IEEE Style Citation: Varsha Pal, Akshay Varkale, “Statistical Modeling for Sentiment Classification: A Review,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.100-105, 2020.
MLA Style Citation: Varsha Pal, Akshay Varkale "Statistical Modeling for Sentiment Classification: A Review." International Journal of Computer Sciences and Engineering 8.10 (2020): 100-105.
APA Style Citation: Varsha Pal, Akshay Varkale, (2020). Statistical Modeling for Sentiment Classification: A Review. International Journal of Computer Sciences and Engineering, 8(10), 100-105.
BibTex Style Citation:
@article{Pal_2020,
author = {Varsha Pal, Akshay Varkale},
title = {Statistical Modeling for Sentiment Classification: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2020},
volume = {8},
Issue = {10},
month = {10},
year = {2020},
issn = {2347-2693},
pages = {100-105},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5239},
doi = {https://doi.org/10.26438/ijcse/v8i10.100105}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i10.100105}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5239
TI - Statistical Modeling for Sentiment Classification: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Varsha Pal, Akshay Varkale
PY - 2020
DA - 2020/10/31
PB - IJCSE, Indore, INDIA
SP - 100-105
IS - 10
VL - 8
SN - 2347-2693
ER -
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Abstract
Sentiment classification is the process of using NLP, statistics, or machine learning methods to extract, identify, or otherwise characterize the sentiment content of a text unit. Based on a sample of tweets, how are people responding to this ad campaign/product release/news item? There are several application of opinion mining such as on business intelligence, Politics/political science, Law/policy making, Sociology, Psychology etc. By use of digital platform administration can collect response from consumer and by means of applying opinion mining technique a useful information from user collected data. In this paper we have given a brief review over different work done in the field of sentiment classification and given tabular comparison among different opinion classification technique based on accuracy.
Key-Words / Index Term
TPR,FNR,ML,NL,SVM ANN
References
[1] B. Vamshi Krishna, Ajeet Kumar Pandey and A. P. Siva Kumar Feature Based Opinion Mining and Sentiment Analysis Using Fuzzy Logic Springer 2018.
[2] Y. Wang, J. Zhang Keyword Extraction from Online Product Reviews Based on Bi-Directional LSTM Recurrent Neural Network IEEE 2017.
[3] Sandra Garcia Esparza , Michael P. O’Mahony, Barry Smyth Mining the real-time web: A novel approach to product recommendation Elsevier 2011.
[4] Isa Maks , Piek Vossen A lexicon model for deep sentiment analysis and opinion mining applications Elsevier 2012.
[5] Rodrigo Moraes, João Francisco Valiati, Wilson P. Gavião Neto Document-level sentiment classification: An empirical comparison between SVM and ANN Elsevier 2013.
[6] Tarik S. Zakzouk Comparing text classifiers for sports news Elsevier 2012.
[7] Ngoc Phuong Chau, Viet Anh Phan, Minh Le Nguyen Deep Learning and Sub-Tee Mining for Document Level Sentiment Classification KSE 2016.
[8] Ivo Danihelka et. al. Associative Long Short-Term Memory arXiv 2016.
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