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Building a New Tourism Sentiment Lexicon Containing Descriptive Words in Modern Standard and Colloquial Arabic
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 53 |
| Year of Publication: 2022 |
| Authors: Mohammed Alkoli, B. Sharada, Sami A.M. Alquhali |
10.5120/ijca2022921951
|
Mohammed Alkoli, B. Sharada, Sami A.M. Alquhali . Building a New Tourism Sentiment Lexicon Containing Descriptive Words in Modern Standard and Colloquial Arabic. International Journal of Computer Applications. 183, 53 ( Feb 2022), 29-31. DOI=10.5120/ijca2022921951
Abstract
In tourism industry, sentiment analysis is emerging as a technology that can be used to assess the sentiments of the tourists based on their responses on different social media sites or platforms. Sentiment analysis is an important and helpful technique for decision makers to evaluate services and identify problems and deficiencies. Too many studies have been done on this field in other languages, but in Arabic the number of studies is limited. In addition, such studies on Arabic examine each dialect of Arabic separately and no single study includes sentiment analysis examination of a group or some varieties of Arabic dialects along with Modern Standard Arabic (MSA). The previous studies also do not address the different Arabic dialects, therefore the researches here think there should be a study that includes sentiment analysis of a number of Arabic dialects along with Modern Standard Arabic, the current research paper is an example, as most Arabs (Arab people) express their opinions in their dialects of Arabic and few use Modern Standard Arabic. The main goal of this research paper is to build a new sentiment analysis lexicon based on the opinions of Arab tourists visiting India. This lexicon includes lexemes (words or vocabularies) taken of three Arabic dialects (namely: Gulf, Levantine and Egyptian dialects of Arabic) along with Modern Standard Arabic. The lexicon will be also evaluated by comparing it to the existing one, namely SemEval2016, using a machine learning technique called Support Vector Classifier for obtaining better results. Thus, building a new dictionary will be effective in sentiment analysis in modern Arabic and most Arabic dialects.
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