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Copyright of Journal of Information Science & Technology Association/Joho no Kagaku to Gijutsu is the property of Information Science & Technology Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper reports an innovative Chinese register study based on regression analysis for sentence length distribution and text clustering. Although end of sentence is not conventionally marked in Chinese, we resolve this issue by assuming that segments between periods, question marks, and exclamation marks are sentences, which can be further divided into simple sentences and compound sentences. We also assume that segments between punctuation marks that express pauses in utterances form sentences (i.e., clauses). Using regression analysis, we find that the frequency distribution of sentence and clause lengths in Chinese can be fitted by the formula F = aLbcL, where L is sentence/clause length. Texts from different registers give rise to different fitted values of the parameters, and hence can serve to differentiate these registers. Finally, we use these parameters to represent and cluster texts from different registers. The successful text clustering results further prove that the parameters of the fitted results are reliable linguistic characteristics for different registers. In terms of linguistic theories, our study shows that it is just as effective to model sentence length in Chinese using sociological words (i.e., characters) as it is using linguistic words. [ABSTRACT FROM AUTHOR]

Structural differences between scientific articles that arise from their translation from Russian into English are studied using the modal topic modeling technique. Each collected document is represented by two modes, that is, English and Russian. As a result of the topic modeling, the Φ and Θ bimodal matrices are obtained. Analysis of the Φ matrix showed that the topics were divided according to the degree of conformity between Russian and English terms when the words are considered in descending order of probability. For 90% of the topics, the English words fully match the Russian ones. Analysis of the Θ matrix showed that for 99% of the documents there is a subject with a value greater than 0.95. Thus, most of the documents are monotopical, which does not depend on the document language. [ABSTRACT FROM AUTHOR]

This article introduces a different method for text representation in order to perform clustering over different articles which, arguably, has no subjective information with similar topic-sentiment use of language. Using the joint sentiment/topic model, the text is vectorized in a low dimensional space. These vectors were then used as distance measurement for clustering texts. While comparing this unusual method with a traditional bag ofwords representation an improvement in the performance of the algorithms was observed. The authors think this method of representation might have implications for future studies of the computational interpretation of texts. [ABSTRACT FROM AUTHOR]

Text mining refers to the process of extracting the high-quality information from the text. It is broadly used in applications, like text clustering, text categorization, text classification, etc. Recently, the text clustering becomes the facilitating and challenging task used to group the text document. Due to some irrelevant terms and large dimension, the accuracy of text clustering is reduced. In this paper, the semantic word processing and novel Particle Grey Wolf Optimizer (PGWO) is proposed for automatic text clustering. Initially, the text documents are given as input to the pre-processing step which caters the useful keyword for feature extraction and clustering. Then, the resultant keyword is applied to wordnet ontology to find out the synonyms and hyponyms of every keyword. Subsequently, the frequency is determined for every keyword which is used to build the text feature library. Since the text feature library contains the larger dimension, the entropy is utilized to select the most significant feature. Finally, the new algorithm Particle Grey Wolf Optimizer (PGWO) is developed by integrating the particle swarm optimization (PSO) into the grey wolf optimizer (GWO). Thus, the proposed algorithm is used to assign the class labels to generate the different clusters of text documents. The simulation is performed to analyze the performance of the proposed algorithm, and the proposed algorithm is compared with existing algorithms. The proposed method attains the clustering accuracy of 80.36% for 20 Newsgroup dataset and the clustering accuracy of 79.63% for Reuter which ensures the better automatic text clustering. [ABSTRACT FROM AUTHOR]

EDCM, the Exponential-family approximation to the Dirichlet Compound Multinomial (DCM), proposed by Elkan (2006), is an efficient statistical model for high-dimensional and sparse count data. EDCM models take into account the burstiness phenomenon correctly while being many times faster than DCM. This work proposes the use of Minimum Message Length (MML) criterion for determining the number of components that best describes the data with a finite EDCM mixture model. Parameters estimation is based on the previously proposed Deterministic Annealing Expectation-Maximization (DAEM) approach. The validation of the proposed unsupervised algorithm involves different real applications: text document modeling, topic novelty detection and hierarchical image clustering. A comparison with results obtained for other information-theory based selection criteria is provided. [ABSTRACT FROM AUTHOR]

