Hybrid Deep Learning Models for Sentiment Analysis

Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

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ASA: A framework for Arabic sentiment analysis

Journal of Information Science ◽

10.1177/0165551519849516 ◽

2019 ◽

Vol 46 (4) ◽

pp. 544-559 ◽

Cited By ~ 4

Author(s):

Ahmed Oussous ◽

Fatima-Zahra Benjelloun ◽

Ayoub Ait Lahcen ◽

Samir Belfkih

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Short Term Memory ◽

Research Area ◽

Support Vector ◽

Learning Models ◽

Arabic Natural Language Processing ◽

Arabic Sentiment Analysis

Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

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A semiautomatic annotation approach for sentiment analysis

Journal of Information Science ◽

10.1177/01655515211006594 ◽

2021 ◽

pp. 016555152110065

Author(s):

Rahma Alahmary ◽

Hmood Al-Dossari

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Annotation Process ◽

Learning Classifiers ◽

Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Applied Sciences ◽

10.3390/app11188438 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8438

Author(s):

Muhammad Mujahid ◽

Ernesto Lee ◽

Furqan Rustam ◽

Patrick Bernard Washington ◽

Saleem Ullah ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Online Education ◽

Sentiment Analysis ◽

Topic Modeling ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

E Learning ◽

Machine Learning Models

Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

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Empirical Evaluation of Shallow and Deep Learning Classifiers for Arabic Sentiment Analysis

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3466171 ◽

2022 ◽

Vol 21 (1) ◽

pp. 1-25

Author(s):

Ali Bou Nassif ◽

Abdollah Masoud Darya ◽

Ashraf Elnagar

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Empirical Evaluation ◽

Detailed Comparison ◽

Learning Models ◽

Similar Work ◽

Learning Classifiers ◽

Arabic Sentiment Analysis ◽

Gated Recurrent Units

This work presents a detailed comparison of the performance of deep learning models such as convolutional neural networks, long short-term memory, gated recurrent units, their hybrids, and a selection of shallow learning classifiers for sentiment analysis of Arabic reviews. Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model. The datasets used in this study are multi-dialect Arabic hotel and book review datasets, which are some of the largest publicly available datasets for Arabic reviews. Results showed deep learning outperforming shallow learning for binary and multi-label classification, in contrast with the results of similar work reported in the literature. This discrepancy in outcome was caused by dataset size as we found it to be proportional to the performance of deep learning models. The performance of deep and shallow learning techniques was analyzed in terms of accuracy and F1 score. The best performing shallow learning technique was Random Forest followed by Decision Tree, and AdaBoost. The deep learning models performed similarly using a default embedding layer, while the transformer model performed best when augmented with araBERT.

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Bi-LSTM Sentiment Classifier for Climate Change Issues in South Korea

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1056.0782s619 ◽

2019 ◽

Vol 8 (2S6) ◽

pp. 295-299

Keyword(s):

Climate Change ◽

Machine Learning ◽

Big Data ◽

South Korea ◽

Sentiment Analysis ◽

Training Data ◽

Learning Models ◽

Wide Range ◽

Machine Learning Models ◽

Big Data Technology

A sentiment analysis using SNS data can confirm various people’s thoughts. Thus an analysis using SNS can predict social problems and more accurately identify the complex causes of the problem. In addition, big data technology can identify SNS information that is generated in real time, allowing a wide range of people’s opinions to be understood without losing time. It can supplement traditional opinion surveys. The incumbent government mainly uses SNS to promote its policies. However, measures are needed to actively reflect SNS in the process of carrying out the policy. Therefore this paper developed a sentiment classifier that can identify public feelings on SNS about climate change. To that end, based on a dictionary formulated on the theme of climate change, we collected climate change SNS data for learning and tagged seven sentiments. Using training data, the sentiment classifier models were developed using machine learning models. The analysis showed that the Bi-LSTM model had the best performance than shallow models. It showed the highest accuracy (85.10%) in the seven sentiments classified, outperforming traditional machine learning (Naive Bayes and SVM) by approximately 34.53%p, and 7.14%p respectively. These findings substantiate the applicability of the proposed Bi-LSTM-based sentiment classifier to the analysis of sentiments relevant to diverse climate change issues.

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A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Information ◽

10.3390/info12090374 ◽

2021 ◽

Vol 12 (9) ◽

pp. 374

Author(s):

Babacar Gaye ◽

Dezheng Zhang ◽

Aziguli Wulamu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

State Of The Art ◽

Accuracy Score ◽

Learning Models ◽

Proposed Model

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

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An Approach to Integrating Sentiment Analysis into Recommender Systems

Sensors ◽

10.3390/s21165666 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5666

Author(s):

Cach N. Dang ◽

María N. Moreno-García ◽

Fernando De la Prieta

Keyword(s):

Deep Learning ◽

Collaborative Filtering ◽

Sentiment Analysis ◽

Recommender Systems ◽

User Satisfaction ◽

Online News ◽

Learning Models ◽

Adaptive Architecture ◽

Wide Range ◽

The One

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

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Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Sensors ◽

10.3390/s20082359 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2359 ◽

Cited By ~ 3

Author(s):

Meriem Zerkouk ◽

Belkacem Chikhaoui

Keyword(s):

Deep Learning ◽

Behavioral Problems ◽

Short Term Memory ◽

Training Data ◽

Abnormal Behavior ◽

Elderly Persons ◽

Learning Models ◽

Accuracy Measure ◽

Public Data ◽

Abnormal Behaviors

The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.

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Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Entropy ◽

10.3390/e23050596 ◽

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.

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