SENTIMENT CLASSIFICATION OF THE LOCAL VISITORS' SOCIAL MEDIA REVIEWS

2018 ◽  
Vol 4 (26) ◽  
pp. 5534-5538
Author(s):  
Semra AKTAŞ POLAT
Keyword(s):  
Social Media ◽  
Sentiment Classification

Sentiment Classification of Sinhala Content in Social Media: An Ensemble Approach

2021 ◽  
Author(s):  
Pradeep Jayasuriya ◽  
Ranjiva Munasinghe ◽  
Samantha Thelijjagoda
Keyword(s):  
Social Media ◽  
Sentiment Classification ◽  
Ensemble Approach

Sentiment classification of Sinhala content in social media

2020 ◽  
Author(s):  
Pradeep Jayasuriya ◽  
Sarith Ekanayake ◽  
Ranjiva Munasinghe ◽  
Bihara Kumarasinghe ◽  
Isuru Weerasinghe ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Classification

Sentiment Analysis System for Classification of Patient-Generated Health Reviews Using Rough Set Theory and Machine Learning Technique

2020 ◽  
Vol 10 (10) ◽  
pp. 2361-2368
Author(s):  
Alaa Omran Almagrabi ◽  
Shakeel Ahmad
Keyword(s):  
Social Media ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis System ◽  
Theory Technique

Advancements in social media domain have led to a prominent progress in the number of online communities. Sites, such as Twitter and Facebook, provide an avenue for the unrestricted generation, communication, and distribution of messages as well as information. In this work, we propose a sentiment classification system from patient-generated content posted by users on medical forums and social media sites. The rough set theory is a numerical rule-based technique employed for categorizing and examining doubtful, partial or indistinct data. The emphasis of this study is on the employment of the rough set theory technique for sentiment classification of patient-generated health reviews. We investigated four rough set theory-based algorithms, namely: Genetic, Learning from Examples Module version 2 (LEM2), Exhaustive and Covering, to generate rules for sentiment classification of patient-generated health reviews text. The Rough Set Exploration System (RSES 2.0) software is utilized to conduct experiments. Additionally, we applied SVM classifier to classify emotions. The experimental results show that the Genetic algorithm outperforms the comparing algorithms with an accuracy of 84.2% and Support Vector Machine outperforms other classifiers with an accuracy of 80.5%.

Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media

2020 ◽  
Vol 10 (2) ◽  
pp. 40-58 ◽  
Author(s):  
Sanur Sharma ◽  
Anurag Jain
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Information Gain ◽  
Empirical Evaluation ◽  
Ensemble Classifier ◽  
Sentiment Classification ◽  
Ensemble Classifiers ◽  
Chi Squared

This article presents a study on ensemble learning and an empirical evaluation of various ensemble classifiers and ensemble features for sentiment classification of social media data. The data was collected from Twitter in real-time using Twitter API and text pre-processing and ranking-based feature selection is applied to textual data. A framework for a hybrid ensemble learning model is presented where a combination of ensemble features (Information Gain and CHI-Squared) and ensemble classifier that includes Ada Boost with SMO-SVM and Logistic Regression has been implemented. The classification of Twitter data is performed where sentiment analysis is used as a feature. The proposed model has shown improvements as compared to the state-of-the-art methods with an accuracy of 88.2% with a low error rate.

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