This article introduces a different method for text representation in order to perform clustering over different articles which, arguably, has no subjective information with similar topic-sentiment use of language. Using the joint sentiment/topic model, the text is vectorized in a low dimensional space. These vectors were then used as distance measurement for clustering texts. While comparing this unusual method with a traditional bag ofwords representation an improvement in the performance of the algorithms was observed. The authors think this method of representation might have implications for future studies of the computational interpretation of texts. [ABSTRACT FROM AUTHOR]

The "Superhost" badge is that Airbnb entitles the host provides good services. This study verifies Airbnb's "Superhost" mechanism by applying text mining technologies, combined with Long Short-Term Memory (LSTM) and K-Means, to the entire dataset of tourists' online reviews of Hangzhou city, China. Six kinds of hosts' good services are identified, including "Three Meals or Night Snacks," "Fruits, Drinks or Snacks," "Travel Guides," "Free Shuttle or Helping with Luggage," "Chats," and "Replies or Communications." The study reveals the minority of "Superhosts" are mentioned of providing the majority of six kinds of good services, which means "Superhosts" do deserve the badge. [ABSTRACT FROM AUTHOR]

Policy text contains large amount of diversified data and strictly conforms to standards and specifications, but the traditional text clustering method cannot solve the problems of high dimensionality, sparse features, and similar meanings, so this paper proposes a weighted algorithm based on the LDA-Gibbs model to improve the accuracy of policy text clustering. Firstly, it provides realistic basis for the assumptions of the LDA-Gibbs topic model and the weighted algorithm; secondly, it pre-processes the existing policy text simulated data, establishes the LDA-Gibbs model, forms a weighted algorithm, and generates training data to determine the number of optimal topics in the LDAGibbs model and completes the final clustering of the policy text; finally, by summarizing, classifying and deducing the conclusions of the experimental data, this paper proves the objective validity and effects of this method. Hopefully the overall design of this method can be applied in the prospective study on the formulation of new policies in the future, the retrospective evaluation and testing of the existing policies and the formation of a two-way interactive mechanism. [ABSTRACT FROM AUTHOR]

Stemming is one of the stages performed on the process of extracting information from the text. Stemming is a process of converting words into their roots. There is an indication that the most accurate stemmer algorithm is not the only way to achieve the best performance in information retrieval (IR). In this study, seven Indonesian stemmer algorithms and an English stemmer algorithm are compared, they are Nazief, Arifin, Fadillah, Asian, Enhanched confix stripping (ECS), Arifiyanti and Porter. The data used are 2,734 tweets collected from the official twitter account of PLN. First, the aims are to analyze the correlation between stemmer accuracy and information retrieval performance in Indonesian text language. Second, is to identify the best algorithm for Indonesian text processing purpose. This research also proposed improved algorithm for stemming Indonesian text. The result shows that correlation found in the previous research does not occur for the Indonesian language. The result also shows that the proposed algorithm was the best for Indonesian text processing purpose with weighted scoring value of 0.648. [ABSTRACT FROM AUTHOR]

Abstract Clustering is the method employed to group Web pages containing related information into clusters, which facilitates the allocation of relevant information. Clustering performance is mostly dependent on the text features' characteristics. The Arabic language has a complex morphology and is highly inflected. Thus, selecting appropriate features affects clustering performance positively. Many studies have addressed the clustering problem in Web pages with Arabic content. There are three main challenges in applying text clustering to Arabic Web page content. The first challenge concerns difficulty with identifying significant term features to represent original content by considering the hidden knowledge. The second challenge is related to reducing data dimensionality without losing essential information. The third challenge regards how to design a suitable model for clustering Arabic text that is capable of improving clustering performance. This paper presents an overview of existing Arabic Web page clustering methods, with the goals of clarifying existing problems and examining feature selection and reduction techniques for solving clustering difficulties. In line with the objectives and scope of this study, the present research is a joint effort to improve feature selection and vectorization frameworks in order to enhance current text analysis techniques that can be applied to Arabic Web pages. [ABSTRACT FROM AUTHOR]

