DT-FNN based effective hybrid classification scheme for twitter sentiment analysis

2021 ◽  
Author(s):  
Huma Naz ◽  
Sachin Ahuja ◽  
Deepak Kumar ◽  
Rishu
Keyword(s):  
Sentiment Analysis ◽  
Classification Scheme ◽  
Hybrid Classification

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

XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

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

scholarly journals On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Asriyanti Indah Pratiwi ◽  
Adiwijaya
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Classification Scheme ◽  
Information Gain ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Enormous Number

Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.

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

scholarly journals Hybrid Classification Technique for Sentiment Analysis of the Twitter Data

2019 ◽  
Vol 8 (10) ◽  
pp. 1750-1753
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Research Work ◽  
Related Data ◽  
Twitter Data ◽  
Opinion Extraction ◽  
Social Media Platforms ◽  
Hybrid Classification ◽  
Media Platform

Sentiment can be described in the form of any type of approach, thought or verdict which results because of the occurrence of certain emotions. This approach is also known as opinion extraction. In this approach, emotions of different peoples with respect to meticulous rudiments are investigated. For the attainment of opinion related data, social media platforms are the best origins. Twitter may be recognized as a social media platform which is socially accessible to numerous followers. When these followers post some message on twitter, then this is recognized as tweet. The sentiment of twitter data can be analyzed with the feature extraction and classification approach. The hybrid classification is designed in this work which is the combination of KNN and random forest. The KNN classifier extract features of the dataset and random forest will classify data. The approach of hybrid classification is applied in this research work for the sentiment analysis. The performance of the proposed model is tested in terms of accuracy and execution time.

Opinion spam detection framework using hybrid classification scheme

2019 ◽  
Vol 24 (5) ◽  
pp. 3475-3498 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Asmat Ullah ◽  
Shakeel Ahmad ◽  
Aurangzeb Khan
Keyword(s):  
Classification Scheme ◽  
Spam Detection ◽  
Hybrid Classification ◽  
Opinion Spam
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