A Study Analyzing an Innovative Approach to Sentiment Analysis with VADER
Keywords:
Sentiment Analysis, Polarity, VADERAbstract
Sentiment Analysis is the process of determining whether a written piece conveys a positive, negative, or neutral tone. A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to entities, topics, themes, and categories within a sentence or phrase. Sentiment analysis aids data analysts in large enterprises to gauge public opinion, conduct nuanced market research, monitor brand and product reputation, and understand customer experiences.
VADER (Valence Aware Dictionary and Sentiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed specifically for analyzing sentiments expressed in social media. VADER utilizes a sentiment lexicon, which is a list of lexical features (such as words) categorized based on their semantic orientation as positive or negative. VADER not only provides a Positivity and Negativity score but also offers insights into the intensity of the sentiment. When analyzing a piece of text, VADER checks if any of the words in the text are present in the lexicon. It can identify elements that indicate the writer's sentiment, whether positive or negative, including informal language, multiple punctuation marks, acronyms, and even emoticons. Additionally, VADER accounts for intensifiers and negations in the text.
References
K. Zvarevashe and O. O. Olugbara, “A framework for sentiment analysis with opinion mining of hotel reviews,”
Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa,
, pp. 1-4.
Valdivia, Ana & Luzon, Maria & Herrera, Francisco. (2017). Sentiment Analysis in TripAdvisor. IEEE Intelligent Systems. 32. 72-77.
M. Wongkar and A. Angdresey, “Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter,” 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia,
, pp. 1-5,
L. Mandloi and R. Patel, “Twitter Sentiments Analysis Using Machine Learninig Methods,” 2020 International Conference for EmergingT echnology (INCET), Belgaum, India, 2020, pp. 1-5.
S. Dhawan, K. Singh and P. Chauhan, “Sentiment Analysis of Twitter Data in Online Social Network,” 2019 5th International Conferenceo n Signal Processing, Computing and Control (ISPCC), Solan, India, 2019, pp. 255-259, doi: 10.1109/ISPCC48220.2019.8988450.
Chauhan and S. Sehgal, “Sentiment analysis on product reviews,” 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, 2017, pp. 26-31.
C. Zhao, C. Hu and T. Peng, “Analysis of Product Evaluations: An Adaptive Approach Based on Extended Sentiment Dictionaries,” 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, 2017, pp. 148-152
Z. Singla, S. Randhawa and S. Jain, “Statistical and sentiment analysis of consumer product reviews,” 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, 2017, pp. 1-6.
P. R. Mala and S. S. Devi, “Product response analytics in Facebook,” 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 2017, pp. 1265-1269.
N. M. Shelke, V. Thakre and S. Deshpande, “Identification of scope of valence shifters for sentiment analysis of product reviews,” 2016 Sixth International Symposium on Embedded Computing and System Design (ISED), Patna, 2016, pp. 265-269.
Y. Hegde and S. K. Padma, “Sentiment Analysis Using Random Forest Ensemble for Mobile Product Reviews in Kannada,” 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 777-782.
Shastri, L., Parvathy, A. G., Kumar, A., Wesley, J., & Balakrish-nan, R. (2010). Sentiment Extraction. IAAI-10.
Snow, R., O’Connor, B., Jurafsky, D., & Ng, A. Y. (2008). Cheapand Fast - But is it Good?. In Proc. EMNLP-08.
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng,A., & Potts, C. (2013). Recursive Deep Models for
Semantic Compositionality Over Sentiment Treebank. In Proc. EMNLP-13.
Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M.(1966). General Inquirer. Cambridge, MA: MIT Press.
Strauss, A. L., & Corbin, J. (1998). Basics of Qualitative Re-search. Thousand Oaks, CA: Sage Publications.
Surowiecki, J. (2004). The Wisdom of Crowds. NY, NY: Anchor.
Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M.(2010). Predicting Elections with Twitter:. In Proc.
ICWSM-10.
Turney, P. D., & Littman, M. L. (2003). Measuring praise andcriticism. ACM Trans. Inf. Syst., 21(4), 315–346.
Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S.(2012). A system...real-time Twitter sentiment
analysis. ACL-12.
Wilson, T., Wiebe, J., & Hwa, R. (2004). Just how mad are you?finding strong and
weak opinion clauses. In Proc. NCAI-04s.
Published
Issue
Section
Copyright (c) 2023 Journal of Engineering Design and Analysis
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
We, the undersigned, give an undertaking to the following effect with regard to our article entitled
“_______________________________________________________________________________________________________________________________________________________________________________
________________________________________________________________________________” submitted for publication in (Journal title)________________________________________________ _______________________________________________________Vol.________, Year _________:-
1. The article mentioned above has not been published or submitted to or accepted for publication in any form, in any other journal.
2. We also vouchsafe that the authorship of this article will not be contested by anyone whose name(s) is/are not listed by us here.
3. I/We declare that I/We contributed significantly towards the research study i.e., (a) conception, design and/or analysis and interpretation of data and to (b) drafting the article or revising it critically for important intellectual content and on (c) final approval of the version to be published.
4. I/We hereby acknowledge ADRs conflict of interest policy requirement to scrupulously avoid direct and indirect conflicts of interest and, accordingly, hereby agree to promptly inform the editor or editor's designee of any business, commercial, or other proprietary support, relationships, or interests that I/We may have which relate directly or indirectly to the subject of the work.
5. I/We also agree to the authorship of the article in the following sequence:-
Authors' Names (in sequence) Signature of Authors
1. _____________________________________ _____________________________________
2. _____________________________________ _____________________________________
3. _____________________________________ _____________________________________
4. _____________________________________ _____________________________________
5. _____________________________________ _____________________________________
6. _____________________________________ _____________________________________
7. _____________________________________ _____________________________________
8. _____________________________________ _____________________________________
Important
(I). All the authors are required to sign independently in this form in the sequence given above. In case an author has left the institution/ country and whose whereabouts are not known, the senior author may sign on his/ her behalf taking the responsibility.
(ii). No addition/ deletion/ or any change in the sequence of the authorship will be permissible at a later stage, without valid reasons and permission of the Editor.
(iii). If the authorship is contested at any stage, the article will be either returned or will not be
processed for publication till the issue is solved.
