LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic

2019 ◽  
pp. 108-121
Author(s):  
Kathrein Abu Kwaik ◽  
Motaz Saad ◽  
Stergios Chatzikyriakidis ◽  
Simon Dobnik
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Dialectal Arabic ◽  
Deep Learning Model

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals A Multi-Layer Dual Attention Deep Learning Model With Refined Word Embeddings for Aspect-Based Sentiment Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 114795-114807 ◽  
Author(s):  
Syeda Rida-E-Fatima ◽  
Ali Javed ◽  
Ameen Banjar ◽  
Aun Irtaza ◽  
Hassan Dawood ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Word Embeddings ◽  
Deep Learning Model

scholarly journals A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets

2021 ◽  
pp. 107242
Author(s):  
Mohammad Ehsan Basiri ◽  
Shahla Nemati ◽  
Moloud Abdar ◽  
Somayeh Asadi ◽  
U. Rajendra Acharrya
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Deep Learning Model

Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain

2021 ◽  
pp. 125-137
Author(s):  
Isanka Rajapaksha ◽  
Chanika Ruchini Mudalige ◽  
Dilini Karunarathna ◽  
Nisansa de Silva ◽  
Amal Shehan Perera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Legal Domain ◽  
Deep Learning Model

scholarly journals Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying

2019 ◽  
Vol 9 (13) ◽  
pp. 2760 ◽  
Author(s):  
Khai Tran ◽  
Thi Phan
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Model ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model

Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.

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