Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed

PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data

Project Testbed: Argument Mapping and Deliberation Analytics

One key goal of the Catalyst project was to design metrics that could capture and represent aspects of the conversation’s structural quality, to assist harvesters and moderators. Many such metrics, alerts and visualizations were developed in the course of the project, but initial user testing has shown that users find it difficult to interpret abstract signals. Following that, we have both introduced new analytics that we felt could be more directly useful, and improved the representation of existing ones. We evaluated their usefulness in a smaller conversation and in experimental settings

A Survey on Mining Top-k Competitors from Large Unstructured Dataset Using k_means Clustering Algorithm and Sentiment Analysis Approach

Along line of research has shown the vital significance of recognizing and observing company�s contestants. In the framework of this activity various questions are emerge like: In what way we formalize and measure the competitiveness between two items? Who are the most important competitors of a specified item? What are the various features of an item that act on competitiveness? Inspired by this issue, the advertising and administration group have concentrated on observational strategies for competitor distinguishing proof and in addition on techniques for examining known contenders. Surviving examination on the previous has concentrated on mining near articulations (e.g.one product is superior then other product) from the web or other documentary sources. Despite the fact that such articulations can without a doubt be indications of strength, they are truant in numerous spaces. By surveying the various papers, we found the conclusion of basic significance of the competitiveness between two items on the basis of market segments

Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

Online content clustering using variant K-Means Algorithms

Thesis (MTech)--Cape Peninsula University of Technology, 2019We live at a time when so much information is created. Unfortunately, much of the information is redundant. There is a huge amount of online information in the form of news articles that discuss similar stories. The number of articles is projected to grow. The growth makes it difficult for a person to process all that information in order to update themselves on a subject matter. There is an overwhelming amount of similar information on the internet. There is need for a solution that can organize this similar information into specific themes. The solution is a branch of Artificial intelligence (AI) called machine learning (ML) using clustering algorithms. This refers to clustering groups of information that is similar into containers. When the information is clustered people can be presented with information on their subject of interest, grouped together. The information in a group can be further processed into a summary. This research focuses on unsupervised learning. Literature has it that K-Means is one of the most widely used unsupervised clustering algorithm. K-Means is easy to learn, easy to implement and is also efficient. However, there is a horde of variations of K-Means. The research seeks to find a variant of K-Means that can be used with an acceptable performance, to cluster duplicate or similar news articles into correct semantic groups. The research is an experiment. News articles were collected from the internet using gocrawler. gocrawler is a program that takes Universal Resource Locators (URLs) as an argument and collects a story from a website pointed to by the URL. The URLs are read from a repository. The stories come riddled with adverts and images from the web page. This is referred to as a dirty text. The dirty text is sanitized. Sanitization is basically cleaning the collected news articles. This includes removing adverts and images from the web page. The clean text is stored in a repository, it is the input for the algorithm. The other input is the K value. All K-Means based variants take K value that defines the number of clusters to be produced. The stories are manually classified and labelled. The labelling is done to check the accuracy of machine clustering. Each story is labelled with a class to which it belongs. The data collection process itself was not unsupervised but the algorithms used to cluster are totally unsupervised. A total of 45 stories were collected and 9 manual clusters were identified. Under each manual cluster there are sub clusters of stories talking about one specific event. The performance of all the variants is compared to see the one with the best clustering results. Performance was checked by comparing the manual classification and the clustering results from the algorithm. Each K-Means variant is run on the same set of settings and same data set, that is 45 stories. The settings used are, • Dimensionality of the feature vectors, • Window size, • Maximum distance between the current and predicted word in a sentence, • Minimum word frequency, • Specified range of words to ignore, • Number of threads to train the model. • The training algorithm either distributed memory (PV-DM) or distributed bag of words (PV-DBOW), • The initial learning rate. The learning rate decreases to minimum alpha as training progresses, • Number of iterations per cycle, • Final learning rate, • Number of clusters to form, • The number of times the algorithm will be run, • The method used for initialization. The results obtained show that K-Means can perform better than K-Modes. The results are tabulated and presented in graphs in chapter six. Clustering can be improved by incorporating Named Entity (NER) recognition into the K-Means algorithms. Results can also be improved by implementing multi-stage clustering technique. Where initial clustering is done then you take the cluster group and further cluster it to achieve finer clustering results