Aspect Based Opinion Mining & Sentiment Analysis

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Alka Londhe
P. V. R. D. Prasada Rao


Opinion mining is a relatively new field that refers to the practice of collecting feedback in the form of online reviews and ratings left by users on various topics. Researchers are now able to monitor the states of consciousness of individuals in real-time because to this development. Just lately, a number of research papers for sentiment analysis were implemented, each of which was based on a unique categorization and ranking procedure. However, the amount of time necessary for the newline performing class has not decreased in any way. Sentiment Sensitivity newline word list SST was provided as a solution to the problem of function mismatch in the go-domain sentiment class across the source area and the target domain; however, achieving improved accuracy and identifying distributional similarities of words became less effective as time went on. Hidden Markov’s persistent development may be seen at the beginning. Cosine In order to achieve more effective and clean pre-processing, a method that is conceptually quite similar to HM-CPCS has been devised. The HM-CPCS methodology, which has recently been suggested, makes use of the POS tagger, a variant of which is based on the Hidden Markov algorithm. Evaluations are created using data from a wide variety of different domains. Similar to a newline, the tags that come before and after it compute the possibility of transitions and the existence of the term newline among the tags in order to increase capability. This is done in order to improve capability.

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How to Cite
Londhe, A. ., and P. V. R. D. P. . Rao. “Aspect Based Opinion Mining &Amp; Sentiment Analysis”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 10, Oct. 2022, pp. 131-42, doi:10.17762/ijritcc.v10i10.5742.


Aarati Mahadik, Asha Bharambe, “Aspect Based Opinion Mining and Ranking: Survey”, International Journal of Current Engineering and Technology, vol. 5, no. 6, pp. 3589-3592, December 2015.

Zhao C, Wang S, Li D (2016) Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model. PLoS ONE 11(11): e0165560.

Abbasi, S. France, Z. Zhang and H. Chen, "Selecting Attributes for Sentiment Classification Using Feature Relation Networks," in IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 3, pp. 447-462, March 2011, doi: 10.1109/TKDE.2010.110.

Alireza Yousefpour , Roliana Ibrahim & Haza Nuzly Abdel Hamed 2017, “Ordinal-based and Frequency-based Integration of Feature Selection Methods for Sentiment Analysis”, Expert Systems with Applications, Elsevier, vol.75, pp. 80-93.

Alpaslan Burak Eliacik and Nadia Erdogan. 2018. “Influential user weighted sentiment analysis on topic based microblogging community”. Expert Syst. Appl. 92, C (February 2018), 403–418.

Anindya Ghose & Panagiotis G Ipeirotis (2011, “Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics”, IEEE Transactions on Knowledge and Data Engineering, vol. 23, issue no. 10, pp. 1498 – 1512.

Anna Jurek, Maurice D Mulvenna & Yaxin Bi 2015, “Improved lexicon based sentiment analysis for social media analytics”, Security Informatics, Springer, vol. 4, issue no. 9,pp. 1-13.

Barath Sriram R, Abhishek Balaji, Dhanya R & Mariappan, AK 2015, “Ranking Of Products Using Opinion Mining On Authentic Reviews”, International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), vol. 13, issue no. 4, pp. 208-211.

Cagatay Catal & Mehmet Nangir 2017, “A sentiment classification model based on multiple classifiers”, Applied Soft Computing, Elsevier, vol. 50, pp. 135–141.

Charalampos Karyotis, Faiyaz Doctor, Rahat Iqbal, Anne James & Victor Chang 2017, “A fuzzy computational model of emotion for cloud based sentiment analysis”, Information Sciences, Elsevier, pp. 1-40.

Chihli Hung & Shiuan-Jeng Chen 2016, “Word sense disambiguation based sentiment lexicons for sentiment classification”, KnowledgeBased Systems, Elsevier, vol. 110, pp. 224-232.

Danushka Bollegala, David Weir & John Carroll 2013, “Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus”, IEEE Transactions on Knowledge and Data Engineering, vol. 25, issue no. 8, pp. 1719-1731.

Samad, A. . (2022). Internet of Things Integrated with Blockchain and Artificial Intelligence in Healthcare System. Research Journal of Computer Systems and Engineering, 3(1), 01–06. Retrieved from

Danushka Bollegala, Tingting Mu & John Y Goulermas 2016, “Crossdomain Sentiment Classification using Sentiment Sensitive Embeddings”, IEEE Transactions on Knowledge and Data Engineering, vol. 28, issue no. 2, pp. 398 – 410.

