On Development of a Fuzzy Stochastic Programming Model with Its Application to Business Management

This chapter expresses efficiency of fuzzy goal programming for multiobjective aggregate production planning in fuzzy stochastic environment. The parameters of the objectives are taken as normally distributed fuzzy random variables and the chance constraints involve joint Cauchy distributed fuzzy random variables. In model formulation process the fuzzy chance constrained programming model is converted into its equivalent fuzzy programming using probabilistic technique, a-cut of fuzzy numbers and taking expectation of parameters of the objectives. Defuzzification technique of fuzzy numbers is used to find multiobjective linear programming model. Membership function of each objective is constructed depending on their optimal values. Afterwards a weighted fuzzy goal programming model is developed to achieve the highest degree of each of the membership goals to the extent possible by minimizing group regrets in a multiobjective decision making context. To explore the potentiality of the proposed approach, production planning of a health drinks manufacturing company has been considered.

First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

Web Data Mining is the application of data mining techniques to extract useful knowledge from web data like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into three categories: Web content mining, Web structure mining and Web usage mining. Web content data are content availed to users to satisfy their required information. Web structure data represents linkage and relationship of web pages to others. Web usage data involves log data collected by web server and application server which is the main source of data. The growth of WWW and technologies has made business functions to be executed fast and easier. As large amount of transactions are performed through e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying the Web Mining techniques.

Predictive Analysis for Digital Marketing Using Big Data

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

Aspect-Based Sentiment Analysis of Online Product Reviews

Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion from text. It is also considered as a task of natural language processing and data mining. Sentiment analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect based sentiment analysis with current and future trend of research on aspect based sentiment analysis. This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600. To perform aspect based classification we are using lexical approach on eclipse platform which classify the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for part of speech tagging, frequency based method is used for extraction of the aspects/features and used negation handling for improving the accuracy of the system.

The Integral of Spatial Data Mining in the Era of Big Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

Joint Decision for Price Competitive Inventory Model with Time-Price and Credit Period Dependent Demand

The problem analyzes a supply chain comprised of two front-runner retailers and one supplier. The retailers' offer customers delay in payments to settle the accounts against the purchases which is received by the supplier. The market demand of the retailer depends on time, retail price and a credit period offered to the customers with that of the other retailer. The supplier gives items with same wholesale price and credit period to the retailers. The joint and independent decisions are analyzed and validated numerically.

Strategic Best-in-Class Performance for Voice to Customer

For any forward-looking perspective, organizational information which is typically historical, incomplete and most of the time inaccurate, needs to be enriched with external information. However, traditional systems and approaches are slow, inflexible and cannot handle new volume and complexity of information. Big data, an evolving term, basically refers to voluminous amount of structured, semi-structured or unstructured information in the form of data with a potential to be mined for ‘best in class information'. Primarily, big data can be categorized by 3V's: volume, variety and velocity. Recent hype around big data concepts predicts that it will help companies to improve operations and makes faster and intelligent decisions. Considering the complexities in realms of supply chain, in this study, an attempt has been made to highlight the problems in storing data in any business, especially under Indian scenario where logistics arena is most unstructured and complicated. Conclusion may be significant to any strategic decision maker / manager working with distribution and logistics.

Mining Big Data for Marketing Intelligence

This chapter elaborates on mining techniques useful in big data analysis. Specifically, it will elaborate on how to use association rule mining, self organizing maps, word cloud, sentiment extraction, network analysis, classification, and clustering for marketing intelligence. The application of these would be on decisions related to market segmentation, targeting and positioning, trend analysis, sales, stock markets and word of mouth. The chapter is divided in two sections of data collection and cleaning where we elaborate on how twitter data can be extracted and mined for marketing decision making. Second part discusses various techniques that can be used in big data analysis for mining content and interaction network.

Business Intelligence through Opinion Mining

Business organizations have been adopting different strategies to impress upon their customers and attract them towards their products and services. On the other hand, the opinions of the customers gathered through customer feedbacks have been a great source of information for companies to evolve business intelligence to rightly place their products and services to meet the ever-changing customer requirements. In this work, we present a new approach to integrate customers' opinions into the traditional data warehouse model. We have taken Twitter as the data source for this experiment. First, we have built a system which can be used for opinion analysis on a product or a service. The second process is to model the opinion table so obtained as a dimensional table and to integrate it with a central data warehouse schema so that reports can be generated on demand. Furthermore, we have shown how business intelligence can be elicited from online product reviews by using computational intelligence technique like rough set base data analysis.