Ensemble of Classifiers and Term Weighting Schemes for Sentiment Analysis in Turkish

Author:

Onan AytuğORCID,

Abstract

With the advancement of information and communication technology, social networking and microblogging sites have become a vital source of information. Individuals can express their opinions, grievances, feelings, and attitudes about a variety of topics. Through microblogging platforms, they can express their opinions on current events and products. Sentiment analysis is a significant area of research in natural language processing because it aims to define the orientation of the sentiment contained in source materials. Twitter is one of the most popular microblogging sites on the internet, with millions of users daily publishing over one hundred million text messages (referred to as tweets). Choosing an appropriate term representation scheme for short text messages is critical. Term weighting schemes are critical representation schemes for text documents in the vector space model. We present a comprehensive analysis of Turkish sentiment analysis using nine supervised and unsupervised term weighting schemes in this paper. The predictive efficiency of term weighting schemes is investigated using four supervised learning algorithms (Naive Bayes, support vector machines, the k-nearest neighbor algorithm, and logistic regression) and three ensemble learning methods (AdaBoost, Bagging, and Random Subspace). The empirical evidence suggests that supervised term weighting models can outperform unsupervised term weighting models.

Publisher

Izmir UOD

Reference29 articles.

1. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. J. (2011, June). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media (LSM 2011) (pp. 30-38).

2. A survey of text classification algorithms;Aggarwal;In Mining text data (pp,2012

3. Bagging predictors;Breiman;Machine learning,1996

4. On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis;Carvalho;Artificial Intelligence Review,2020

5. Representation learning for very short texts using weighted word embedding aggregation;Boom;Pattern Recognition Letters,2016

Cited by 31 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems;Journal of Software: Evolution and Process;2024-02-24

2. Sentiment Analysis for Products Review based on NLP using Lexicon-Based Approach and Roberta;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

3. A data-driven ensemble machine learning approach for predicting the mechanical strength of 3D printed orthopaedic bone screws;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2023-11-23

4. Rating Text Classification with Weighted Negative Supervision on Classifier Layer;Chinese Journal of Electronics;2023-11

5. Quadratic optimization for the hyper-parameter based on maximum entropy search;Journal of Intelligent & Fuzzy Systems;2023-08-24

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3