SUMMARY IN GENERAL SUMMARY OF AN OVERVIEW OF OPINION MINING
DOI:
https://doi.org/10.36755/jac.v1i1.47Keywords:
Text summarization , Opinion Mining , Sentiment Analysis , Natural LanguageAbstract
The internet is a very effective resource for solving all problems in the present era. The world's population as a whole spends one-third of their time and money using the internet. People learn things from it in every aspect of life, including education, entertainment, communication, shopping, etc. In order to achieve this, consumers take use of websites and share comments or opinions about various goods, services, events, etc. based on their personal experiences. In this way, the input from those webs is composed into a sizable amount of textual data that can be investigated, assessed, and controlled for the decision-making process. Natural Language Processing (NLP) and the extraction of the key theme are both aspects of opinion mining (OM). It's crucial to get feedback on our ideas from people in the form of good, negative, and neutral remarks. Researchers are now presenting information in the form of summaries that will benefit a variety of people. Since the 1950s, the research community has generated automatic summaries, but there are two distinct types of automation: abstractive and extractive approaches. A brief summary of opinion mining's automation process approaches and summarization techniques is provided in this paper.
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[1] Li, P., Bing, L., Lam, W., Li, H., & Liao, Y. (2015b, April). Reader-Aware Multi-Document Summari-zation via Sparse Coding. In IJCAI (pp. 1270-1276).
[2] Parveen, D., & Strube, M. (2015, July). Integrating Importance, Non-Redundancy and Coherence in Graph-Based Extractive Summari-zation. In IJCAI (pp. 1298-1304). DOI: https://doi.org/10.18653/v1/D15-1226
[3] Yao, J. G., Wan, X., & Xiao, J. (2015, June). Compressive Docu-ment Summarization via Sparse Optimization. In IJCAI(pp. 1376-1382).
[4] Banerjee, S., Mitra, P., & Sugiya-ma, K. (2015, July). Multi-Document Abstractive Summariza-tion Using ILP Based Multi-Sentence Compression. In IJCAI (pp. 1208-1214).
[5] Oufaida, H., Blache, P., & Nouali, O. (2015, June). Using Distributed Word Representations and mRMR Discriminant Analysis for Multilin-gual Text Summarization. In International Conference on Applications of Natural Language to Information Systems (pp. 51-63). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-19581-0_4
[6] Gupta, V. (2013). Hybrid algorithm for multilingual summarization of Hindi and Punjabi documents. In Mining Intelligence and Knowledge Exploration (pp. 717-727). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-03844-5_70
[7] Bairi, R., Iyer, R. K., Ramakrish-nan, G., & Bilmes, J. A. (2015). Summarization of Multi-Document Topic Hierarchies using Submodu-lar Mixtures. In ACL (1) (pp. 553-563). DOI: https://doi.org/10.3115/v1/P15-1054
[8] Kedzie, C., McKeown, K., & Diaz, F. (2015). Predicting Salient Up-dates for Disaster Summarization. In ACL (1) (pp. 1608-1617). DOI: https://doi.org/10.3115/v1/P15-1155
[9] Hong, K., Marcus, M., & Nenkova, A. (2015). System Combination for Multi-document Summarization. In EMNLP(pp. 107-117). DOI: https://doi.org/10.18653/v1/D15-1011
[10] Yao, J. G., Wan, X., & Xiao, J. (2015). Phrase-based Compressive Cross-Language Summarization. In EMNLP (pp. 118-127). DOI: https://doi.org/10.18653/v1/D15-1012
[11] Khan, A., Salim, N., & Kumar, Y. J. (2015). A framework for multi-document abstractive summarization based on semantic role labelling. Applied Soft Com-puting, 30, 737-747. DOI: https://doi.org/10.1016/j.asoc.2015.01.070
[12] Rush, A. M., Chopra, S., & Weston, J. (2015). A neural atten-tion model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685. DOI: https://doi.org/10.18653/v1/D15-1044
[13] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient fore-casting approach for resource utili-zation in cloud data center using CNN-LSTM model. Neural Com-puting and Applications, 33(16), 10043-10055.
[14] Ullah, A., & Nawi, N. M. (2021). An improved in tasks allo-cation system for virtual machines in cloud computing using HBAC algorithm. Journal of Ambient In-telligence and Humanized Compu-ting, 1-14. DOI: https://doi.org/10.1007/s12652-021-03496-z
[15] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient fore-casting approach for resource utili-zation in cloud data center using CNN-LSTM model. Neural Com-puting and Applications, 33(16), 10043-10055 DOI: https://doi.org/10.1007/s00521-021-05770-9
[16] Sebai, D., & Shah, A. U. (2022). Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencod-ers. Signal, Image and Video Pro-cessing, 1-9. DOI: https://doi.org/10.1007/s11760-022-02231-1
[17] Alam, T., Ullah, A., & Benaida, M. (2022). Deep rein-forcement learning approach for computation offloading in block-chain-enabled communications sys-tems. Journal of Ambient Intelli-gence and Humanized Computing, 1-14. DOI: https://doi.org/10.1007/s12652-021-03663-2
[18] Bing, L., Li, P., Liao, Y., Lam, W., Guo, W., & Passonneau, R. J. (2015). Abstractive multi-document summarization via phrase selection and merging. arXiv pre-print arXiv:1506.01597. DOI: https://doi.org/10.3115/v1/P15-1153
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Copyright (c) 2023 Rifat Mustafa, Shabana Rai, Ubaid Ullah, Muhammad Sohaib Naz (Author)
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