M. Nasiri; H. Rahmani
Abstract
Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, ...
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Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, Psychologists discover these dimensions through personality questionnaires. As an automatic way, varied personal input types (textual/image/video) of people are gathered and analyzed for this purpose. In this paper, we proposed a method called DENOVA (DEep learning based on the ANOVA), which predicts FFM using deep learning based on the Analysis of variance (ANOVA) of words. For this purpose, DENOVA first applies ANOVA to select the most informative terms. Then, DENOVA employs Word2Vec to extract document embeddings. Finally, DENOVA uses Support Vector Machine (SVM), Logistic Regression, XGBoost, and Multilayer perceptron (MLP) as classifiers to predict FFM. The experimental results show that DENOVA outperforms on average, 6.91%, the state-of-the-art methods in predicting FFM with respect to accuracy.
Document and Text Processing
S. Momtazi; A. Rahbar; D. Salami; I. Khanijazani
Abstract
Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, ...
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Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use semantic models for document vector representations. Latent Dirichlet allocation (LDA) topic modeling and doc2vec neural document embedding are two well-known techniques for this purpose. In this paper, we first study the conceptual difference between the two models and show that they have different behavior and capture semantic features of texts from different perspectives. We then proposed a hybrid approach for document vector representation to benefit from the advantages of both models. The experimental results on 20newsgroup show the superiority of the proposed model compared to each of the baselines on both text clustering and classification tasks. We achieved 2.6% improvement in F-measure for text clustering and 2.1% improvement in F-measure in text classification compared to the best baseline model.
A.1. General
A. Zarei; M. Maleki; D. Feiz; M. A. Siahsarani kojuri
Abstract
Competitive intelligence (CI) has become one of the major subjects for researchers in recent years. The present research is aimed to achieve a part of the CI by investigating the scientific articles on this field through text mining in three interrelated steps. In the first step, a total of 1143 articles ...
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Competitive intelligence (CI) has become one of the major subjects for researchers in recent years. The present research is aimed to achieve a part of the CI by investigating the scientific articles on this field through text mining in three interrelated steps. In the first step, a total of 1143 articles released between 1987 and 2016 were selected by searching the phrase "competitive intelligence" in the valid databases and search engines; then, through reviewing the topic, abstract, and main text of the articles as well as screening the articles in several steps, the authors eventually selected 135 relevant articles in order to perform the text mining process. In the second step, pre-processing of the data was carried out. In the third step, using non-hierarchical cluster analysis (k-means), 5 optimum clusters were obtained based on the Davies–Bouldin index, for each of which a word cloud was drawn; then, the association rules of each cluster was extracted and analyzed using the indices of support, confidence, and lift. The results indicated the increased interest in researches on CI in recent years and tangibility of the strong and weak presence of the developed and developing countries in formation of the scientific products; further, the results showed that information, marketing, and strategy are the main elements of the CI that, along with other prerequisites, can lead to the CI and, consequently, the economic development, competitive advantage, and sustainability in market.
