Open Access   Article Go Back

Sentiment Analysis: Approaches and Methods

Amardeep Kaur1 , Jagroop Kaur2

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 1285-1287, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.12851287

Online published on Jul 31, 2018

Copyright © Amardeep Kaur, Jagroop Kaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Amardeep Kaur, Jagroop Kaur, “Sentiment Analysis: Approaches and Methods,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1285-1287, 2018.

MLA Style Citation: Amardeep Kaur, Jagroop Kaur "Sentiment Analysis: Approaches and Methods." International Journal of Computer Sciences and Engineering 6.7 (2018): 1285-1287.

APA Style Citation: Amardeep Kaur, Jagroop Kaur, (2018). Sentiment Analysis: Approaches and Methods. International Journal of Computer Sciences and Engineering, 6(7), 1285-1287.

BibTex Style Citation:
@article{Kaur_2018,
author = {Amardeep Kaur, Jagroop Kaur},
title = {Sentiment Analysis: Approaches and Methods},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {1285-1287},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2601},
doi = {https://doi.org/10.26438/ijcse/v6i7.12851287}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.12851287}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2601
TI - Sentiment Analysis: Approaches and Methods
T2 - International Journal of Computer Sciences and Engineering
AU - Amardeep Kaur, Jagroop Kaur
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 1285-1287
IS - 7
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
949 360 downloads 185 downloads
  
  
           

Abstract

Sentiment Analysis is application that is changing the ecommerce and many other businesses around the world. It is mainly an application related with text mining and works with the integration of machine learning algorithms(ML) and deep learning algorithms. It is used to increase the business productivity and also to better the customer experience by providing meaningful data out of unstructured data. This paper explains different ways and levels to do sentiment analysis and also explains Natural Language Processing(NLP) and its different approaches. Therefore, this paper brings an overview on sentiment analysis and different techniques and approaches integrated with it.

Key-Words / Index Term

Lexicon, Machine Learning, NLP,Semantic Analysis, Keyword Spotting

References

[1] Anna Baj-Rogowska(2017),” Sentiment Analysis of Facebook Posts:the Uber case”, IEEE International Conference on Intelligent Computing and Information Systems (ICICIS).
[2] Antonio Teixeira and Raul M.S. Laureano(2013),”Data extraction and preparation to perform the sentiment analysis using open source tools”.
[3] Sanjida Akter and Muhammad Tareq Aziz(2016),” Sentiment Analysis On Facebook Group Using Lexicon Based Approach”,IEEE.
[4] Saud Alashri, Srinivasa Srivatsav Kandala, Vikash Bajaj, Roopek Ravi, Kendra L. Smith and Kevin C. Desouza(2016),” An Analysis of Sentiments on Facebook during the
2016 U.S. Presidential Election”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
[5] N. AZMINA M. ZAMANI, SITI Z. Z. ABIDIN, NASIROH OMAR, M. Z. Z.ABIDEN(2013),”Sentiment Analysis: Determining People`s Emotions in Facebook”, Universiti Teknologi MARA, Malaysia.
[6]Vishal A. Kharde and S.S. Sonawane(2016),” Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.11.
[7] Shufeng Xiong, Hailian Lv , Weiting Zhao, Donghong Ji(2017),” Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings”, Elsevier.
[8] Nehal Mamgain, Ekta Mehta, Ankush Mittal and Gaurav Bhatt(2016),” Sentiment Analysis of Top Colleges in India Using Twitter Data”, International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).
[9] Aliaksei Severyn abd Aliaksei Severyn(2015),” Twitter Sentiment Analysis with Deep Convolutional Neural Networks”, ACM. ISBN 978-1-4503-3621-5/15/08.
[10] Jˆonatas Wehrmann, Willian Becker, Henry E. L. Cagnini, and Rodrigo C. Barros(2017),” A Character-based Convolutional Neural Network for Language-Agnostic Twitter Sentiment Analysis”,IEEE.
[11] Nhan Cach Dang, Fernando De la Prieta, Juan Manuel Corchado and María N. Moreno(2016),” Framework for Retrieving Relevant Contents Related to Fashion from Online Social Network Data, Springer.