Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era

Sentiment refers to emotions. Sentiment analysis, often known as opinion mining, is the technique of identifying and extracting subjective data from pre-web and post-web reviews using text analytics, computational linguistics, and natural language processing. Hindi is an Indian language which is used by many of Indians. Due to phenomenal growth of online product reviews in Hindi post-web Hindi reviews are also increasing rapidly. A machine learning based method in this paper to analysis postweb text data. The present method is divided into four steps. First of all, an annotated Hindi review data set is developed from post-web sources. In second step, feature extraction is performed on annotated Hindi review dataset using the Term-Frequency/ Inverse-Document Frequency (TF-IDF) and count vectorization techniques. In the third step, the retrieved features are given to the classifier so it can make predictions. Moreover, annotated dataset translated into English. Second step and third step are performed on annotated English dataset in last step. A range of evaluation criteria, including precision, recall, and F1- score, are presented in the results. In both instances, the support vector machine produced the most pertinent results.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

This systematic literature review explores the impact of Natural Language Processing (NLP) in developing Business Intelligence (BI) systems focusing on the rise of Conversational Business Intelligence (CBI). It seeks to determine how NLP can improve user accessibility, decision making, and options available in navigating integration concerns in BI frameworks. Using the PRISMA 2020 guidelines, the review examined 18 peer-reviewed studies presented in the period between 2019 and 2024 through the Google Scholar and the Saudi Digital Library. Inclusion criteria based on pre-set criteria of NLP’s utilization in BI were applied to studies, and for their quality – methodological rigor and relevance, were considered. Findings had to be thematically grouped to handle issues of user accessibility, decision consequences and technical issues. NLP obviously increases BI accessibility with conversational interfaces that empower non-technical users, up to 30% more adoption rates in self-service systems. It enhances decision making using advanced analytics; sentiment analysis (85% accuracy) and predictive modeling (>95% accuracy) enable real time insights. However, scalability limitation, computational requirement and ethical issues such as bias and privacy call for strong solutions for CBI’s effective deployment. NLP integration of BI systems creates transformative value in terms of organizational data application, but facing technical and ethical challenges, adoption is not an easy task. In future research, building of scalable architectures, domain-specific NLP applications and use of ethical frameworks should be considered for CBI systems to be accessible, efficient and trustworthy. These have an implication that calls for interdisciplinary activities in ensuring that technological innovation is matched with practical utility.

In the digital era, Customer Relationship Management (CRM) systems have become central to sustaining competitive advantage through enhanced customer satisfaction and operational efficiency. With the exponential rise in digital interactions, chatbot interfaces powered by Natural Language Processing (NLP) models have emerged as transformative tools in automating and personalizing customer service. This research explores the design, integration, and evaluation of chatbot user interfaces (UIs) tailored for CRM using advanced NLP techniques. The study begins with a comprehensive review of existing literature, highlighting the evolution of chatbots from rule-based systems to intelligent, context-aware virtual assistants. Key NLP components—such as intent recognition, entity extraction, sentiment analysis, and context management—are discussed in the context of enhancing user interaction and personalization. We then present a modular architecture for chatbot-CRM integration, emphasizing user interface design principles that prioritize accessibility, trust, and conversational clarity. A comparative analysis of popular chatbot platforms (Salesforce Einstein Bot, Azure Bot, Rasa, and Dialogflow) is conducted based on criteria such as NLP capabilities, CRM integration, analytics support, and cost-effectiveness. Real-world performance metrics—including customer satisfaction (CSAT), average resolution time, and escalation rates—are analyzed to assess the effectiveness of chatbotdriven CRM solutions. Furthermore, we identify key challenges such as language ambiguity, handling complex queries, and ethical concerns like data privacy and AI bias. The paper concludes with strategic recommendations for hybrid models combining AI and human support, and future research directions including emotion-aware chatbots and multilingual support. This study provides a roadmap for businesses and developers to implement effective, scalable chatbot UIs that optimize customer relationship management using NLP.

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.

Nowadays, financial data on social networks play an important role to predict the stock market. However, the exponential growth of financial information on social networks such as Twitter has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. The Natural Language Processing (NLP) and sentiment analysis areas can solve this problem. In this respect, we propose a supervised machine learning method to detect the polarity of financial tweets. The method employs a set of lexico-morphological and semantic features, which were extracted with UMTextStats tool. Furthermore, we have conducted a comparison of the performance of three classification algorithms (J48, BayesNet, and SMO). The results showed that SMO provides better results than BayesNet and J48 algorithms, obtaining an F-measure of 73.2%.

This study aims to analyse the effect or impact of implementing Customer Relationship Management (CRM) in digital marketing strategies on customer relationships in the digital era. With the development of information and communication technology, companies now face new challenges in managing interactions with customers through various digital platforms. This research uses a descriptive analysis approach through a survey to representatives of the Company's marketing team who implement digital-based CRM with customer satisfaction variables, increasing customer loyalty, improving sales performance and increasing Company profitability. The results show that the implementation of digital-based CRM can positively improve communication efficiency, service personalisation, and more optimal customer data management, which in turn contributes to increased satisfaction, loyalty, performance and profitability. In addition, the findings also reveal that the use of digital marketing tools, such as social media, email marketing, and chatbots, further strengthens companies' relationships with customers. The research suggests that companies leverage digital CRM technology integrated with digital marketing platforms to maximise customer experience and achieve more effective marketing goals.

