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Background
Sentiment analysis becomes ubiquitous for a variety of applications used in marketing, commerce, and public sector. This has been raising a natural interest within the academic research and industry to develop approaches and solutions for ubiquitous sentiment analysis. However, we can observe that most of the academic research focuses on...
Citations
... Chen et al. (2023) use machine learning methods to analyse texts and identify financial crises. Other approaches include regression and ranking methods (Tsai & Wang, 2017), lexicon and sentiment analysis (Meyer et al., 2017), and Rule-Based Emission Model algorithm (Tromp et al., 2017). ...
This paper introduces statistical models Wordscores and Wordfish to study and predict banking crises. While Wordscores is akin to supervised learning, Wordfish is analogous to unsupervised learning. Both methods estimate the position of banking distress on a tranquil-to-crisis spectrum. Findings suggest that the two statistical methods signal banking crisis up to two-years in advance, with robust results from AUROC, Granger causality and VAR impulse responses. Both methods outperform random forests in predicting crises using textual data. The Wordscores index highlights increased usage of banking sector nomenclature two years preceding a crisis, and Granger causes a crisis series with one and two lag lengths. Results from the Wordfish technique, a statistical model with Poisson distribution, show the index spikes before and during the Global Financial Crisis, when a large share of the countries in the world encountered banking crises. This paper contributes to literature on text-based models of banking crises by bolstering the preemptive policy responses available to policy makers. Given their early warning signals, both Wordscores and Wordfish can be considered a part of the toolset to monitor the stability and resilience of the banking sector.
... Even though natural language processing is a powerful tool (Rajalakshmi et al. 2017) in big data analysis, the classification of sentiment value is complicated. In addition, more researches have been done opinion mining in various levels to extract the text like naïve Bayes (Wang et al. 2014), mapreduce (Tromp et al. 2017), entropy-weighted genetic algorithm (EWGA) (Schintler and Kulkarni 2014), benchmarking on datasets (Liu and Zhang 2012), Rule-Based Emission Model (RBEM) deep learning (Edgcomb and Zima 2019), Emotion tracker (Kim et al. 2019), etc., still the selection of suitable word in large dataset is impossible. To end this issue, the current research work aimed to develop a machine learning model with a heuristic approach. ...
Nowadays, the big data is ruling the entire digital world with its applications and facilities. Thus, to run the online services in better way, some of the machine learning models are utilized, also the machine learning strategy is became a trending field in big data; hence the success of online services or business is based upon the customer reviews. Almost the review contains neutral, positive, and negative sentiment value; Manual classification of sentiment value is a difficult task so that the natural language processing (NLP) scheme is used which is processed using machine learning strategy. Moreover, the part of speech specification for different languages is difficult. To overcome this issue, the current research aims to develop a novel less error pruning-shortest description length (LEP-SDL) for error pruning and ant lion boosting model (ALBM) for opinion specification purpose. Here, the Telugu news review dataset adopted to process the sentiment analysis in NLP. Furthermore, the fitness function of ant lion model in boosting approach improves the accuracy and precision of opinion specification also makes the classification process easier. Thus, to evaluate the competence of the projected model, it is evaluated with recent existing works in terms of accuracy, precision, etc., and achieved better results by obtaining high accuracy and precision of opinion specification.
... Therefore, experienced data analysts must be careful while they take decisions on the basis of the data. Variability: Tromp, Pechenizkiy & Gaber, (2017) has argued that variability can be confused with variety; variability means the meaning of the word can vary according to context. So many organizations are now concentrating on customer sentiment analysis through analyzing social media data such as Facebook and Twitter. ...
... The sentiment analysis methods are classic machine learning algorithms, including regression and ranking methods (Tsai & Wang, 2017, lexicon and machine learning sentiment analysis (Meyer et al., 2017), Rule-Based Emission Model algorithm (Tromp et al., 2017), fractions of positive and negative words (García, 2013) and so on. ...
Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.
... The business has at long last achieved the point where business insight calculations for expansive scale information preparing, already reasonable just too extensive organizations, have progressed toward becoming commoditized. Using promptly accessible systems, for example, Apache Hadoop and cheap equipment, sellers are currently ready to construct enormous information answers for gathering, putting away and breaking down tremendous measures of unstructured information continuously (Tromp, Pechenizkiy & Gaber, 2017). ...
The motivation behind this chapter is to highlight the qualities, security issue, advantages, and disadvantages of big data. In the recent researches, the issue and challenges are due to the exponential growth of social media data and other images and videos. Big data security threats are rising, which is affecting the data heterogeneity adaptability and privacy preservation analytics. Big data analytics helps cyber security, but no new application can be envisioned without delivering new types of information, working on data-driven calculations and expending determined measure of information. This chapter demonstrates how innate attributes of big data are protected.
The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. The related applications fields, challenges, and most importantly the openings for future research, are detailed.
A large vault of terabytes of information created every day from present-day data frameworks and digital innovations, for example, the internet of things and distributed computing. Investigation of this enormous information requires a ton of endeavors at different dimensions to separate learning for central leadership. An examination is an ebb-and-flow territory of innovative work. The fundamental goal of this paper is to investigate the potential effect of enormous information challenges, open research issues, and different instruments related to it. Subsequently, this article gives a stage to study big data at various stages. It opens another skyline for analysts to build up the arrangement in light of the difficulties, and open research issues. The article comprehended that each large information stage has its core interest. Some of this is intended for bunch handling while some are great at constant scientific. Each large information stage likewise has explicit usefulness. Unique procedures were utilized for the investigation.
The period of vast information and examination has arrived and is changing the world significantly. The field of information frameworks ought to be at the bleeding edge of comprehension and deciphering the effect of the two innovations and administration to lead the endeavors of business to inquire about in the information period. In this chapter, the author investigates administrative issues of business change coming about because of the original appropriation and inventive uses of information sciences in business. The author ends by giving an analysis of big data that covers all the analytical processes and future research headings.
Sentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity. Moreover, big data is produced via mobile networks and social media. Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment. Accordingly, performing sentiment analysis on big data can be helpful for a business to take useful commercial insights from text‐oriented content. However, there are very few comprehensive investigations and profound argument in this context. The goal of this paper is to provide a comprehensive and systematic investigation of the state‐of‐the‐art techniques and highlight the directions for future research. In this paper, we used systematic literature review method and in the first step, we obtained 15 351 articles; then, based on different filters, 48 related articles were attained. We have selected 23 articles based on the year of publication, the relevance of the journal, the completeness of the text, the nonrepeatability of the title, and the page number. Also, we have categorized big data and sentiment analysis into two classifications: centralized and desterilized platforms. Furthermore, the disadvantages and advantages of the investigated techniques are studied and their key issues are emphasized. Consequently, this study shows that a better analysis of textual big data in terms of sentiment increases efficiency, flexibility, and intelligence. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better handling of big data in the field of sentiment analysis. This study sheds some new light on using sentiment analysis and big data for public opinion estimation and prediction.
