Recommendation system using sentiment analysis considering the polarity of the adverb

Guimaraes, Rita Georgina; Rodríguez, Demóstenes Zegarra; Rosa, Renata Lopes; Bressan, Graça

doi:10.1109/isce.2016.7797377

Cited by 23 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, several architectural models for DL algorithms have been presented for many applications, each one with its particularities and means of use [30][31][32][33][34].…”

Section: Deep Learning Algorithms For Object Detectionmentioning

confidence: 99%

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Barbosa

Ayub

Rosa

et al. 2020

Sensors

View full text Add to dashboard Cite

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

show abstract

“…In recent years, several architectural models for DL algorithms have been presented for many applications, each one with its particularities and means of use [30][31][32][33][34].…”

Section: Deep Learning Algorithms For Object Detectionmentioning

confidence: 99%

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Barbosa

Ayub

Rosa

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The main limitation of collaborative filtering systems is that it suffers from limited data information expandability and the problem to a cold start [12]. Mansur et al [14] have observed common challenges across most collaborative filtering algorithms, including difficulty in providing recommendations to new users, limited trust in the conclusions made from small data, user data privacy, and sparsity of data [15] [16].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Yeh¹,

Kashef²

2020

AIT

View full text Add to dashboard Cite

A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as banking, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the usage of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be applied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced research on Blockchain and recommendation systems. We aim at building a secure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance.

show abstract

“…4, August 2019 sentiment analysis in recommender system. A research by [29] discussed about recommendation system using sentiment analysis considering the polarity of adverbs. Shapira et al [11] also discussed on the introduction and challenges in recommender system which was directed to the solution of applying sentiment analysis in recommender systems.…”

Section: Sentiment Based Approach In Product Recommendationmentioning

confidence: 99%

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

Osman

Noah

Darwich

2019

IJMLC

View full text Add to dashboard Cite

The rush to purchase the latest products sometimes prevents people from thinking things through completely. Consequently, recommender services are increasingly emerging. By looking at industry trends, interviewing dozens of leading industry stakeholders, and using publicly available information, it is important to filter out the most relevant information for consumer electronics before purchasing their items. This paper presents an electronic product recommender system based on contextual information from sentiment analysis. The recommendation algorithms mostly rely on users' rating to make prediction of items. Such ratings are usually insufficient and very limited. We present a contextual information sentiment based model for recommender system by making use of user comments and preferences to provide a recommendation. The purpose of this approach is to avoid term ambiguity which is so called domain sensitivity problem in recommendation. The proposed contextual information sentiment-based model illustrates better performance by using results of RMSE and MAE measurements as compared to the conventional collaborative filtering approach in electronic product recommendation.

show abstract

Recommendation system using sentiment analysis considering the polarity of the adverb

Cited by 23 publications

References 2 publications

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

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

Contact Info

Product

Resources

About