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

A new web personalization decision-support artifact for utility-sensitive customer review analysis

2017 ◽  
Vol 94 ◽  
pp. 85-96 ◽  
Author(s):  
Long Flory ◽  
Kweku-Muata Osei-Bryson ◽  
Manoj Thomas
Keyword(s):  
Decision Support ◽  
Web Personalization ◽  
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

Product Review Analysis Using Social Media Data Based on Sentiment Analysis

2020 ◽  
pp. 527-533
Author(s):  
S. M. Mazharul Hoque Chowdhury ◽  
Sheikh Abujar ◽  
Ohidujjaman ◽  
Khalid Been Md. Badruzzaman ◽  
Syed Akhter Hossain
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Product Review ◽  
Social Media Data ◽  
Review Analysis ◽  
Media Data

FLORA: a hierarchical fuzzy system for online accommodation review analysis

2019 ◽  
Vol 21 (3) ◽  
pp. 347-367
Author(s):  
Thara Angskun ◽  
Jitimon Angskun
Keyword(s):  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Online Reviews ◽  
Online Review ◽  
Probability Weighting ◽  
Content Type ◽  
Analysis Process ◽  
Review Analysis ◽  
Accuracy Comparison ◽  
Hierarchical Fuzzy System

Purpose This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap. Design/methodology/approach FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine. Findings The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating. Research limitations/implications This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base. Practical implications This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system. Originality/value This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

scholarly journals Opinion Mining of Amazon Data for Alexa Review Analysis

2021 ◽  
Vol 9 (VII) ◽  
pp. 3814-3819
Author(s):  
Neetima Gautam
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Review Analysis ◽  
Text Information ◽  
Frame Work

The fast expansion in piles of unstructured literary information joined by multiplication of devices to investigate them has opened up extraordinary freedoms and difficulties for text mining research. The programmed naming of information is hard in light of the fact that individuals regularly express feelings in complex manners that are here and there hard to fathom. The marking interaction includes tremendous measure of endeavours and mislabelled datasets typically lead to erroneous choices. In this paper, we plan a frame work for sentiment analysis with opinion mining for the instance of Amazon Alexa. Most accessible datasets are not named which presents a great deal of works for scientists as tolls text information pre-preparing task is concerned. Also, supposition datasets are frequently profoundly area touchy and difficult to make since assumptions are sentiments like feelings, mentalities and conclusions that are ordinarily overflowing with phrases, sound to word imitations, homophones, phonemes, similar sounding word usages and abbreviations. The proposed system is named feeling extremity that naturally readies a supposition dataset for preparing and testing to extricate impartial assessments of inn administrations from surveys to find a reasonable AI calculation for the grouping segment of the structure.

Customer Review Analysis: A Systematic Review

2021 ◽  
Author(s):  
Storm Davis ◽  
Nasseh Tabrizi
Keyword(s):  
Systematic Review ◽  
Review Analysis ◽  
Customer Review
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