Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction

2016 ◽  
pp. 379-397 ◽  
Author(s):  
Georgina Cosma ◽  
Giovanni Acampora
Keyword(s):  
Sentiment Analysis ◽  
Neuro Fuzzy ◽  
Rating Prediction ◽  
Customer Review

scholarly journals CUSTOMER REVIEW ANALYSIS - MULTI-LABEL CLASSIFICATION AND SENTIMENT ANALYSIS

2020 ◽  
Vol 07 (04) ◽  
pp. 28-32
Author(s):  
Shreyas Renga ◽  
Abishek Ganapathy ◽  
T. Hasith Ram Varma
Keyword(s):  
Sentiment Analysis ◽  
Review Analysis ◽  
Customer Review

scholarly journals Measuring e-Commerce service quality from online customer review using sentiment analysis

2018 ◽  
Vol 971 ◽  
pp. 012053 ◽  
Author(s):  
Puspita Kencana Sari ◽  
Andry Alamsyah ◽  
Sulistyo Wibowo
Keyword(s):  
Service Quality ◽  
Sentiment Analysis ◽  
Customer Review

Topic modelling and hotel rating prediction based on customer review in Indonesia

2021 ◽  
Vol 20 (3) ◽  
pp. 282
Author(s):  
Yunanto Putranto ◽  
Bagus Sartono ◽  
Anik Djuraidah
Keyword(s):  
Topic Modelling ◽  
Rating Prediction ◽  
Customer Review

scholarly journals ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction

2021 ◽  
Author(s):  
Jiahao Bu ◽  
Lei Ren ◽  
Shuang Zheng ◽  
Yang Yang ◽  
Jingang Wang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rating Prediction

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

2020 ◽  
pp. 2050010
Author(s):  
Fouzi Harrag ◽  
Abdulmalik Salman Al-Salman ◽  
Alaa Alquahtani
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Good Accuracy ◽  
Hybrid Approach ◽  
Data Sparsity ◽  
Hybrid Recommender System ◽  
Rating Prediction ◽  
Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

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