Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress.
Abstract-Today, the number of users of social network is increasing. Millions of users share opinions on different aspects of life every day. Therefore social network are rich sources of data for opinion mining and sentiment analysis. Also users have become more interested in following news pages on Facebook. Several posts; political for example, have thousands of users' comments that agree/disagree with the post content. Such comments can be a good indicator for the community opinion about the post content. For politicians, marketers, decision makers …, it is required to make sentiment analysis to know the percentage of users agree, disagree and neutral respect to a post. This raised the need to analyze theusers' comments in Facebook. We focused on Arabic Facebook news pages for the task of sentiment analysis. We developed a corpus for sentiment analysis and opinion mining purposes. Then, we used different machine learning algorithms -decision tree, support vector machines, and naive bayes -to develop sentiment analyzer. The performance of the system using each technique was evaluated and compared with others.
Today, the number of users of social network increases and a lot of users share opinions on different aspects of life every day. So the rate of colloquial written text increases dramatically as a medium of expressing ideas especially across the WWW. Therefore, social networks are rich sources of data for opinion mining and sentiment analysis. Arab colloquial dialects are languages that people used to communicate with each other in social networks. Recently, there is a massive amount of Arab colloquial data on Social networks. By increasing the available data, the needing for processing this data and using it is increased. However, most available tools and resources (morphological analyzers, disambiguation systems, annotated data, and parallel corpora) are for Modern Standard Arabic (MSA). Therefore, the need for the automatic transformation from Arab colloquial dialects to Modern Standard Arabic becomes urgent to use Modern Standard Arabic tools and resources for Arab colloquial dialects. The most famous colloquial is Egyptian colloquial dialect, which is considered the most widely used and understood dialect throughout the Arab world. Consequently, the focus of the proposed system is the Egyptian colloquial dialect to prove our approach.
Outdoor navigation remains a challenging activity for People with Visual Impairments (PVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PVI undertake a journey in an outdoor environment and what are their main challenges and obstacles. This is a critical step towards developing robust solutions that meet the requirements of this user group. We undertook a questionnaire-based study with the National Council for the Blind of Ireland (NCBI) and 49 PVI. According to the feedback from our questionnaire, current journey navigation apps do not provide PVI with sufficient information about traffic lights, crossroads, and physical obstacles to support a satisfactory interaction. Our study reveals key aspects of how PVI interact with outdoor navigation applications. Critical gaps exist, for example, over 63% of respondents indicated they had suffered an injury on at least one previous occasion when navigating outdoors. Based on the questionnaire feedback, we present a solution covering the main aspects of outdoor navigation for PVI. Our work aims to contribute to the improvement of outdoor navigation applications for PVI in the future.
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