ADS-Classif: A Robust Tool for Classifying Documents from Diverse Sources Using Weighted Domain-Specific Vocabulary

REFERENCES

• A. I. Kadhim, “Survey on supervised machine learning techniques for automatic text classification,” Artif. Intell. Rev., Vol. 52, no. 1, pp. 273–292, Jan. 2019. DOI: 10.1007/s10462-018-09677-1
   Web of Science ®Google Scholar
• F. Sebastiani, “Machine learning in automated text categorization,” ACM. Comput. Surv., Vol. 34, no. 1, pp. 1–47, Mar. 2002. DOI: 10.1145/505282.505283
   Web of Science ®Google Scholar
• V. Kalra, I. Kashyap, and H. Kaur, “Generation of domain-specific vocabulary set and classification of documents: Weight-inclusion approach,” Int. J. Inf. Technol., Vol. 14, no. 1, pp. 275–285, Feb. 2022. DOI: 10.1007/S41870-021-00830-8
   Google Scholar
• P. Fernandes, L. O. C. Furquim, and L. Lopes, “A supervised method to enhance vocabulary with the creation of domain specific lexica,” in Proceedings - IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 3, pp. 139–142, Nov. 2013. DOI: 10.1109/WI-IAT.2013.168
   Google Scholar
• A. Boldyreva, O. Sobolevskiy, M. Alexandrov, and V. Danilova, “Creating collections of descriptors of events and processes based on internet queries,” in Advances in Computational Intelligence. Mexican International Conference on Artificial Intelligence, Cham: Springer, Oct. 2016, Vol. 10061, pp. 303–314. DOI: 10.1007/978-3-319-62434-1_26
   Google Scholar
• S. S. Tan and J. C. Na, “Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis,” in Digital Libraries: Knowledge, Information, and Data in an Open Access Society. International Conference on Asian Digital Libraries, Cham: Springer, Dec. 2016, Vol. 10075, pp. 285–296. DOI: 10.1007/978-3-319-49304-6_34
   Google Scholar
• Y. Hayashi and T. Ishida, “A dictionary model for unifying machine readable dictionaries and computational concept lexicons,” in Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA), May 2006.
   Google Scholar
• B. Ranaivo-Malançon, S. Saee, R. M. Othman, and J. F. W. Busu, “Transforming semi-structured indigenous dictionary into machine-readable dictionary,” J. Telecommun. Electron. Comput. Eng. (JTEC), Vol. 9, no. 3-11, pp. 7–11, Dec. 2017. Available: https://jtec.utem.edu.my/jtec/article/view/3173
   Google Scholar
• H. Kim and H. H. Wright, “A tutorial on core lexicon: Development, use, and application,” Semin. Speech Lang., Vol. 41, no. 1, pp. 20–31, Jan. 2020. DOI: 10.1055/S-0039-3400973/ID/JR00765-45
   PubMed Web of Science ®Google Scholar
• M. Al-Yahya, S. Al-Malak, and L. Aldhubayi, “Ontological lexicon enrichment: The Badea system for semi-automated extraction of antonymy relations from arabic language corpora,” MJCS, Vol. 29, no. 1, pp. 56–73, Mar. 2016. DOI: 10.22452/mjcs.vol29no1.5
   Google Scholar
• B. Altınel and M. C. Ganiz, “Semantic text classification: A survey of past and recent advances,” Inf. Process. Manage., Vol. 54, no. 6, pp. 1129–1153, Nov. 2018. DOI:10.1016/j.ipm.2018.08.001
   Web of Science ®Google Scholar
• Y. Li, H. Guo, Q. Zhang, M. Gu, and J. Yang, “Imbalanced text sentiment classification using universal and domain-specific knowledge,” Knowl. Based. Syst., Vol. 160, pp. 1–15, Nov. 2018. DOI: 10.1016/J.KNOSYS.2018.06.019
   Web of Science ®Google Scholar
• M. R. Islam and M. F. Zibran, “A comparison of dictionary building methods for sentiment analysis in software engineering text,” Int. Symp. Empirical Software Eng. Meas., Vol. 2017, pp. 478–479, Dec. 2017. DOI: 10.1109/ESEM.2017.67
   Google Scholar
• C. S. P. Kumar and L. D. D. Babu, “Evolving dictionary based sentiment scoring framework for patient authored text,” Evol. Intell., Vol. 14, no. 2, pp. 657–667, 2021. DOI: 10.1007/S12065-020-00366-Z
   Web of Science ®Google Scholar
• V. Jha, R. Savitha, P. D. Shenoy, K. R. Venugopal, and A. K. Sangaiah, “A novel sentiment aware dictionary for multi-domain sentiment classification,” Comput. Electr. Eng., Vol. 69, pp. 585–597, Jul. 2018. DOI: 10.1016/j.compeleceng.2017.10.015
   Web of Science ®Google Scholar
• M. Duşcu and D. Günneç, “Polarity classification of Twitter messages using audio processing,” Inf. Process. Manag., Vol. 57, no. 6, pp. 102346, Nov. 2020. DOI: 10.1016/J.IPM.2020.102346
   Web of Science ®Google Scholar
• M. Sood, H. Kaur, and J. Gera. “Creating domain-based dictionary and its evaluation using classification accuracy,” in Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development, INDIACom 2021, pp. 341–347, Mar. 2021. DOI: 10.1109/INDIACOM51348.2021.00059
   Google Scholar
• M. Song, H. Park, and K. shik Shin, “Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean,” Inf. Process. Manage., Vol. 56, no. 3, pp. 637–653, May 2019. DOI: 10.1016/j.ipm.2018.12.005
   Web of Science ®Google Scholar
• Q. Qiu, Z. Xie, L. Wu, and L. Tao, “Dictionary-based automated information extraction from geological documents using a deep learning algorithm,” Earth Space Sci., Vol. 7, no. 3, pp. e2019EA000993, Mar. 2020. DOI: 10.1029/2019EA000993
   Web of Science ®Google Scholar