A Sentiment Analysis of Egypt’s New Real Estate Registration Law on Facebook

Abstract

In response to the increasing influence of social media networks on shaping the public opinion, sentiment analysis systems and applications have been developed to extract insights and gain an overview of the wider public opinion behind certain topics so as to support businesses, manufacturers, government agencies, and policymakers with their decisions and plans. Despite the importance of sentiment analysis in providing policymakers with effective mechanisms to understand the attitudes of customers and citizens which can be usefully used in decision-making processes and planning for the future, so far studies on sentiment analysis are very limited in Egypt. Much of the work is still done using survey tools such as questionnaires and polls to gather information about the citizens' attitudes towards given issues and topics. Despite the effectiveness of such methods, citizens' reflections on social media platforms and networks remain more powerful in providing comprehensive insights and overviews. Furthermore, social media-based sentiment analysis is usually more representative being based on larger numbers of participants, which has positive implications to reliability. Opinions expressed on social media are often the most powerful forms of feedback for businesses because they are given unsolicited. In light of this argument, this study seeks to provide a sentiment analysis of Egypt's New Real Estate Registration Law on Facebook. To extract information about the users' sentiment polarity (positive, neutral or negative), Facebook posts were used. The rationale is that Facebook is still the most popular social media platform in Egypt. Text classification was then used for classifying the selected data into three main classes/values: Positive, Negative, and Neutral. The findings indicate that sentiments expressed in the users' posts and comments have a significant negative attitude towards the new law. Despite the effectiveness of the automatic evaluation and analysis of the sentiments and opinions of the users in social media concerning the new Real Estate Registration Law, linguistic approaches including Critical Discourse Analysis (CDA), functional linguistics, and semiotics need to be incorporated into sentiment analysis applications for gaining a better understanding of people's attitudes towards specific issues.

Public Opinion Mining on Construction Health and Safety: Latent Dirichlet Allocation Approach

“…Sentiment analysis and applications for social media have been developed by academia to extract insights and understand public opinions on specific topics to support businesses, manufacturers, government agencies, and policymakers' decision-making and planning [32]. Social media-based sentiment analysis is generally more representative because it is based on many participants' opinions, which can enhance reliability [32].…”

“…Sentiment analysis and applications for social media have been developed by academia to extract insights and understand public opinions on specific topics to support businesses, manufacturers, government agencies, and policymakers' decision-making and planning [32]. Social media-based sentiment analysis is generally more representative because it is based on many participants' opinions, which can enhance reliability [32]. However, sentiment analysis is vital for providing policymakers with adequate mechanisms for understanding customer and citizen attitudes that can be used in decision-making processes and future planning [33].…”

“…On the other hand, most users are organizations or companies that share construction health and safety training or products for the public that are beneficial for workers. Some scholars indicated that sentiment analysis on social media understands broader public opinion behind specific topics to support decision-making by businesses, manufacturers, government agencies, and policymakers and planning [32]. Thus, this result can be beneficial for companies to decide how to share the construction health and safety knowledge products, etc., on social media, especially on Instagram.…”

“…Therefore, construction companies can reduce the public's negative perception of construction health and safety awareness through construction safety training. Sentiment analysis and applications for social media can support businesses, manufacturers, government agencies, and policymakers' decisionmaking and planning [32]. Thus, companies have adopted Instagram to share some construction health and safety knowledge, products, etc.…”

Discourse-based Opinion Mining of Customer Responses to Telecommunications Services in Saudi Arabia during the COVID-19 Crisis

“…Despite the popularity of these methodologies and models, one major problem remains. Put simply, they do not consider the context of the texts because this adds to the complexity of the models and increases the time and cost of processing [41]. This study addresses the gap in the literature by proposing an integrated opinion mining model that takes into account the contextual aspects and features of the texts under investigation.…”