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Journal articles on the topic 'Analysis of Text Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Khan, Nida Zafar, and Prof S. R. Yadav. "Analysis of Text Classification Algorithms: A Review." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (2019): 579–81. http://dx.doi.org/10.31142/ijtsrd21448.

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2

Sudheep Elayidom, M., Chinchu Jose, Anitta Puthussery, and Neenu K Sasi. "Text Classification for Authorship Attribution Analysis." Advanced Computing: An International Journal 4, no. 5 (2013): 1–10. http://dx.doi.org/10.5121/acij.2013.4501.

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3

Emery, Peter G. "Text Classification and Text Analysis in Advances Translation Teaching." Meta 36, no. 4 (2002): 567–77. http://dx.doi.org/10.7202/002707ar.

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Résumé On étudie d'abord les différentes bases théoriques sur lesquelles se fonde la classification des textes tout en préconisant comme critère prépondérant le domaine ou « contexte social ». On traite des méthodes d'analyse de textes en tenant compte de certaines théories linguistiques. Enfin, on souligne l'importance de l'analyse textuelle dans la pédagogie de la traduction. Les exemples illuslrafifs sont tirés de traductions arabe/anglais.

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4

Arora, Jhanvi, and Santosh Kumar Bharti. "Rhetorical Analysis and Classification of Poem Text." International Journal of Semiotics and Visual Rhetoric 5, no. 1 (2021): 57–71. http://dx.doi.org/10.4018/ijsvr.2021010105.

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Poetry is one of the richest forms of literature, which in itself includes all components of language a human learns; by components here, the context is towards the rhetorical devices. The rhetorical devices constitute the witty use of words used in the reference to things. The work intends to identify the forms of creative references used by the poets to contrast their style of writing and categorize the text on the basis of the same. On the basis of each such prominent device such as rhymes or alliteration, one can derive the boundary or similarity percentage amongst the poems, which can be

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Kim, Jiyun, and Han-joon Kim. "Multidimensional Text Warehousing for Automated Text Classification." Journal of Information Technology Research 11, no. 2 (2018): 168–83. http://dx.doi.org/10.4018/jitr.2018040110.

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This article describes how, in the era of big data, a data warehouse is an integrated multidimensional database that provides the basis for the decision making required to establish crucial business strategies. Efficient, effective analysis requires a data organization system that integrates and manages data of various dimensions. However, conventional data warehousing techniques do not consider the various data manipulation operations required for data-mining activities. With the current explosion of text data, much research has examined text (or document) repositories to support text mining

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Minaee, Shervin, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. "Deep Learning--based Text Classification." ACM Computing Surveys 54, no. 3 (2021): 1–40. http://dx.doi.org/10.1145/3439726.

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Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the pe

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Zuo, Wan Li, Zhi Yan Wang, Ning Ma, and Hong Liang. "Study on Consistency Analysis in Text Categorization." Applied Mechanics and Materials 539 (July 2014): 181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.539.181.

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Accurate classification of text is a basic premise of extracting various types of information on the Web efficiently and utilizing the network resources properly. In this paper, a brand new text classification method was proposed. Consistency analysis method is a type of iterative algorithm, which mainly trains different classifiers (weak classifier) by aiming at the same training set, and then these classifiers will be gathered for testing the consistency degrees of various classification methods for the same text, thus to manifest the knowledge of each type of classifier. It main determines

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BUSAGALA, L. S. P., W. OHYAMA, T. WAKABAYASHI, and F. KIMURA. "Improving Automatic Text Classification by Integrated Feature Analysis." IEICE Transactions on Information and Systems E91-D, no. 4 (2008): 1101–9. http://dx.doi.org/10.1093/ietisy/e91-d.4.1101.

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SzymańSki, Julian. "Comparative Analysis of Text Representation Methods Using Classification." Cybernetics and Systems 45, no. 2 (2014): 180–99. http://dx.doi.org/10.1080/01969722.2014.874828.

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AbuZeina, Dia, and Fawaz S. Al-Anzi. "Employing fisher discriminant analysis for Arabic text classification." Computers & Electrical Engineering 66 (February 2018): 474–86. http://dx.doi.org/10.1016/j.compeleceng.2017.11.002.

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Celardo, Livia, and Martin G. Everett. "Network text analysis: A two-way classification approach." International Journal of Information Management 51 (April 2020): 102009. http://dx.doi.org/10.1016/j.ijinfomgt.2019.09.005.

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12

Santucci, Valentino, Filippo Santarelli, Luciana Forti, and Stefania Spina. "Automatic Classification of Text Complexity." Applied Sciences 10, no. 20 (2020): 7285. http://dx.doi.org/10.3390/app10207285.

