plain texts in this case CVs (resumes) and job descriptions. Synsets of WordNet were used to enrich the semantic similarity methods such as the Wu-Palmer Similarity (WUP), Leacock-Chodorow similarity (LCH), and path similarity (hypernym/hyponym). Additionally, keyword extraction was used to create a postings list where keywords were weighted. The task …of recruiting new personnel in the companies that publish job descriptions and reciprocally finding a company when workers publish their resumes is discussed in this research work. The creation of a new gold standard was required to achieve a comparison of the proposed methods. A web application was designed to match the documents manually, creating the new gold standard. Thereby the new gold standard confirming benefits of enriching the cosine algorithm semantically. Finally, the results were compared with the new gold standard to check the efficiency of the new methods proposed. The measures used for the analysis were precision, recall, and f-measure, concluding that the cosine similarity weighted semantically can be used to get better similarity scores. Show more

Keywords: Semantic similarity, semantic matching, document similarity, cosine enrichment, keyword enrichment

DOI: 10.3233/JIFS-179889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2263-2278, 2020

Authors: Morales, Valentin | Gomez, Juan Carlos | Van Amerongen, Saskia

Article Type: Research Article

Abstract: Email is one of the most popular ways of communication. Nevertheless, it is also a potential tool to deceive and fill users with unwanted publicity, which reduces productivity. To alleviate such fact, a common solution has been building machine learning models based on the content of emails to automatically separate emails (spam vs ham). In this work, a study of a set of machine learning models and content-based features for the problem of cross-dataset email classification is presented. This problem consists in training and testing the models using different datasets; considering the fact that the datasets were collected under different …independent setups. This has the purpose of simulating future variable or unpredictable conditions in the emails content distributions as could happen in a real setting, where models are trained using emails from a certain period of time, group of users or accounts, but tested with emails from other users or accounts. Experiments were conducted with the models and features using different datasets and two setups, same-dataset, and cross-dataset, to show the complexity of the later. The performance was evaluated using the Area Under the ROC Curve, a common metric in email classification. The results show interesting insights for the problem. Show more

Keywords: Email classification, data mining, machine learning, cross-dataset classification

DOI: 10.3233/JIFS-179890

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2279-2290, 2020