This article proposes the modified AHC (Agglomerative Hierarchical Clustering) algorithm which considers the feature similarity and is applied to the text clustering. The words which are given as features for encoding texts into numerical vectors are semantic related entities, rather than independent ones, and the synergy effect between the word clustering and the text clustering is expected by combining both of them with each other. In this research, we define the similarity metric between numerical vectors considering the feature similarity, and modify the AHC algorithm by adopting the proposed similarity metric as the approach to the text clustering. The proposed AHC algorithm is empirically validated as the better approach in clustering texts in news articles and opinions. The significance of this research is to improve the clustering performance by utilizing the feature similarities. [ABSTRACT FROM AUTHOR]

The paper is dedicated to solving the problem of optimal text classification in the area of automated detection of typology of texts. In conventional approaches to topicality-based text classification (including topic modeling), the number of clusters is to be set up by the scholar, and the optimal number of clusters, as well as the quality of the model that designates proximity of texts to each other, remain unresolved questions. We propose a novel approach to the automated definition of the optimal number of clusters that also incorporates an assessment of word proximity of texts, combined with text encoding model that is based on the system of sentence embeddings. Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerative hierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering. The preferred number of clusters is determined based on the “e-2” hypothesis. We set up an experiment on two datasets of real-world labeled data: News20 and BBC. The proposed model is tested against more traditional text representation methods, like bag-of-words and word2vec, to show that it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models with different encoding methods. We use three quality metrics to demonstrate that clustering quality does not drop when the number of clusters grows. Thus, we get close to the convergence of text clustering and text classification.

Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information that could be used to test the treatment quality for improving medical-related decisions. One of the significant techniques in literature for discharge summaries classification is feature extraction techniques from the domain of natural language processing on text data. We propose a novel sentiment analysis method for discharge summaries classification that relies on vector space models, statistical methods, association rule, and extreme learning machine autoencoder (ELM-AE). Our novel hybrid model is based on statistical methods that build the lexicon in a domain related to health and medical records. Meanwhile, our method examines treatment quality based on an idea inspired by sentiment analysis. Experiments prove that our proposed method obtains a higher F1 value of 0.89 with good TPR (True Positive Rate) and FPR (False Positive Rate) values compared with various well-known state-of-the-art methods with different size of training and testing datasets. The results also prove that our method provides a flexible and effective technique to examine treatment quality based on positive, negative, and neutral terms for sentence-level in each discharge summary.

Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods.

In product development, the foremost issue is to identify what the customers' expectations would be from the product. As a promising approach to the product development, Quality Function Deployment also gives crucial importance to the collection and analysis of Voice of the Customer (VoC) to deduce true customer needs. Data sources of VoC include surveys, interviews, focus groups, gemba visits as well as customer reviews which can be collected through call centers, internet homepages, blogs, and microblogs in social networks. Customers' verbatim or reviews obtained from these resources require more detailed extraction to define them as the positive restatement of problems, opportunities or image issues independent of the product or the solution. Basically, this clarification process is a content analysis in which the developers usually seek to extract and classify the spoken-unspoken customer needs from VoC. This labor-intensive manual approach brings subjectivity to the analysis and can take so much time in the case of having condensed and large-volume text data. During the past decade, the field of text mining has enabled to solve these kinds of problems efficiently by unlocking hidden information and developing new knowledge; exploring new horizons; and improving the research process and quality. This paper utilizes a particular algorithm of text clustering, a recently popular field of interest in text mining, to analyze VoC and shows how text mining can also support the clarification process for better extraction of customer needs. Practical implications are presented through analysis of online customer reviews for a product.

To effectively manage the great amount of data on Arabic web pages and to enable the classification of relevant information are very important research problems. Studies on sentiment text mining have been very limited in the Arabic language because they need to involve deep semantic processing. Therefore, in this paper, we aim to retrieve machine-understandable data with the help of a Web content mining technique to detect covert knowledge within these data. We propose an approach to achieve clustering with semantic similarities. This approach comprises integrating k-means document clustering with semantic feature extraction and document vectorization to group Arabic web pages according to semantic similarities and then show the semantic annotation. The document vectorization helps to transform text documents into a semantic class probability distribution or semantic class density. To reach semantic similarities, the approach extracts the semantic class features and integrates them into the similarity weighting schema. The quality of the clustering result has evaluated the use of the purity and the mean intra-cluster distance (MICD) evaluation measures. We have evaluated the proposed approach on a set of common Arabic news web pages. We have acquired favorable clustering results that are effective in minimizing the MICD, expanding the purity and lowering the runtime.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Íkala: Revista de Lenguaje y Cultura is the property of Universidad de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)