.png){kind=icon}
返回
KL-NF technique for sentiment classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-021-10559-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work proposes sentiment analysis for low-resource languages like Hindi using Neuro-Fuzzy Technique. Low-resource languages suffer from the scarcity of resources; consequently, we propose a method that can be implemented for any language. We use information theory for establishing a relation between terms that exists in a sentence. This work proposes a novel approach for calculating feature values using Kullback-Leibler (KL) divergence method. The feature values are employed to calculate the membership values associated with the Fuzzy logic in Neuro-Fuzzy Technique. The novelty of this method lies in its predictive nature that can mitigate the impact generated from un-labeled, unknown data or multi-domain data. We have seen the results for multi-domain data in our experiments. We evaluate our results using Accuracy, Precision, Recall and F1-Score. Our experiments show the efficacy of the proposed approach. It achieved 93.01% accuracy for English dataset and 91.18% accuracy for Hindi dataset which is more than the other state-of-art techniques like Naive Bayes and SVM. Additionally, we found that our approach provides satisfactory results with multi-domain data as both the datasets were of different domains.
Keyword:
Sentiment analysis
Neuro-fuzzy
KL divergence
Hindi
Low-resource language
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Impaired perceptual learning in a mouse model of Fragile X syndrome is mediated by parvalbumin neuron dysfunction and is reversible脆性X综合征小鼠模型的感知学习受损是由小白蛋白神经元功能障碍介导的，并且是可逆的
OAAI
OAAI分享收藏One-pot synthesis of uniform and monodisperse superparamagnetic molecularly imprinted polymer nanospheres through a sol–gel process for selective recognition of bisphenol A in aqueous media.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容