Dim En Nyaung & Thin Lai Lai Thein 2015, “Feature-Based Summarizing and Ranking from Customer Reviews”, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 9, issue no. 3, pp. 734-739.

Kshirsagar, D. P. R. ., Patil, D. N. N. ., & Makarand L., M. . (2022). User Profile Based on Spreading Activation Ontology Recommendation. Research Journal of Computer Systems and Engineering, 3(1), 73–77. Retrieved from

S. L. Bangare, G. Pradeepini, S. T. Patil, “Implementation for brain tumor detection and three dimensional visualization model development for reconstruction”, ARPN Journal of Engineering and Applied Sciences (ARPN JEAS), Vol.13, Issue.2, ISSN 1819-6608, pp.467-473. 20/1/2018

Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, and Shahajan Miah, “Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis”, Hindawi, Journal of Food Quality, Volume 2022, Article ID 6987569, 8 pages,

S. L. Bangare, S. Prakash, K. Gulati, B. Veeru, G. Dhiman and S. Jaiswal, "The Architecture, Classification, and Unsolved Research Issues of Big Data extraction as well as decomposing the Internet of Vehicles (IoV)," 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), 2021, pp. 566-571, doi: 10.1109/ISPCC53510.2021.9609451.

N. Shelke, S. Chaudhury, S. Chakrabarti, S. L. Bangare et al. “An efficient way of text-based emotion analysis from social media using LRA-DNN”, Neuroscience Informatics, Volume 2, Issue 3, September 2022, 100048, ISSN 2772-5286,

Suneet Gupta, Sumit Kumar, Sunil L. Bangare, Shibili Nuhmani, Arnold C. Alguno, Issah Abubakari Samori, “Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media”, Computational Intelligence and Neuroscience, vol. 2022, Article ID 3490860, 9 pages, 2022.

Gururaj Awate, S. L. Bangare, G. Pradeepini and S. T. Patil, “Detection of Alzheimers Disease from MRI using Convolutional Neural Network with Tensorflow”, arXiv,

Xu Wu, Dezhi Wei, Bharati P. Vasgi, Ahmed Kareem Oleiwi, Sunil L. Bangare, and Evans Asenso, “Research on Network Security Situational Awareness Based on Crawler Algorithm”, Security and Communication Networks, Hindawi, ISSN:1939-0114, E-ISSN:1939-0122,

Yusuf, K., Shuaibu, D. S., & Babale, S. A. (2022). Channel Propagation Characteristics on the Performance of 4G Cellular Systems from High Altitude Platforms (HAPs). International Journal of Communication Networks and Information Security (IJCNIS), 13(3).

Tang, Duyu & Wei, Furu & Yang, Nan & Zhou, Ming & Liu, Ting & Qin, Bing. (2014), “Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification” 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference. 1. 1555-1565. 10.3115/v1/P14-1146.

Kotelnikov, Evgeny & Pletneva, M. (2016), “Text sentiment classification based on a genetic algorithm and word and document co-clustering”. Journal of Computer and Systems Sciences International. 55. 106-114. 10.1134/S1064230715060106.

Turovsky, O. L., Vlasenko, V., Rudenko, N., Golubenko, O., Kitura, O., & Drobyk, O. (2022). Two-Time Procedure for Calculation of Carrier Frequency of Phasomodulated in Communication Systems. International Journal of Communication Networks and Information Security (IJCNIS), 13(3).

Jyoti S. Deshmukh and Amiya Kumar Tripathy, “Entropy based classifier for cross-domain opinion mining”, Elsevier Journal of Applied Computing and Informatics, Volume 14, Issue 1, pp. 55-64, January 2018.

S. D. Pande and M. S. R. Chetty. (2018) Analysis of Capsule Network (Capsnet) Architectures and Applications, Journal of Advanced Research in Dynamical and Control Systems, Vol. 10, No. 10, pp. 2765-2771.

Sandeep Pande and Manna Sheela Rani Chetty, “Bezier Curve Based Medicinal Leaf Classification using Capsule Network”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 8, No. 6, pp. 2735-2742, 2019.

Pande S.D., Chetty M.S.R. (2021) Fast Medicinal Leaf Retrieval Using CapsNet. In: Bhattacharyya S., Nayak J., Prakash K.B., Naik B., Abraham A. (eds) International Conference on Intelligent and Smart Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 1312.