Social media competitive analysis and text mining: A case study in the pizza industry

Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy.     

A sequence-based filtering method for exhibition booth visit recommendations

As exhibitions are known to play important roles in marketing and sales promotion, the exhibition industry has grown significantly not only in the exhibition event size and frequency but also in the number of participating firms and visitors. While the challenge in assessing economic returns from exhibitions is being studied, it is agreed that the eventual success of an exhibition resides largely in its ability to meet the visitors’ needs. Visitors use an exhibition as a source of information when searching for products or services. Though an exhibition provides an information-rich environment, however, visitors often get lost in the abundance of information. A specialized recommender system can be a good solution to information overload as it can guide visitors to right exhibition booths and help them collect necessary information. Traditional collaborative-filtering recommender systems, however, use only customers’ rating or purchase records so that they do not capture exhibition visitors’ temporal visit sequences and dynamic preferences. Moreover, due to the computation overhead, they cannot generate real-time recommendation in ubiquitous environments for exhibitions. In order to overcome these drawbacks, this study proposes a booth recommendation procedure that takes into consideration not only booth visit records but also visit sequences. Experiment results show that the proposed procedure achieves higher recommendation accuracy, faster computation, and more diversity than a typical collaborative-filtering recommender system. From the results, we conclude that the proposed booth recommendation procedure is suitable for real-time recommendation in ubiquitous exhibition environments.     

Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques

This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizens’ use of e-government services. Data mining uses a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualization. Three artificial neural network models (multi-layer perceptron neural network [MLP], probabilistic neural network [PNN] and self-organizing maps neural network [SOM]) and three machine learning techniques (classification and regression trees [CART], multivariate adaptive regression splines [MARS], and support vector machines [SVM]) are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are sex, age, educational level, e-government services perceived usefulness, ease of use, compatibility, subjective norms, trust, civic mindedness, and attitudes. The study shows how it is possible to identify various dimensions of e-government services usage behavior by uncovering complex patterns in the dataset, and also shows the classification abilities of data mining techniques.     

Mining movies for song sequences with video based music genre identification system

Musical sequences with actors dancing and lip-synching to songs sung by playback singers are integral parts, particularly of South Asian movies. Fans seek out movies for their songs and they often seek songs of a particular genre. In fact, song and dance sequence of South Asian movies are an industry of their own. Given the huge numbers of movies produced in South Asia over the past decades, most of which are in digital archives, it is an important problem to automatically extract and categorise their musical sequences. This paper proposes a system for musical sequences extraction from movies. Our method invokes an SVM-based classifier and makes as well a novel application of probabilistic timed automaton to distinguish musical sequences from non-musical. Our system analyses both audio and video signals to give a classifier that not only extracts musical sequences from movies but identifies their genre. We achieved a recall of 93.24% with precision of 87.34% in song extraction when applied on 10 popular Bollywood movies. An accuracy of 89.5% has been achieved on Bollywood song genre identification.     

数据挖掘技术在物流管理中的应用

在分析了数据挖掘技术与物流管理特点的基础上,详细描述了数据挖掘技术中聚类分析技术在物流管理中对货物分类的优化应用,同时对数据挖掘技术在物流网点分布和建设以及物流货物运送中解决复杂问题的能力进行了描述。     

清代贵州六盘水境内矿业兴衰原因

清代,因统治者经营边疆、开放矿禁及铸币所需,促进了贵州六盘水境内大规模的矿业开采,其发达程度为历代所不能比拟,矿业开发极大推动了该地区人口增长和社会发展,但因课税沉重,开采过度,成同战乱等原因,清末,六盘水境内矿业逐渐凋敝。     

导引术对大学生健身理念的启示

文章运用文献资料法和对比分析法,发现近年来大学生学习和就业等压力大及身体运动不足等原因,从而导致身体素质逐年下降。通过对中国传统导引术的理论研究与分析,探索其对大学生的身体发展产生的良好影响,为大学生提供优质的健身理念,以促进大学生的身体健康。     

浅议当代采矿技术发展的研究

经济的快速发展,工业的不断壮大,对于矿产的需求量也越来越多,这就要求采矿业不断地机械化、标准化和高效化,要求采矿技术不断地朝着先进、安全的目标发展。本文对于目前的采矿技术进行了介绍,并对采矿技术的未来发展进行了展望。     

试析计算机数据挖掘技术在档案信息管理系统中的运用

随着社会科技的快速发展,计算机网络技术得到迅速推广与普及。不仅推动了社会的进步,还在很大程度上改善了人们的生活状况。计算机技术被应用到各个领域,给社会各界带来了极大的便利。我国的档案信息管理工作也受到计算机技术的影响。本文通过介绍计算机数据挖掘技术,分析了数据挖掘技术的形式和在档案管理系统中的运用及其意义。