T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme

2017 ◽  
Vol 35 (1) ◽  
pp. e12233 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Fazal Masud Kundi ◽  
Shakeel Ahmad ◽  
Aurangzeb Khan ◽  
Furqan Khan
Keyword(s):  
Sentiment Analysis ◽  
Classification Scheme ◽  
Analysis Framework ◽  
Hybrid Classification

scholarly journals Lexicon-enhanced sentiment analysis framework using rule-based classification scheme

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171649 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Aurangzeb Khan ◽  
Shakeel Ahmad ◽  
Maria Qasim ◽  
Imran Ali Khan
Keyword(s):  
Sentiment Analysis ◽  
Classification Scheme ◽  
Analysis Framework ◽  
Rule Based

XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

2014 ◽  
Author(s):  
Caroline Brun ◽  
Diana Nicoleta Popa ◽  
Claude Roux
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Classification

Sentiment Analysis as a Service: A Social Media Based Sentiment Analysis Framework

2017 ◽  
Author(s):  
Kashif Ali ◽  
Hai Dong ◽  
Athman Bouguettaya ◽  
Abdelkarim Erradi ◽  
Rachid Hadjidj
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Framework

UAE e-Learning Sentiment Analysis Framework

2021 ◽  
Author(s):  
Dana Wehbe ◽  
Ahmed Alhammadi ◽  
Hajar Almaskari ◽  
Kholoud Alsereidi ◽  
Heba Ismail
Keyword(s):  
Sentiment Analysis ◽  
Analysis Framework ◽  
E Learning

Sentiment Analysis Technologies in AliMe — An Intelligent Assistant for E-commerce

2021 ◽  
pp. 2050016
Author(s):  
Shuangyong Song ◽  
Chao Wang ◽  
Siyang Liu ◽  
Haiqing Chen ◽  
Huan Chen ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Customer Service ◽  
User Satisfaction ◽  
Business Value ◽  
Analysis Framework ◽  
Artificial Intelligent ◽  
Single Task ◽  
Sentiment Detection ◽  
Intelligent Assistant ◽  
Key Techniques

In this paper, we introduce a sentiment analysis framework and its corresponding key techniques used in AliMe, an artificial intelligent (AI) assistant for e-commerce customer service, whose fundamental ability of sentiment analysis provides support for five upper-layer application modules: user sentiment detection, user sentiment comfort, sentimental generative chatting, user service quality control and user satisfaction prediction. Detailed implementation of each module is demonstrated and experiments show our framework not only performs well on each single task but also manifests its competitive business value as a whole.

scholarly journals A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

2020 ◽  
Vol 380 ◽  
pp. 1-10 ◽  
Author(s):  
Kia Dashtipour ◽  
Mandar Gogate ◽  
Jingpeng Li ◽  
Fengling Jiang ◽  
Bin Kong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Analysis Framework ◽  
Dependency Grammar
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