Publications

2022

1. MEX

   Analyzing tourism reviews using an LDA topic-based sentiment analysis approach

   Twil Ali, Bencharef Omar, and Kaloun Soulaimane

   MethodsX, 2022

   Bib HTML PDF

   @article{ali2022analyzing,
     title = {Analyzing tourism reviews using an LDA topic-based sentiment analysis approach},
     author = {Ali, Twil and Omar, Bencharef and Soulaimane, Kaloun},
     journal = {MethodsX},
     pages = {101894},
     year = {2022},
     publisher = {Elsevier},
   }

2021

1. TMP

   Exploring destination’s negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on TripAdvisor

   Twil Ali, Bidan Marc, Bencharef Omar, and 2 more authors

   Tourism Management Perspectives, 2021

   Abs DOI Bib

   Tourism is entering a phase of intellectual changes characterized by consumer power’s dynamism through social media. The immense amount of internet knowledge has become the number one means of communication globally through emails and social networks. Any Internet user can freely express themselves and give their views and feelings on any subject, including companies. The current research aims to provide a new technique by combining topic modeling and lexicon-based algorithms to gain insights about Marrakech’s e-reputation, enhancing the tourism experience in this city. This study explores about 39,216 TripAdvisor reviews from different locations and attractions in Marrakech, Morocco. This approach is mainly based on Latent Dirichlet Allocation (LDA), to extract hidden aspects and dimensions from the feedbacks of tourists who visited Marrakech, search for some latent patterns inside the reviews and conduct Lexicon-based sentiment analysis to pinpoint the weaknesses directly and develop the touristic experience in this city.

   @article{ALI2021100892,
     title = {Exploring destination's negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on TripAdvisor},
     journal = {Tourism Management Perspectives},
     volume = {40},
     pages = {100892},
     year = {2021},
     issn = {2211-9736},
     doi = {https://doi.org/10.1016/j.tmp.2021.100892},
     url = {https://www.sciencedirect.com/science/article/pii/S2211973621001057},
     author = {Ali, Twil and Marc, Bidan and Omar, Bencharef and Soulaimane, Kaloun and Larbi, Safaa},
     keywords = {E-reputation, TripAdvisor, Tourism, Latent Dirichlet Allocation, Topic modeling, Sentiment analysis, Marrakech, Morocco}
   }

2020

1. JTUST

   HOW CAN WE ANALYSE EMOTIONS ON TWITTER DURING AN EPIDEMIC SITUATION? A FEATURES ENGINEERING APPROACH TO EVALUATE PEOPLE’S EMOTIONS DURING THE COVID-19 PANDEMIC.

   Ali Twil, Oussama Stitini, Soulaimane Kaloun, and 1 more author

   Journal of Tianjin University of Science and Technology, 2020

   Abs DOI Bib PDF

   The Coronavirus (COVID 19) pandemic has changed the way we live. Today, we live in a revolution in which the way we communicate and interact with others has forever changed. The interpretation of the COVID-19 awareness crisis and the assessment of public feelings expressed via social media under COVID-19 has become a critical task. In this research paper, using Coronavirus related Tweets, we classify public emotions associated with the pandemic. We may get an idea of how a person feels about this pandemic by examining the feelings of these tweets. For that, we give a methodological overview, the first of which concerns the approach to machine learning using traditional feature extraction with a 64% low classification accuracy, the second approach uses feature engineering to boost accuracy. Detecting emotion during an epidemic situation is an emerging research area generating interest, but which presents particular challenges due to the limited amount of resources available. In this article, we propose an emotion detection model that uses machine learning algorithms, especially feature engineering to classify the content of a tweet as joy, fear, anger or sadness. We first try to apply machine learning algorithms using traditional feature extraction and then we try to propose a feature engineering approach in order to boost and construct higher-accuracy. Our system’s primary goal is to figure out how the pandemic has changed people’s actions and interpret the emotions expressed through Twitter from the beginning of the pandemic.

   @article{stitinican,
     title = {HOW CAN WE ANALYSE EMOTIONS ON TWITTER DURING AN EPIDEMIC SITUATION? A FEATURES ENGINEERING APPROACH TO EVALUATE PEOPLE’S EMOTIONS DURING THE COVID-19 PANDEMIC.},
     author = {Twil, Ali and Stitini, Oussama and Kaloun, Soulaimane and Bencharef, Omar},
     year = {2020},
     journal = {Journal of Tianjin University of Science and Technology},
     doi = {10.17605/OSF.IO/U9H52},
   }