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This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimen

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13

P., Parvathi. "An Analysis of Short Text Detection and Classification Algorithms." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 176–82. http://dx.doi.org/10.22214/ijraset.2020.6026.

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Ansari, Asif, and Sreenarayanan NM. "Analysis of Text Classification of Dataset Using NB-Classifier." International Journal of Computer Science and Engineering 7, no. 6 (2020): 24–28. http://dx.doi.org/10.14445/23488387/ijcse-v7i6p107.

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Yıldırım, Savaş, and Tuğba Yıldız. "A comparative analysis of text classification for Turkish language." Pamukkale University Journal of Engineering Sciences 24, no. 5 (2018): 879–86. http://dx.doi.org/10.5505/pajes.2018.15931.

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Singh Tejavath, C., and T. Hirwarkar. "ANALYSIS OF DIFFERENT CLASSIFICATION ALGORITHMS FOR TEXT DATA MINING." Advances in Mathematics: Scientific Journal 9, no. 6 (2020): 3479–87. http://dx.doi.org/10.37418/amsj.9.6.27.

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17

Janani, R., and S. Vijayarani. "Text Classification A Comparative Analysis of Word Embedding Algorithms." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 818–22. http://dx.doi.org/10.26438/ijcse/v7i4.818822.

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Stein, Roger Alan, Patricia A. Jaques, and João Francisco Valiati. "An analysis of hierarchical text classification using word embeddings." Information Sciences 471 (January 2019): 216–32. http://dx.doi.org/10.1016/j.ins.2018.09.001.

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19

Long, Jun, Lu-da Wang, Zu-de Li, Zu-ping Zhang, and Liu Yang. "WordNet-based lexical semantic classification for text corpus analysis." Journal of Central South University 22, no. 5 (2015): 1833–40. http://dx.doi.org/10.1007/s11771-015-2702-8.

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20

Patel, Hemlata, and Dhanraj Verma. "Performance Analysis of Feature Selection Techniques for Text Classification." International Research Journal on Advanced Science Hub 2, Special Issue ICSTM 12S (2020): 44–50. http://dx.doi.org/10.47392/irjash.2020.259.

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21

Ce, Peng, and Bao Tie. "An Analysis Method for Interpretability of CNN Text Classification Model." Future Internet 12, no. 12 (2020): 228. http://dx.doi.org/10.3390/fi12120228.

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With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning. Among them, it is a very important and efficient way to classify text based on the convolutional neural network (CNN) model. Text data are a kind of sequence data, while time sequentiality of the general text data is relatively weak, so text classification is usually less relevant to the sequential structure of the full text. Therefore, CNN-based text classification has gradually become a research hotspot when deal

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22

Chincholkar, Bhushan R. "Implementation Analysis of Data Classification Approach for Sentiment Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1509–12. http://dx.doi.org/10.22214/ijraset.2021.36613.

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Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has bee n a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for publ

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23

Alsmadi, Issa, and Keng Hoon Gan. "Review of short-text classification." International Journal of Web Information Systems 15, no. 2 (2019): 155–82. http://dx.doi.org/10.1108/ijwis-12-2017-0083.

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PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related

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Et. al., Vanitha kakollu,. "Predictive Analysis using Convolution Network on Sentiment Analysis of Text Classification using Machine Learning." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 318–22. http://dx.doi.org/10.17762/itii.v9i2.349.

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Today we have large amounts of textual data to be processed and the procedure involved in classifying text is called natural language processing. The basic goal is to identify whether the text is positive or negative. This process is also called as opinion mining. In this paper, we consider three different data sets and perform sentiment analysis to find the test accuracy. We have three different cases- 1. If the text contains more positive data than negative data then the overall result leans towards positive. 2. If the text contains more negative data than positive data then the overall resu

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Et. al., Vanitha kakollu,. "Predictive Analysis using Convolution Network on Sentiment Analysis of Text Classification using Machine Learning." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 313–17. http://dx.doi.org/10.17762/itii.v9i2.348.

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Today we have large amounts of textual data to be processed and the procedure involved in classifying text is called natural language processing. The basic goal is to identify whether the text is positive or negative. This process is also called as opinion mining. In this paper, we consider three different data sets and perform sentiment analysis to find the test accuracy. We have three different cases- 1. If the text contains more positive data than negative data then the overall result leans towards positive. 2. If the text contains more negative data than positive data then the overall resu

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26

Gao, Ming Ze, Fang Fang Li, Zhe Yuan Ding, and Wei Dong Xiao. "Coupling Sentiment Dictionary and SVM Classification for Text Orientation Analysis." Advanced Materials Research 989-994 (July 2014): 2444–49. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2444.

