Spatial Big Data and Moving Objects: A Comprehensive Survey <s> FIGURE 1. <s> This chapter outlines how one might utilize the massive amounts of web-based, geographically-referenced digital social data for geographical research. Because much of these data are user-generated and produced through social media platforms, we also focus on the pitfalls associated with such sources and the benefits of a mixed methods approach to these data. Not only can digital social data be mapped for visual analysis, it is also useful to use a range of quantitative methods to understand relationships between different subsets of the data. In addition, closer, systematic readings via qualitative methods of social data provides insights of particular people’s perceptions and experiences of the world around them. Thus, while making maps is often the starting point for geographers working with this kind of research, it is rarely the end point. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> FIGURE 1. <s> Abstract Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> FIGURE 1. <s> The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. <s> BIB003
A use case study of people's weekday activity patterns. Study is conducted for a period of three months by tracking (through GPS) the continuous spatial movements of people from one activity space to another  . supplemented by the upsurge of spatial technologies. This has provided new mapping mechanisms where users are involved in online activities in real time. The coupling of users social media and location-based information generates tons of crowdsourced data that can be used in many contexts. However, this unstructured data requires advanced data mining techniques to draw meaningful insights. Social media data together with geotagging, known as geosocial data enables content creation by users and is publicly accessible. Thus, social media is one of the main sources of SBD generation. This is mainly because of the exponential growth of mobile usage thus making it possible to measure VOLUME 6, 2018 and analyze different types of social, economic, and political activities BIB001 . BD generated via social media is also known as 'social BD'. This social BD is defined in BIB003 as ''those processes and methods that are designed to provide sensitive and relevant knowledge to any user or company from social media data sources when data sources can be characterized by their different formats and contents, their very large size, and the online or streamed generation of information''. One of the features of data generated through social media applications is that it is unstructured in nature. Due to the various types of social media generated data, the analytics performed on social BD vary as well. Text analytics are used for social media data collected from organizational proprietary web pages as well as pages hosted on various third party social media websites. Social media content helps firms in understanding the opinions of consumers and general public related to social events, political movements, company strategies, marketing campaigns, and product preferences  . In order to better understand these opinions, sentiment analysis (SA) techniques are used in literature. SA is a computation study of opinions, sentiments, emotions, and attitudes expressed in text towards an entity BIB002 . Due to the ever increasing trend in e-commerce, online reviews and product evaluations are becoming common, and therefore, producers and service providers take into consideration the analysis of these customer opinions  . However, the increasing use of mobile devices coupled with social media data has provided organizations an access to much more in-depth data which is even more useful. This is usually referred as 'mobile analytics' or 'mobile business intelligence'. According to Chen et al.  , mobile data generation and analytics have a wide range of applications such as location-based mobile sensing apps that are activity-sensitive, disaster management, m-health, m-learning, mobile social networking, crowd-sourcing, gamification, mobile advertising, and social marketing. Some of these applications are discussed in detail in the following subsections.