Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain

Osman, Nurul Aida; Noah, Shahrul Azman Mohd; Darwich, Mohammad

doi:10.18178/ijmlc.2019.9.4.821

Cited by 32 publications

(14 citation statements)

References 29 publications

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“…In yet another approach, Osman et al [ 30 ] presented an electronic product recommender system based on contextual information from sentiment analysis. Because ratings are usually insufficient and very limited, they constructed a contextual information sentiment model for a recommender system by making use of user comments and preferences.…”

Section: Background and Related Workmentioning

confidence: 99%

An Approach to Integrating Sentiment Analysis into Recommender Systems

Dang

García

Prieta

2021

Sensors

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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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Section: Background and Related Workmentioning

confidence: 99%

An Approach to Integrating Sentiment Analysis into Recommender Systems

Dang

García

Prieta

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In yet another approach, Osman et al [10] presented an electronic product recommender system based on contextual information from sentiment analysis. Because ratings are usually insufficient and very limited, they constructed a contextual information sentiment model for a recommender system by making use of user comments and preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach to Integrating Sentiment Analysis into Recommender Systems

Dang¹,

Moreno²,

Prieta³

2021

Preprint

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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 in order 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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“…Step 2 (14,943). On the other hand, the total number of nouns that have high similarity values with the main aspects that are stored in the Main_Aspects list and chosen as core terms in the summary field is 208.…”

Section: ) Generation Of the Main Aspects And Core Termsmentioning

confidence: 99%

“…It has an obvious effect in many applications when the aspects are extracted efficiently with their opinions' values. For example, they can be used to construct user and item profiles in decision-making processes, such as recommender systems [1,14], or to construct a domain ontology [15]. Additionally, it has been used in many systems for different purposes such as tourism [16], online education [17], and transportation [18].…”

Section: Introductionmentioning

confidence: 99%