SocialRec: A Context-Aware Recommendation Framework With Explicit Sentiment Analysis

Irfan, Rizwana; Khalid, Osman; Khan, Muhammad Usman Shahid; Rehman, Faisal; Khan, Atta ur Rehman; Nawaz, Raheel

doi:10.1109/access.2019.2932500

Cited by 13 publications

(5 citation statements)

References 45 publications

(69 reference statements)

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“…Irfan et al [8] proposed a hybrid framework that uses context-aware recommendation based on product ratings and customer reviews. They have used text mining methods over large-scale user item feedback to calculate the sentiment scores.…”

Section: Related Workmentioning

confidence: 99%

A New Fuzzy Lexicon Expansion and Sentiment Aware Recommendation System in e-Commerce

Manikandan¹,

P²,

S³

2023

IJACSA

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Customers' feedbacks are necessary for an online business to enrich themselves. The customers' feedback reflects the quality of the products and the e-commerce services. The companies are in a position to concentrate more and analyze the customers' feedback or reviews carefully by applying new techniques for predicting the current trends, customers' expectations, and the quality of their services. The e-business will succeed when one accurately predicts customer purchase patterns and expectations. For this purpose, we propose a new fuzzy logic incorporated sentiment analysis-based product recommendation system to predict the customers' needs and recommend suitable products successfully. The proposed system incorporates a newly developed sentiment analysis model which incorporates the classification through fuzzy temporal rules. Moreover, the basic level data preprocessing activities such as stemming, stop word removal, syntax analysis and tokenization are performed to enhance the sentiment classification accuracy. Finally, this product recommendation system recommends suitable products to the customers by predicting the customers' needs and expectations. The proposed system is evaluated using the Amazon dataset and proved better than the existing recommendation systems regarding precision, recall, serendipity and nDCG.

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Section: Related Workmentioning

confidence: 99%

A New Fuzzy Lexicon Expansion and Sentiment Aware Recommendation System in e-Commerce

Manikandan¹,

P²,

S³

2023

IJACSA

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“…Furthermore, it is possible to identify the website that is being viewed on the network [26]- [28]. Moreover, user activities can be revealed by the network traffic [10], [29]- [31]. Private information can also be leaked in location-based applications [32]- [35].…”

Section: Related Workmentioning

confidence: 99%

Fingerprinting Technique for YouTube Videos Identification in Network Traffic

et al. 2022

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Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users' activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) -the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.

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“…Multi-domain sentiment analysis approaches focus on developing models to transfer information between different domains. Although these approaches enable the transfer of domain-specific knowledge to other domains, limitation is the need to construct new transfer models for each additional domain analysis [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

Kadurhalli Sangappa,

Chikkanaravangala Paramashivaia

2024

IJEECS

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<span>Sentiment analysis is a well-recognized research field that has acknowledged significant attention in recent years. Researchers have made extensive efforts in employing various methodologies to explore these domains. Sentiment classification plays a fundamental role in natural language processing (NLP). However, studies have shown that sentiment classification models heavily depend on the specific domain. In the context of movie industry, where the demand for reliable movie reviews is high and not all movies are of exceptional quality and worthy of viewers time. Therefore, people depend on movie reviews before watching a movie. This explores the use of data from various domains to improve classification performance within each domain, addressing the difficulty of multi-domain sentiment classification in natural language processing. Therefore, it is crucial to effectively utilize shared sentiment knowledge across different domains for real-world applications. To solve these issues, a multi-domain aspect-oriented sentiment analysis for movie recommendation using feature extraction techniques. The main contribution of this work is to eliminate the time for users to go through a lengthy list of movies to make their decision. The novelty of this work is analysis of different movie genres, TV shows genres with accurate results. The presented approach's performance is validated by evaluating various metrics, including precision, recall, mean square error (MSE) and F1-score.</span>

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SocialRec: A Context-Aware Recommendation Framework With Explicit Sentiment Analysis

Cited by 13 publications

References 45 publications

A New Fuzzy Lexicon Expansion and Sentiment Aware Recommendation System in e-Commerce

A New Fuzzy Lexicon Expansion and Sentiment Aware Recommendation System in e-Commerce

Fingerprinting Technique for YouTube Videos Identification in Network Traffic

Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

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