Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion Mining

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

We introduce Big Data Analytics (BDA) and Sentiment Analysis (SA) to the study of international negotiations, through an application to the case of the UK-EU Brexit negotiations and the use of Twitter user sentiment. We show that SA of tweets has potential as a real-time barometer of public sentiment towards negotiating outcomes to inform government decision-making. Despite the increasing need for information on collective preferences regarding possible negotiating outcomes, negotiators have been slow to capitalise on BDA. Through SA on a corpus of 13,018,367 tweets on defined Brexit hashtags, we illustrate how SA can provide a platform for decision-makers engaged in international negotiations to grasp collective preferences. We show that BDA and SA can enhance decision-making and strategy in public policy and negotiation contexts of the magnitude of Brexit. Our findings indicate that the preferred or least preferred Brexit outcomes could have been inferred by the emotions expressed by Twitter users. We argue that BDA can be a mechanism to map the different options available to decision-makers and bring insights to and inform their decision-making. Our work, thereby, proposes SA as part of the international negotiation toolbox to remedy for the existing informational gap between decision makers and citizens’ preferred outcomes.

Addressing the policy challenges and opportunities of “Big data”

While much research work has been devoted to supply chain management and demand forecast, research on designing big data analytics methodologies to enhance sales forecasting is seldom reported in existing literature. The big data of consumer‐contributed product comments on online social media provide management with unprecedented opportunities to leverage collective consumer intelligence for enhancing supply chain management in general and sales forecasting in particular. The main contributions of our work presented in this study are as follows: (1) the design of a novel big data analytics methodology that is underpinned by a parallel aspect‐oriented sentiment analysis algorithm for mining consumer intelligence from a huge number of online product comments; (2) the design and the large‐scale empirical test of a sentiment enhanced sales forecasting method that is empowered by a parallel co‐evolutionary extreme learning machine. Based on real‐world big datasets, our experimental results confirm that consumer sentiments mined from big data can improve the accuracy of sales forecasting across predictive models and datasets. The managerial implication of our work is that firms can apply the proposed big data analytics methodology to enhance sales forecasting performance. Thereby, the problem of under/over‐stocking is alleviated and customer satisfaction is improved.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.

The rapid advancements in Artificial Intelligence (AI) and Big Data Analytics have significantly transformed financial markets, enabling more precise, data-driven, and strategic decision-making. This study explores the profound impact of AI-driven analytics in financial decision-making, focusing on how machine learning algorithms, predictive analytics, and automation enhance market efficiency, risk assessment, and investment strategies. The research highlights how AI-powered Big Data Analytics processes vast amounts of structured and unstructured financial data to identify patterns, trends, and anomalies that influence market movements. It further examines how financial institutions leverage AI for fraud detection, sentiment analysis, and algorithmic trading, leading to more informed and efficient investment decisions. Through a survey-based approach, this study collects data from financial professionals, analysts, and investors to evaluate their perspectives on AI’s role in improving data accuracy, reducing uncertainty, and optimizing financial strategies. The findings provide valuable insights into how AI-driven analytics contributes to minimizing risks, enhancing forecasting accuracy, and supporting regulatory compliance in financial markets. The study concludes that the integration of AI in Big Data Analytics is revolutionizing financial decision-making by improving efficiency, reducing human biases, and enabling real-time strategic actions. However, challenges such as data privacy concerns, ethical considerations, and the need for advanced AI governance frameworks remain crucial areas for future exploration. Keywords—Artificial Intelligence, Big Data Analytics, Financial Markets, Strategic Decision-Making, Algorithmic Trading, Risk Assessment, Predictive Analytics.

With the rapid growth of web technology and easy access of internet, online shopping has been increased. Now people express their opinions and share their experiences that greatly influence new buyers for purchasing products, thereby generating large data sets. This large data is very helpful for analyzing customer preference, needs and its behavior toward a product. Companies face the challenge of analyzing this sheer amount of data to extract customer opinion. To address this challenge, in this paper, we performed sentiment analysis on the customer review real-world data at phrase level to find out customer preference by analyzing subjective expressions. Then we calculated the strength of sentiment word to find out the intensity of each expression and applied clustering for placing the words in various clusters based on their intensity. We also compared the results of our technique with star-ranking given on the same dataset and found the drastic change in our results. We also provide a visual representation of our results to provide a clear insight of customer preference and behavior to help decision makers for better decision making.

