Understanding customer sentiment: lexical analysis of restaurant reviews

Jinat Ara, Md Toufique Hasan, Abdullah Al Omar, Hanif Bhuiyan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

9 Citations (Scopus)

Abstract

Understanding customer's sentiment (satisfaction or dissatisfaction) is considered as valuable information for both the potential customers and restaurant authority. However, analyzing customer reviews (unstructured texts) one by one is a difficult task and also practically impossible when the number of reviews is enormous. Therefore, it seems conceivable to have a mechanism to analyze customer reviews automatically and provide the necessary information in a precise way. Here, we introduce a Natural Language Processing (NLP) based opinion mining methodology to analyze the customer opinion automatically. For that, first, a captive portal is used to collect customer's reviews. Then, the opinion mining technique is applied to analyze the reviews to explore customer sentiment about food quality, service, environment, etc. A data-driven experiment is conducted to evaluate the proposed methodology. The experiment result showed the effectiveness of the proposed method for retrieving and analyzing customer sentiment.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE Region 10 Symposium, TENSYMP 2020
Place of PublicationBangladesh
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages295-299
Number of pages5
Edition1st
ISBN (Electronic)9781728173665
DOIs
Publication statusPublished - 5 Jun 2020
Externally publishedYes
EventIEEE Region 10 Symposium 2020 - Virtual/Online, Dhaka, Bangladesh
Duration: 5 Jun 20207 Jun 2020
https://ieeexplore.ieee.org/xpl/conhome/9230456/proceeding

Conference

ConferenceIEEE Region 10 Symposium 2020
Abbreviated titleTENSYMP 2020
Country/TerritoryBangladesh
CityDhaka
Period5/06/207/06/20
Internet address

Keywords

  • captive portal
  • customer opinion
  • lexical analysis
  • opinion mining
  • sentiment analysis

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