Sentiment Analysis Using Ensemble-Hybrid Model with Hypernym Based Feature Engineering

2020 ◽  
Author(s):  
Sashank Sridhar ◽  
Sowmya Sanagavarapu
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Feature Engineering

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment Analysis

2020 ◽  
Vol 19 (03) ◽  
pp. 2050019
Author(s):  
Hajar El Hannach ◽  
Mohammed Benkhalifa
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Active Phase ◽  
Semantic Relations ◽  
Term Extraction ◽  
Supervised Classifiers ◽  
Restaurant Reviews

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.

An Efficient Hybrid Model for Vietnamese Sentiment Analysis

2017 ◽  
pp. 227-237
Author(s):  
Thanh Hung Vo ◽  
Thien Tin Nguyen ◽  
Hoang Anh Pham ◽  
Thanh Van Le
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model

scholarly journals Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 596
Author(s):  
Kia Dashtipour ◽  
Mandar Gogate ◽  
Ahsan Adeel ◽  
Hadi Larijani ◽  
Amir Hussain
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
English Language ◽  
Short Term Memory ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Long Short Term Memory

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

scholarly journals Ensemble Sentiment Analysis Method based on R-CNN and C-RNN with Fusion Gate

2019 ◽  
Vol 14 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Fushen Yang ◽  
Changshun Du ◽  
Lei Huang
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Contextual Information ◽  
Final Analysis ◽  
Service Evaluation ◽  
Feature Engineering ◽  
Data Sets ◽  
Analysis Method ◽  
Analysis Model ◽  
Monitoring Service

Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis in the current network environment. At present, the sentiment analysis algorithms with good effects are all based on statistical learning methods. The performance of this method depends on the quality of feature extraction, while good feature engineering requires a high degree of expertise and is also time-consuming, laborious, and affords poor opportunities for mobility. Neural networks can reduce dependence on feature engineering. Recurrent neural networks can obtain context information but the order of words will lead to bias; the text analysis method based on convolutional neural network can obtain important features of text through pooling but it is difficult to obtain contextual information. Aiming at the above problems, this paper proposes a sentiment analysis method based on the combination of R-CNN and C-RNN based on a fusion gate. Firstly, RNN and CNN are combined in different ways to alleviate the shortcomings of the two, and the sub-analysis network R-CNN and C-RNN finally combine the two networks through the gating unit to form the final analysis model. We performed experiments on different data sets to verify the effectiveness of the method.

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