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

This paper examines empirically the role of business intelligence (BI) in customer relationship management (CRM). Drawing on relevant literature on BI and CRM, the research model for the current investigation proposes that BI approaches of an organisation and its competition influence organisational business strategy which in turn impacts its customer strategy. The model is tested empirically using survey data of 165 respondents from 73 different private and state owned businesses operating in a transitional economy of East Europe. Empirical evidence confirms a key role of BI in CRM through its impact on organisational business and customer strategies development. Such findings make two important contributions. For research, they provide an improved understanding of the factors and processes involved in realising benefits from BI. For practice, they show managers how BI can be leveraged to achieve performance gains through competitive actions. Further research is recommended to confirm and extend the current investigation.

Abstract: This chapter explores the cutting-edge advancements in computational language technology, delving into the transformative impact of recent innovations in the field. It covers the evolution from traditional rule-based and statistical methods to advanced neural network architectures, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers. The chapter highlights key applications in natural language processing (NLP), including speech recognition, machine translation, and sentiment analysis, while addressing the ethical and societal implications of these technologies. Emphasis is placed on emerging research areas, such as multilingual NLP, low-resource language processing, and conversational AI. The chapter concludes by discussing future trends and challenges, including scalability, interpretability, and fairness in NLP systems, providing a comprehensive overview of the current state and future directions of computational linguistics. Keywords: Computational Linguistics,Natural Language Processing (NLP),Neural Networks,Deep Learning,Speech Recognition,Machine Translation,Sentiment Analysis,Multilingual NLP,Low-Resource Languages,Conversational AI,Ethical AI,Data Privacy,Algorithmic Bias and Future Trends in NLP.

Aspect based sentiment analysis (ABSA) is regards as the most demanding area of research in the field of text mining and natural language processing. In the last decade, people tend to be more interested in consulting online media outlets to make decisions concerning products or events. However, the existing ABSA approaches alone are not sufficient in providing comprehensive information required to make an informed decision. Most of the early studies focused on the binary classification of “positive” and “negative” to predict an overall sentiment of a review, which reduced the effectiveness in decision making towards the entities. Even though earlier studies on SA are centered on aspect extraction, there are limited works that effectively combine aspect extraction with emotional affects such as surprise, sadness and happiness, which consequently leads to incomplete meaning of a content. In this paper, we investigate a problem involving aspects in relation to emotional affects. To propose a novel supervised approach for the aspect extraction with their associated emotion classes in sentiment analysis (SA), we conducted an analysis of some prominent ABSA approaches. This research work will lead to a more accurate results of SA and could assist both novice and prominent researchers in comprehending the relationship between aspects and emotions in SA considering its sematic role in business intelligence.

There is currently a trend for companies to invest in business intelligence (BI) systems and integrate them with their original enterprise resource planning (ERP) systems in order to enhance their management decision-making capability. Even though, much attention has been paid to decision-making benefits of integrated systems in practice, limited academic research has investigated the issue of decision-making improvement. Therefore, the motivation for this paper is to examine the impact of integrated ERP and BI (abbreviated as ERPBI) systems on decision making performance (DMP) and introduce a comprehensive measurement instrument for assessing that impact. A survey was conducted involving 108 companies in the semiconductor industry in Taiwan. The results of the survey indicate firstly, that ERPBI system usage in organisations is positively related to DMP. Secondly, organisations using ERPBI systems achieve higher levels of decision-making performance compared to organisations which only use ERP systems.

This study looks at the use of sentiment analysis and opinion mining in business intelligence by organisations to develop and sustain a competitive advantage. It discusses variables such as organisation structure, business intelligence, knowledge management, and opinions mining as some sources of competitive advantage. The approach for this research is based on a positivist paradigm. A survey research strategy was used and a questionnaire was used to collect sample data. The analysis revealed that variables related to knowledge management (KM) and business intelligence (BI) can be used to explain sustainable competitive advantage (CA). The regression model showed that the surrogate variables related to KM and BI can be used to explain CA; while OM was insignificant, taking into account the sample, its size and the context within which the study was conducted. The finding may appear contradictory to literature, implying that OM may not be contributing to sustainable competitive advantage. However, on closer scrutiny, the surrogate variable that represents the dependent construct of sustainable competitive advantage directly ‘speaks’ to the role of OM orientated towards processes that utilize opinions geared towards integration of KM and BI for sustaining competitive advantage. Thus, while the regression model depicted OM as insignificant in CA, the significant surrogate variable used for representing the dependent variable specifically captured the import of OM. OM, while negated as an independent variable, found its way as the ‘glue’ in linking BI and KM to sustainable CA. Key words: Business intelligence, competitive advantage, sentiment analysis, opinion mining, competitive advantage.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

Visualization interfaces that offer multiple coordinated views on a particular set of data items are useful for navigating and exploring complex information spaces. In application areas like customer relationship management (CRM) or business intelligence (BI), for example, structured customer or company data is often associated to text-based information like telemarketing transcripts or trade press articles. Supporting the analysis of such text information and related structured data is a major challenge for current CRM and BI tools. In this paper, we present a system for visually analyzing these particular information spaces based on a multiple views paradigm. The system brings together views on text similarity, text categories, and associated relational attributes; visual overlay techniques help to keep track of navigation and selection operations in complementary views. The paper sketches an application example and summarizes results from an initial usability evaluation.