End of studies' Thesis: Sentiment analysis on customer reviews of the Brandt brand taking into account credibility
This repository contains the thesis conducted as part my end of studies project at ESI-Algiers in order to obtain my diploma as a state computer science engineer. It deals with the topic of sentiment analysis and opinion spam detection, with a proof of concept designed for reputation analysis of my case study : the Brandt brand. It is a continuation of my master thesis.
- Title: Analyse des Sentiments sur les avis des clients de la marque Brandt avec prise en compte de la crédibilité
- Author: Wassila Maria Slimani
- University: Ecole nationale Supérieure d’Informatique ESI, Oued Smar Alger, Algérie
- Year: 2023
In today's world, businesses are often concerned about their online identity because they are aware that their reputation can be affected or even destroyed by a single negative review. This is why they try to analyze and understand online reviews along two main dimensions: (i) to assess their reputation, and (ii) to use them in estimating customer satisfaction and improving their overall customer experience. However, not all reviews can be considered trustworthy. Indeed, spammers, whether they are individuals or businesses, deliberately write negative or positive reviews to harm or promote a brand. Building on a previous research project focused on analyzing the credibility of opinions, we continue our investigation by examining reputation through a case study of the Brandt company. To do this, we have conducted a literature review of various approaches for detecting the credibility of reviews. From this, we have identified the challenges we need to address in analyzing e-reputation for Brandt. We have explored several models for sentiment analysis and credibility assessment, taking into account language-related challenges. Subsequently, we have proposed a framework for e-reputation analysis that encompasses the entire analysis process, from data collection and language identification to polarity and credibility prediction, as well as the calculation of e-reputation indicators. This framework also facilitates regular model evaluation to update e-reputation indicators, including an overview of review authenticity, the correlation between spam and polarity, and more. Our most effective models achieve an accuracy of 98% for language identification, 78% for multilingual sentiment analysis, and 66% for multilingual spam detection.
This thesis is licensed under the Creative Commons Attribution 4.0 International License.
If you use information from this thesis in your work, please cite it using the following BibTeX entry:
@thesis{MasterSlimani2023,
author = {Wassila Maria Slimani},
title = {Analyse des Sentiments sur les avis des clients de la marque Brandt avec prise en compte de la crédibilité},
school = {Ecole nationale Supérieure d’Informatique ESI, Oued Smar Alger, Algérie},
year = {2023},
howpublished = {https://mariaslimani.github.io/PFE_Thesis/}
}If you have any questions, feedback, or potential collaboration opportunities related to this thesis, please feel free to contact me on my email address.