Sentiment Analysis : A Review and Comparative Analysis on Colleges
DOI:
https://doi.org/10.32628/CSEIT217266Keywords:
Sentiment Analysis, Machine Learning, Opinion MiningAbstract
Sentiment analysis is the process of detecting positive and negative sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, to understand it and make better. Sentiment analysis models focus on polarity (positive, negative and neutral) and even intentions (interested v. not interested). Depending on how you want to interpret feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs. This paper focuses the reviews of college which are an important form of opinion contents. The basic objective of this work is to classify every sentence’s semantic orientation (e.g. positive, negative and neutral) of the reviews. It is a really useful analysis since we could possibly determine the overall opinion about the colleges.
References
- Nageswara Rao Moparthi, Dr. N.Geethanjali, “Design and implementation of hybrid phase based ensemble technique for defect discovery using SDLC software metrics”, An International Conference by IEEE, AEEICB16 (978-1-4673-9745-2) PP. 269-276, ©2016 IEEE.
- Wei Zhao, et.al., "Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis", IEEE Transactions on Knowledge and Data Engineering, Vol. 30, No. 1, 185, 2018.
- Rui Xia, Feng X, ChengqingZong, Qianmu Li, Yong Qi, Tao Li,Dual, “Sentiment Analysis: Considering Two Sides of One Review”, IEEE Transactions On Knowledge And Data Engineering, VOL. 27, NO. 8, AUGUST 2015.
- Ch. Nanda Krishna, Dr. P. Vidya Sagar, Dr. Nageswara Rao Moparthi, ”Sentiment Analysis of Top Colleges”, 4th International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB-18).
- Chaturvedi, S., Mishra, V., & Mishra, N. (2017), “Sentiment analysis using machine learning for business intelligence”, International Conference on Power, Control, Signals and Instrumentation Engineering IEEE (ICPCSI), 1(1), 2162–2166.
- Dr.S.Gomathi, Rohini, Punitha. R, “Sentiment Analysis of Google Reviews of a College”, International Conference on Computing Intelligence and Data Science (ICCIDS 2018).
- Suci Laila Ramdhani, Rachmadita Andreswari, Muhammad Azani Hasibuan, “Sentiment Analysis of Product Reviews using Naïve Bayes Algorithm: A Case Study”, The 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 123-127, IEEE 2018.
- Vivek Narayanan, Ishan Arora, Arjun Bhatia, (2013), “Fast and Accurate Sentiment Classification Using an Enhanced Naïve Bayes Model”, Intelligent Data Engineering and Automated Learning – IDEAL, Springer, Vol. 8206.
- Nontobeko Hlongwane, Yo-Ping Huang, Li-Jen Kao, “Using Sentiment Analysis to Determine Users’ Likes on Twitter”, 16th Int. Conf. on Dependable, Autonomic & Secure Comp., ©2018.
- Dr.K.A.Waghmare, Miss. Sheetal K. Bhala, “Survey Paper on Sentiment Analysis for Tourist Reviews”, 2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22 – 24,IEEE 2020.
- Mais Yasen, Sara Tedmori, “Movies Reviews Sentiment Analysis and Classification”, Jordan International joint conference on Electrical and Information Technology (JEEIT), IEEE 2019.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.