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Sentiment classification finds various applications in opinion mining, which can help users determine sentiment tendency of texts and information. In this paper, we consider the problem of text orientation analysis. In particular, we propose a two-stage approach by coupling sentiment dictionary and classification methods. In the first stage, we build sentiment dictionary and rules to obtain the texts whose emotional scores are ranked in the top 1/4 and the bottom 1/4. These texts are marked classified for supervising the second stage. In the second stage, we employ the SVM classifier to proces

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27

Jurka, Timothy,P, Loren Collingwood, Amber,E Boydstun, Emiliano Grossman, and Wouter,van Atteveldt. "RTextTools: A Supervised Learning Package for Text Classification." R Journal 5, no. 1 (2013): 6. http://dx.doi.org/10.32614/rj-2013-001.

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28

Borisov, Leonid Andreevich, Anastasia Yurievna Ivchenko, Nikolay Alexeevich Mitin, and Yurii Nikolaevich Orlov. "Classification of text information with the use of bigram analysis." Keldysh Institute Preprints, no. 106 (2017): 1–22. http://dx.doi.org/10.20948/prepr-2017-106.

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29

Tuerxun, Palidan, Fang Dingyi, and Askar Hamdulla. "The KNN based Uyghur Text Classification and its Performance Analysis." International Journal of Hybrid Information Technology 8, no. 3 (2015): 63–72. http://dx.doi.org/10.14257/ijhit.2015.8.3.07.

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Tuerxun, Palidan, Fang Dingyi, and Hamdulla Askar. "The SVM based Uyghur Text Classification and its Performance Analysis." International Journal of Multimedia and Ubiquitous Engineering 10, no. 4 (2015): 283–90. http://dx.doi.org/10.14257/ijmue.2015.10.4.27.

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31

Syamala, Maganti, N. J. Nalini, Lakshamanaphaneendra Maguluri, and R. Ragupathy. "Comparative Analysis of Document level Text Classification Algorithms using R." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012076. http://dx.doi.org/10.1088/1757-899x/225/1/012076.

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32

Kumar, Akshi, Vikrant Dabas, and Parul Hooda. "Text classification algorithms for mining unstructured data: a SWOT analysis." International Journal of Information Technology 12, no. 4 (2018): 1159–69. http://dx.doi.org/10.1007/s41870-017-0072-1.

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33

Singh, Shailendra Kumar, and Manoj Kumar Sachan. "SentiVerb system: classification of social media text using sentiment analysis." Multimedia Tools and Applications 78, no. 22 (2019): 32109–36. http://dx.doi.org/10.1007/s11042-019-07995-2.

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34

Chi, Nai-Wen, Ken-Yu Lin, and Shang-Hsien Hsieh. "Using ontology-based text classification to assist Job Hazard Analysis." Advanced Engineering Informatics 28, no. 4 (2014): 381–94. http://dx.doi.org/10.1016/j.aei.2014.05.001.

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35

Singh, Shailendra Kumar, and Manoj Kumar Sachan. "Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis." International Journal on Semantic Web and Information Systems 17, no. 2 (2021): 59–78. http://dx.doi.org/10.4018/ijswis.2021040104.

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The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilin

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Sapozhnikova, L. E., and O. A. Gordeeva. "Text classification using convolutional neural network." Information Technology and Nanotechnology, no. 2416 (2019): 219–26. http://dx.doi.org/10.18287/1613-0073-2019-2416-219-226.

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In this article, the method of text classification using a convolutional neural network is presented. The problem of text classification is formulated, the architecture and the parameters of a convolutional neural network for solving the problem are described, the steps of the solution and the results of classification are given. The convolutional network which was used was trained to classify the texts of the news messages of Internet information portals. The semantic preprocessing of the text and the translation of words into attribute vectors are generated using the open word2vec model. The

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Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. "Feature Selection for Ordinal Text Classification." Neural Computation 26, no. 3 (2014): 557–91. http://dx.doi.org/10.1162/neco_a_00558.

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Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form. As in other supervised learning tasks such as binary or multiclass classification, feature selection is often needed in order to improve efficiency and avoid overfitting. However, although feature selection has been exte

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D'hondt, Eva, Suzan Verberne, Cornelis Koster, and Lou Boves. "Text Representations for Patent Classification." Computational Linguistics 39, no. 3 (2013): 755–75. http://dx.doi.org/10.1162/coli_a_00149.

