Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services

Dang, Cach N.; García, María N. Moreno; Prieta, Fernando De la

doi:10.3390/electronics10202459

Cited by 16 publications

(4 citation statements)

References 30 publications

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“…Using sentiment analysis to gain a deeper understanding of user preferences, Dang et al. ( 2021 ) proposed methods to enhance the functionality of recommender systems for streaming services. The Multimodal Album Reviews Dataset (MARD) and Amazon Movie Reviews were used to test and compare two different LSTM and CNN combinations, LSTM-CNN and CNN-LSTM.…”

Section: Applications Of Sentiment Analysismentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

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Section: Applications Of Sentiment Analysismentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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“…The proposed hybrid deep learning model consists of three main components: a word embedding layer, a CNN layer, and an LSTM layer with an attention mechanism, as proposed in the research [20] and using the research [21], but these models are applied to English languages. In this paper, we apply two models on Vietnamese language.…”

Section: Proposed Modelmentioning

confidence: 99%

Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models

Dang¹,

García²,

Prieta³

et al. 2023

Preprint

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Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.

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“…For instance, Alatrash et al [15] presented a recommendation approach that integrates sentiment analysis and genrebased similarity in collaborative filtering methods. Dang et al [29] proposed using BERT for genre preprocessing and feature extraction and hybrid deep learning models for sentiment analysis of user reviews.…”

Section: B: Sentiment Analysis For Enhanced Recommendationsmentioning

confidence: 99%

“…However, recommendation systems face challenges such as data sparsity, cold start, and the need for collecting past user feedback [26], [27]. Researchers are developing more effective recommendation algorithms to overcome these challenges and improve accuracy and user satisfaction [28], [29]. As the amount of data being collected continues to increase due to technological advances, there is a growing need for techniques that can efficiently handle large amounts of data [30], [31].…”

mentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services

Cited by 16 publications

References 30 publications

Sentiment analysis: A survey on design framework, applications and future scopes

Sentiment analysis: A survey on design framework, applications and future scopes

Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models

Deep Learning-Based Recommendation System: Systematic Review and Classification

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