Enhancement of the Lexical Approach by N-Grams Technique via Improving Negation-Based Traditional Sentiment Analysis
Keywords:
Negation handling, N-Grams techniques, Sentiment analysis, Pre-processing technique, Machine learningAbstract
Sentiment analysis, often known as opinion mining, is a significant area in artificial intelligence today. Sentiment analysis was widely observed in this field. Currently, a lot of data is constantly being exchanged as text on social networking and e-commerce platforms like Facebook, Twitter, Amazon, etc. Therefore, sentiment analysis is the best technique for businesses to comprehend what their customers want from them so that they may adapt their plans in response to client feedback and expand their customer base. To extract the exact meaning from the text is a tough task. So here, our effort is to get the positive and negative sentiment of reviews from the dataset and enhance the performance of sentiment through Natural language processing (NLP) over pre-existing pre-processing technique and machine learning algorithms. So for this purpose, we have an Amazon product review dataset. Which is an extract from the Kaggle website. In this study, we aim to remove noise from the dataset and improve the traditional NLP preprocessing technique after that, we will use Term Frequency-Inverse Document Frequency (TF-IDF) method for feature selection and then classify the result through the classification algorithm such as Artificial Neural Network (ANN), Naïve Bayes (NB), and Support Vector Machine (SVM).
Downloads
References
M. A. Palomino and F. Aider, “Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis,” Appl. Sci., vol. 12, no. 17, Sep. 2022, doi: 10.3390/app12178765.
V. Chang, L. Liu, Q. Xu, T. Li, and C. H. Hsu, “An improved model for sentiment analysis on luxury hotel review,” in Expert Systems, John Wiley and Sons Inc, Feb. 2023. doi: 10.1111/exsy.12580.
A. Iqbal, R. Amin, J. Iqbal, R. Alroobaea, A. Binmahfoudh, and M. Hussain, “Sentiment Analysis of Consumer Reviews Using Deep Learning,” Sustain., vol. 14, no. 17, Sep. 2022, doi: 10.3390/su141710844.
K. Gupta, N. Jiwani, and P. Whig, “Effectiveness of Machine Learning in Detecting Early-Stage Leukemia,” 2023, pp. 461–472. doi: 10.1007/978-981-19-2535-1_34.
R. Prabowo and M. Thelwall, “Sentiment analysis: A combined approach,” J. Informetr., vol. 3, no. 2, pp. 143–157, Apr. 2009, doi: 10.1016/j.joi.2009.01.003.
Y. Yu, W. Duan, and Q. Cao, “The impact of social and conventional media on firm equity value: A sentiment analysis approach,” Decis. Support Syst., vol. 55, no. 4, pp. 919–926, Nov. 2013, doi: 10.1016/j.dss.2012.12.028.
R. Kumar Behera, S. Kumar Rath, S. Misra, R. Damaševičius, and R. Maskeliūnas, “Distributed Centrality Analysis of Social Network Data Using MapReduce,” Algorithms, vol. 12, no. 8, p. 161, Aug. 2019, doi: 10.3390/a12080161.
S. Vohra and J. Teraiya, “Applications and Challenges for Sentiment Analysis : A Survey,” Int. J. Eng. Res. Technol., vol. 2, 2013.
A. Alsayat, “Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model,” Arab. J. Sci. Eng., vol. 47, no. 2, pp. 2499–2511, Feb. 2022, doi: 10.1007/s13369-021-06227-w.
N. P. Cruz, M. Taboada, and R. Mitkov, “A machine-learning approach to negation and speculation detection for sentiment analysis,” J. Assoc. Inf. Sci. Technol., vol. 67, no. 9, pp. 2118–2136, Sep. 2016, doi: 10.1002/asi.23533.
J. Serrano-Guerrero, J. A. Olivas, F. P. Romero, and E. Herrera-Viedma, “Sentiment analysis: A review and comparative analysis of web services,” Inf. Sci. (Ny)., vol. 311, pp. 18–38, Aug. 2015, doi: 10.1016/j.ins.2015.03.040.
B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Found. Trends® Inf. Retr., vol. 2, no. 1–2, pp. 1–135, 2008, doi: 10.1561/1500000011.
I. Councill, R. Mcdonald, and L. Velikovich, “What’s great and what’s not: Learning to classify the scope of negation for improved sentiment analysis,” pp. 51–59, Jun. 2010.
Aashutosh Bhatt, Ankit Patel, Harsh Chheda, and Kiran Gawande, “Amazon Review Classification and Sentiment Analysis ,” Int. J. Comput. Sci. Inf. Technol., vol. 6 (6), pp. 5107–5110, 2015.
E. Haddi, X. Liu, and Y. Shi, “The Role of Text Pre-processing in Sentiment Analysis,” Procedia Comput. Sci., vol. 17, pp. 26–32, 2013, doi: 10.1016/j.procs.2013.05.005.
K. S. Srujan, S. S. Nikhil, H. Raghav Rao, K. Karthik, B. S. Harish, and H. M. Keerthi Kumar, “Classification of Amazon Book Reviews Based on Sentiment Analysis,” 2018, pp. 401–411. doi: 10.1007/978-981-10-7512-4_40.
N. Prollochs, S. Feuerriegel, and D. Neumann, “Enhancing Sentiment Analysis of Financial News by Detecting Negation Scopes,” in 2015 48th Hawaii International Conference on System Sciences, IEEE, Jan. 2015, pp. 959–968. doi: 10.1109/HICSS.2015.119.
N. Prollochs, S. Feuerriegel, and D. Neumann, “Detecting Negation Scopes for Financial News Sentiment Using Reinforcement Learning,” in 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE, Jan. 2016, pp. 1164–1173. doi: 10.1109/HICSS.2016.147.
L. Rokach, R. Romano, and O. Maimon, “Negation recognition in medical narrative reports,” Inf. Retr. Boston., vol. 11, no. 6, pp. 499–538, Dec. 2008, doi: 10.1007/s10791-008-9061-0.
B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based Framework for Text Categorization,” Procedia Eng., vol. 69, pp. 1356–1364, 2014, doi: 10.1016/j.proeng.2014.03.129.
T. U. Haque, N. N. Saber, and F. M. Shah, “Sentiment analysis on large scale Amazon product reviews,” in 2018 IEEE International Conference on Innovative Research and Development (ICIRD), IEEE, May 2018, pp. 1–6. doi: 10.1109/ICIRD.2018.8376299.
B. S. Rintyarna, R. Sarno, and C. Fatichah, “Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context,” Int. J. Inf. Decis. Sci., vol. 12, no. 1, p. 75, 2020, doi: 10.1504/IJIDS.2020.104992.
A. Ritter, Mausam, O. Etzioni, and S. Clark, “Open domain event extraction from twitter,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA: ACM, Aug. 2012, pp. 1104–1112. doi: 10.1145/2339530.2339704.
A. R. Razon, J. A. Barnden, and J. A. Barnden@cs, “A New Approach to Automated Text Readability Classification based on Concept Indexing with Integrated Part-of-Speech n-gram Features.”
M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif. Intell. Rev., vol. 55, no. 7, pp. 5731–5780, Oct. 2022, doi: 10.1007/s10462-022-10144-1.
A. P. Rodrigues et al., “Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/5211949.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Harish Dutt Sharma, Sanjay Sharma
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
