Abstract
In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.
Highlights
Nowadays, online websites are suffering from lag in marketing their products due to lack of effective systems that analyze and trace customer assessments to their services; so some companies remain unknown despite their good quality of services 1
They are: singleword and double-word. It consists of 532 singlewords (215 positive and 317 negative) and 419 double-words (233 positive and 186 negative). 4- Sentiment Analysis Stage: in this stage, three different types of machine learning algorithms are implemented in the sentiment analysis steps which are Rough Set Theory (RST), Naïve Bayes (NB) and K-Nearest Neighbors (KNN)
The dataset are collected from 71 Iraqi tourism companies in the Facebook, Table 2 shows the number of dataset details
Summary
Online websites are suffering from lag in marketing their products due to lack of effective systems that analyze and trace customer assessments to their services; so some companies remain unknown despite their good quality of services 1. A sentiment analysis model for Tourist Company is suggested on Iraqi dialect Facebook posts to assess tourism and travel companies based on extracting sentiments from customer's comments on the social networking sites of these companies to discover useful decisions. 4- In (10), in this paper, a comparative study is applied between using Support Vector Machine (SVM), Naïve Bayesian (NB) and Multilayer Perceptron Neural Network (MLP-NN) classification methods on Arabic data sets which are Aljazeera news web site Saudi Press, Agency (SPA) and Alhayat. From these studies, it was identified that an Arabic language and Iraqi dialect based on sentiment analysis for tourism companies is not considered and it was proposed to develop the solution to the research problem
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