Extract Business Intelligence Research Articles

Hypex: A Tool for Extracting Business Intelligence from Sentiment Analysis using Enhanced LSTM

Sentiment analysis, an application of machine learning in business is the process of identifying and cataloging comments, reviews, tweets, feedback, and even random rants according to the tone or sentiments conveyed by it. The data is analysed using machine learning approach of Long Short Term Memory (LSTM) rating the sentiments on a scale ranging from −100 to 100. A new proposed activation function is used for LSTM giving best results as compared to the existing Artificial Neural Network (ANN) techniques. Depending upon the mined opinion, the business intelligence tools evaluate the products or services of a company eventually resulting in the increase of the sales of that company. The results clearly show that BI extracted from SA is quite instrumental in driving business effectiveness and innovation.

Mining big data streams using business analytics tools: a bird's eye view on MOA and SAMOA

Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.

Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content

Social media platforms provide an enormous public repository of textual data from which valuable information can be extracted. We show that firms can extract business intelligence from social media data bearing on an important business application, measuring brand personality. Specifically, we develop a text analytics framework that integrates different distinct sources of social media data generated by consumers, employees, and firms, to measure brand personality. Based on Elastic-Net regression analyses of a large corpus of social media data, including self-descriptions of 1,996,214 consumers who followed the sample of brands on social media, 312,400 employee reviews of the brands’ firms, and 680,056 brand official tweets, we develop a brand personality model that achieves prediction accuracy as high as 0.78. Among key insights, we find that the profile of individuals who choose to associate with brands on social media is an important predictor of brand personality; this provides the first real-world evidence for a consumer identity-brand personality link. We also identify a link between an organization’s internal corporate environment as perceived by employees and brand personality as judged by consumers. We further illuminate the practical implication of our predictive model by building a cloud-based information system that allows managers and analysts to explore and track personality of their own brands and their competitors’ brands.

Mining Big data streams using Business analytics tools: A bird

Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.

Tourism analytics with massive user-generated content: A case study of Barcelona

Abstract The aim of this paper is to highlight the usefulness of big data analytics to support smart destinations by studying the online image of Barcelona (a leading smart city and tourist destination) as transmitted via social media through the analysis of more than 100,000 relevant travel blogs and online travel reviews (OTRs) written in English by tourists who have visited the city in the last 10 years. The proposed methodology used in this paper facilitates the massive gathering, cleaning up, and analysis of tourism-related user-generated content (UGC) from the most suitable sources, and helps to define the transmitted image of the city through collecting and processing large volumes of digital data. It is also used to extract business intelligence (BI) from OTRs concerning visits to Barcelona’s main landmark/attraction, La Sagrada Familia. The findings of this massive content analysis of information from a trustworthy source, UGC data, is very useful in appling BI to destination management, both in order to develop and assess marketing strategies and to improve branding and positioning policies among tourism and marketing organizations. It reinforces the ability of cities such as Barcelona to develop a smart city and destination concept, as well as a strategy for themselves.

Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective

Classical supervised machine learning techniques have been explored for semantically annotating unstructured textual data such as consumers' comments archived at social media websites to extract business intelligence. However, these techniques often require a large number of manually labeled training examples to produce accurate annotations. Several active learning approaches that are designed based on probabilistic sequence models have been explored to minimize the number of labeled training examples for semantic annotation tasks. Recent research has shown that large-margin classifiers are viable alternatives to automated semantic annotation, given their strong generalization capabilities and the ability to process high-dimensional data. However, the existing active learning methods that are designed for probabilistic sequence models cannot be easily adapted and applied to large-margin classifiers. The main contribution of this paper is the development of novel active learning methods for large-margin classifiers to fill the aforementioned research gap. In particular, we propose an innovative perspective of taking active learning as a search of optimal parameters for large-margin classifiers. A rigorous evaluation involving two benchmark tests and an empirical test based on real-world data extracted from Amazon.com reveals that the proposed active learning methods can train effective classifiers with significantly fewer training examples while achieving similar annotation performance, compared to a typical state-of-the-art classifier that only uses several labeled training examples. More specifically, one of our proposed active learning methods can reduce the number of training examples by 19.74% at the 68% level of F1 when compared to the best baseline method, as evaluated based on the Amazon data set. Our research opens the door to the application of intelligent semantic annotation techniques to support real-world applications such as automatically analyzing consumer comments for customer relationship management.

Online Technology to Manage the Real Estate Owned (REO) Process

The WinREO real estate management software provides a case for extracting business intelligence benefits for a small/medium-sized business systematically as part of other business functions. The software was developed by breaking down the complex REO sales business process into a series of data points, which can then be captured from users as part of a workflow engine. These many tables of data are used to construct tailored workflows and combined to produce meaningful reports for various users inside the organization. Tools for collaboration on the basis of shared data, performance measurement and knowledge management bring additional business intelligence benefits to a small- or medium-sized real estate office through continuous collection of data in various sections of a the software suite. This document details the software's functions, noting the various instance and implementations of data collection for these purposes.

Extracting Business Intelligence from Online Product Reviews

The project proposes to build a system which is capable of extracting business intelligence for a manufacturer, from online product reviews. For a particular product, it extracts a list of the discussed features and their associated sentiment scores. Online products reviews and review characteristics are extracted from www.Amazon.com . A two level filtering approach is adapted to choose a set of reviews that are perceived to be useful by customers. The filtering p rocess is based on the concept that the reviewer generated textual content and other characteristics of the review, influence peer customers in making purchasing choices. The filtered reviews are then processed to obtain a relative sentiment score associat ed with each feature of the product that has been discussed in these reviews. Based on these scores, the customer's impression of each feature of the product can be judged and used for the manufacturers benefit.

Extracting Business Intelligence from Online Product Reviews

Extracting Business Intelligence from Online Product Reviews

Powerful Tool to Expand Business Intelligence: Text Mining

With the extensive inclusion of document, especially text, in the business systems, data mining does not cover the full scope of Business Intelligence. Data mining cannot deliver its impact on extracting useful details from the large collection of unstructured and semi-structured written materials based on natural languages. The most pressing issue is to draw the potential business intelligence from text. In order to gain competitive advantages for the business, it is necessary to develop the new powerful tool, text mining, to expand the scope of business intelligence. In this paper, we will work out the strong points of text mining in extracting business intelligence from huge amount of textual information sources within business systems. We will apply text mining to each stage of Business Intelligence systems to prove that text mining is the powerful tool to expand the scope of BI. After reviewing basic definitions and some related technologies, we will discuss the relationship and the benefits of these to text mining. Some examples and applications of text mining will also be given. The motivation behind is to develop new approach to effective and efficient textual information analysis. Thus we can expand the scope of Business Intelligence using the powerful tool, text mining.