Version 1
: Received: 17 April 2024 / Approved: 17 April 2024 / Online: 18 April 2024 (08:07:13 CEST)
How to cite:
Fragkos, N.; Liapakis, A.; Ntaliani, M.; Ntalianis, F.; Costopoulou, C. A Sentiment Analysis Approach for Exploring CustomerReviews of Online Food Delivery Services: A Greek Case. Preprints2024, 2024041203. https://doi.org/10.20944/preprints202404.1203.v1
Fragkos, N.; Liapakis, A.; Ntaliani, M.; Ntalianis, F.; Costopoulou, C. A Sentiment Analysis Approach for Exploring CustomerReviews of Online Food Delivery Services: A Greek Case. Preprints 2024, 2024041203. https://doi.org/10.20944/preprints202404.1203.v1
Fragkos, N.; Liapakis, A.; Ntaliani, M.; Ntalianis, F.; Costopoulou, C. A Sentiment Analysis Approach for Exploring CustomerReviews of Online Food Delivery Services: A Greek Case. Preprints2024, 2024041203. https://doi.org/10.20944/preprints202404.1203.v1
APA Style
Fragkos, N., Liapakis, A., Ntaliani, M., Ntalianis, F., & Costopoulou, C. (2024). A Sentiment Analysis Approach for Exploring CustomerReviews of Online Food Delivery Services: A Greek Case. Preprints. https://doi.org/10.20944/preprints202404.1203.v1
Chicago/Turabian Style
Fragkos, N., Filotheos Ntalianis and Constantina Costopoulou. 2024 "A Sentiment Analysis Approach for Exploring CustomerReviews of Online Food Delivery Services: A Greek Case" Preprints. https://doi.org/10.20944/preprints202404.1203.v1
Abstract
The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of the study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, there is a significant need for the creation of industry-specific lexical resources that provide decision-makers with valuable insights.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
