Adaptive sentiment analysis using multioutput classification: a performance comparison
TL;DR: In this paper , a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees.
read more
Abstract: The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
DistilRoBiLSTMFuse: an efficient hybrid deep learning approach for sentiment analysis
Sonia Khan Papia,Md Asif Khan,Tanvir Habib,Mizanur Rahman,Md. Nahidul Islam +4 more
TL;DR: This study proposes DistilRoBiLSTMFuse, a hybrid deep learning approach for sentiment analysis, achieving outstanding performance on IMDb (93.97%) and Twitter USAirline (98.33%) datasets, outperforming existing state-of-the-art methods.
References
Survey on Multi-Output Learning
TL;DR: The four Vs of multi-output learning are characterized, i.e., volume, velocity, variety, and veracity, and the ways in which the four Vs both benefit and bring challenges to multi- output learning by taking inspiration from big data are examined.
191
A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets.
TL;DR: In this paper, the authors have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores.
Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains
TL;DR: It is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach not only in terms of Accuracy, Precision, Recall and F-measure but also in termsof economy of time and efforts used.
102
Chinese Micro-Blog Sentiment Analysis Based on Multiple Sentiment Dictionaries and Semantic Rule Sets
TL;DR: A method for constructing multiple sentiment dictionaries, which mainly constructs original sentiment dictionary, emoji dictionary, and other related dictionaries are proposed, which innovatively constructed a Chinese micro-blog new word sentiment dictionary.
Sentiment analysis algorithms: evaluation performance of the Arabic and English language
Mohamed Elhag Mohamed Abo,Nordiana Ahmad Kharman Shah,Vimala Balakrishnan,Ahmed Abdelaziz +3 more
- 01 Aug 2018
TL;DR: This paper evaluates and discussed the application of Naive Bayes and Decision Tree in sentiment analysis using a multi-dataset in different languages to understand which can give a better result when used with $ML$ algorithms.
32