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With the increasing rate of patent application filings, automated patent classification is of rising economic importance. This article investigates how patent classification can be improved by using different representations of the patent documents. Using the Linguistic Classification System (LCS), we compare the impact of adding statistical phrases (in the form of bigrams) and linguistic phrases (in two different dependency formats) to the standard bag-of-words text representation on a subset of 532,264 English abstracts from the CLEF-IP 2010 corpus. In contrast to previous findings on classi

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39

Thangaraj, M., and M. Sivakami. "Text Classification Techniques: A Literature Review." Interdisciplinary Journal of Information, Knowledge, and Management 13 (2018): 117–35. http://dx.doi.org/10.28945/4066.

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Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classifi

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Stringham, Oliver C., Stephanie Moncayo, Katherine G. W. Hill, et al. "Text classification to streamline online wildlife trade analyses." PLOS ONE 16, no. 7 (2021): e0254007. http://dx.doi.org/10.1371/journal.pone.0254007.

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Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing mode

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41

Lakhmi Prasanna, P., D. Rajeswara Rao, Y. Meghana, K. Maithri, and T. Dhinesh. "Analysis of supervised classification techniques." International Journal of Engineering & Technology 7, no. 1.1 (2017): 283. http://dx.doi.org/10.14419/ijet.v7i1.1.9486.

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As the number of digital documents and data are being increased rapidly, it is important to classify them in to respective categories. This process of classifying the data is called classification. There are three ways in to which the data can be classified un supervised, supervised and semi supervised methods. Automatic Text Classification is done by supervised learning techniques. This paper discusses about various classification techniques, their advantages and limitations. Finally, it concludes with the best classification technique. In this paper the best classification technique that was

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42

Guo, Qipeng, Xipeng Qiu, Pengfei Liu, Xiangyang Xue, and Zheng Zhang. "Multi-Scale Self-Attention for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7847–54. http://dx.doi.org/10.1609/aaai.v34i05.6290.

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In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules. We propose a Multi-Scale Transformer which uses multi-scale multi-head self-attention to capture features from different scales. Based on the linguistic perspective and the analysis of pre-trained Transformer (BERT) on a huge corpus, we further design a strategy to control the scale distribution for each layer. Results of three different kinds of tasks (21 datasets) show our Multi-Scale Transformer outperforms the standard Transformer consistently and significantly on small and moderate size da

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43

Yawale, Mr Pratik S. "Review Classification Approach for User Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1670–72. http://dx.doi.org/10.22214/ijraset.2021.35352.

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Sentiment analysis or opinion mining is one of the fastest growing fields with its demand and benefits that is increasing day by day. With the availability of the internet and modern technology, there has been a tremendous growth in the amount of data. The text that has been posted by people to express their sentiment on social media ,can be analysed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sen

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44

Uylaş Satı, Nur, and Burak Ordin. "Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis." Scientific Programming 2018 (June 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/5349284.

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In direct proportion to the heavy increase of online information data, the attention to text categorization (classification) has also increased. In text categorization problem, namely, text classification, the goal is to classify the documents into predefined classes (categories or labels). Recently various methods in data mining have been experienced for text classification in literature except polyhedral conic function (PCF) methods. In this paper, PCFs are used to classify the documents. The separation algorithms via PCFs which include linear programming subproblems with inequality constrai

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45

Lee, Yong-Gu. "Classification Performance Analysis of Cross-Language Text Categorization using Machine Translation." Journal of the Korean Society for Library and Information Science 43, no. 1 (2009): 313–32. http://dx.doi.org/10.4275/kslis.2009.43.1.313.

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Aggarwal, Archit. "Analysis of Feature Selection Methods for Text Classification using Multiple Datasets." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 720–26. http://dx.doi.org/10.22214/ijraset.2020.6116.

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Shinde, Ashwin. "A Review on Subjectivity Analysis through Text Classification Using Mining Techniques." International Journal of Engineering Research and Applications 07, no. 03 (2017): 38–40. http://dx.doi.org/10.9790/9622-0703013840.

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48

Sutar, M. M., and T. I. Bagban. "Applying Sentiment Analysis to Predict Rating and Classification of Text Review." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 173–78. http://dx.doi.org/10.26438/ijcse/v6i7.173178.

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Amrani, Yassine Al, Mohamed Lazaar, and Kamal Eddine El Kadiri. "A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4554. http://dx.doi.org/10.11591/ijece.v8i6.pp4554-4567.

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Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automat

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C.Dharmadhikari, S., Maya Ingle, and Parag Kulkarni. "Analysis of Semi Supervised Learning Methods towards Multi Label Text Classification." International Journal of Computer Applications 42, no. 16 (2012): 15–20. http://dx.doi.org/10.5120/5775-8026.

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