This chapter provides a brief overview of the growing role played by big data, data analytics, and artificial intelligence (AI) in the accounting profession. The term 'Big Data' along with other trending topics such as 'Data Analytics' and 'Artificial Intelligence (AI)' have become buzzwords in the accounting profession in recent years. The skills of accounting professionals have evolved as technology has made rapid advances from using pencil and paper to typewriters and calculators, and eventually spreadsheets and accounting software. Data analytics in accounting is a relatively new skill set that is growing significantly in all areas of the accounting profession. Accounting professionals who can discern patterns and trends in big data and translate them into compelling strategic narratives will find themselves at the center of the twenty-first-century business world. Therefore, accounting professionals can capitalize on numerous opportunities in this rapidly evolving, disruptive, but ultimately advantageous environment by embracing big data, data analytics, and artificial intelligence (AI) to stay ahead of the competition.

An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.

After a disaster such as earthquakes, debris flows, forest fires, or landslides, etc., a lot of people have to be away from their home and gather in a shelter. In addition, the refugees suffer from the shortage of necessary resources due to impaired life infrastructure, such as damaged roads and communication networks. The degree of reducing damage depends on the amount of food, water, daily necessities and communication resources required by each shelter. How to effectively and efficiently allocate resources according to grasp the exact need of a disaster situation will be an important issue. We estimate the degree of the disaster by collecting and analyzing big data from the SNS, and building a platform for the communication resources to be efficiently and effectively allocated. In order to achieve this goal, we are challenging the following issues A) Understanding situations (user requirements) after disaster occur The SNS streams large scale semantic information about real time situation in society, especially during and after disaster. It is both domain-specific and computational challenge in processing the heterogeneous large data set to extract the exact situational content with reduced semantic uncertainty. The machine learning (ML) and natural language processing (NPL) tool kits are useful in semantic analysis, but still needs domain-specific implementation and computational improvement for the situation understanding from the SNS big data. B) Understanding distribution patterns of situations/users' requirements The disaster related situation is spatiotemporally correlated, and varies dynamically in space and time. It is also domain-specific and computational challenge in estimating the spatiotemporal distribution patterns of the disaster affect based on the spatial big data from SNS. The scan statistics such as the spatial scan have provided well tested mathematical tools and software for spatial data mining. However, new methodologies are necessary since the assumptions have to be different when it meets the spatial big data in SNS. And the computational complexity in spatial big data is also a bottleneck for real-time processing. C) Solving uncertainty of big crowd data One of the major features in big crowd data, e.g., SNS data, is uncertainty behind the data. Especially in a disaster scenario, the collecting time period cannot be long enough to smooth the data automatically. How to efficiently solve uncertainty problem in the big crowd data in a disaster scenario becomes a new and big challenge for disaster management.

The COVID-19 pandemic has emerged as one of the most crucial health emergencies in the last decade where almost all entities of a nation’s ecosystem like inhabitants, businesses, governments, economies, and environment are impacted. The large volumes of epidemiological, clinical, personal, and environmental data generated during any pandemic can provide useful insights about the underlying causes, symptoms, relations, and correlations, which if analyzed can assist in mitigating the impact to a great extent. The cheap and easy connectivity and communication provided by the social media platforms (SMP) have established them as one of the most preferred mediums of communications among the masses. The data generated by these platforms can be analyzed in context of the ongoing COVID-19 crisis to provide critical information and insights related to the ground level realities like spread and severity of infection, the state of implementation of control measures, the mental state of individuals, and their needs. The tweets and comments of the users can provide information about the current situation and intensity of the problems in the affected regions. With the help of techniques like sentiment analysis and web mining, we can identify the emergent requirements and needs (like food, shelter, medicine, medical emergencies, security, etc.) of the population in the COVID-19-affected areas. With this chapter we aim to identify several use cases where the big medical data from the patients, epidemiological data, social media data, and environment-related data can be used to identify patterns, causes, and other growing factors of the COVID-19 pandemic with a goal to mitigate the damages and contain further spread of the disease. The chapter also discusses the impact of a preferred mitigation measure of nationwide lockdown on the number of new novel coronavirus-positive patients as well as the impact on the environment by analyzing the available data. Since the tourism industry is now of the worst hit businesses, we also discussed the impact of COVID-19 on tourism industry. Furthermore, we identify the challenges associated with handling the massive amount of data generated during such pandemics. Finally, the future avenues of using big data for effectively devising predictive mechanisms and techniques to contain such pandemics in the initial stages are discussed. The chapter also discusses the importance of edge/fog technologies and IoT to identify possible use cases and where immediate point of contact actions is needed to mitigate the situations. Since edge computing facilitates calculations near the origin of data, it is imperative to understand the potential use cases in times of COVID-19-like pandemics.