A concept to measure social capital in social network sites

This paper describes a concept to measure social capital. The concept needs dimensions and illustrative factors to explain the existence of social capital in social network sites. This paper describes the dimensions and factors to measure social capital. The objective of this paper is to demonstrate a way to describe social capital. The description of social capital supports the measurement and gives the scientific world the opportunity to identify and to attest social capital in social network sites. The value of the paper is the concept to measure social capital with a questionnaire and gives the opportunity to identify social capital in networks with many participants

SmartPLS for the human resources field to evaluate a model

This paper describes the Partial Least Square model to test the robustness and value of the statistical evaluation. The test is to evaluate the fit of the model for a small sample. The statistical data is calculated with the SmartPLS software. SmartPLS is a tool created for statistical analysis, namely PLS – SEM (Structural Equation Model). The paper describes the advantages and disadvantages of SmartPLS and provides an argument for the use of SmartPLS in the scientific world. At the moment the use of SmartPLS in science concentrates mainly in the information technology field and the marketing area. The authors describe the use of SmartPLS for the human resources area which is a new field for SmartPLS software. The paper further describes the validity and reliability for PLS – SEM

Text segmentation techniques: A critical review

Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007- January 2017)’s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the “word” seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line

Value of expressions behind the letter capitalization in product reviews

Product reviews from consumers are the source of opinions and expressions about purchased items or services. Thus, it is essential to understand the true meaning behind text reviews. One of the ways is to analyze sentiments, expressions and emotions behind the text. However, there are different styles of writing used in the text. One of widely used in the text is letter capitalization. It is commonly used to strengthen an expression or louder tone within the text. This paper explores the value of expression behind letter capitalization in product reviews. We compared fully capitalized text, text with one capitalized words and text without capitalization through the readers’ perspective by asking them to rate the text based on Likert scale. Furthermore, we tested two samples of text with and without capitalization on 27 available online sentiment tools. Testing was done in order to check how current sentiment tools treat letter capitalization in their sentiment score. Results show that of letter capitalization is able to enforce the different level of expression. If the nature of the review is positive, the capitalization makes it more positive. Similar for the negative reviews, the capitalization tends to increase negativity

Machine learning classifiers: Evaluation of the performance in online reviews

This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value. The term “sentiment value” in this study is referring to the polarity (positive, negative or neutral) of the text. This work applies machine learning classifiers from WEKA (Waikato Environment for Knowledge Analysis) toolkit in order to perform their evaluation. WEKA toolkit is a great set of tools for data mining and classification. The performance of the machine learning classifiers was measured by examining overall accuracy, recall, precision, kappa statistic and applying few visualization techniques. Finally, the analysis is applied to find the most suitable classifier for classifying sentiment value. Results show that two classifiers from Rules and Trees categories of classifiers perform equally best comparing to the other classifiers from categories, such as Bayes, Functions, Lazy and Meta. This paper explores the performance of machine learning classifiers in sentiment value classification in the online reviews. Data used is never been used before to explore the performance of machine learning classifiers

Text segmentation for analysing different languages

Over the past several years, researchers have applied different methods of text segmentation. Text segmentation is defined as a method of splitting a document into smaller segments, assuming with its own relevant meaning. Those segments can be classified into the tag, word, sentence, topic, phrase and any information unit. Firstly, this study reviews the different types of text segmentation methods used in different types of documentation, and later discusses the various reasons for utilizing it in opinion mining. The main contribution of this study includes a summarisation of research papers from the past 10 years that applied text segmentation as their main approach in text analysing. Results show that word segmentation was successfully and widely used for processing different languages

Determining the indicators of social capital theory to social network sites

The authors theoretical research provided a framework to define and measure social capital in social network sites. This paper determining with a qualitative method the theoretical dimensions of this framework. The result of this paper is the explanation of social capital theory in social network sites under the consideration of the identified indicators. The sample for this case is students, and the research needs further confirmation by other samples and methods. But this paper gives a first interesting insight in social network sites explained with the social capital theory and provides a first guidance for further research projects

Utilization of Facebook by school children in the apprenticeship seeking process

The search for a practical apprenticeship place can be the first step in the business world for German students. The students have to apply for this placement, as Companies require applicants. Facebook is one of the most often used social networks among the younger generation in Germany, which can provide a direct communication channel between businesses and candidates. The research evaluates the reasons to use Facebook to identify a solid apprenticeship training platform for German students. Research methods applied: scientific publication analysis, survey (by paper-based questionnaire) of German students of the ninth and tenth grade. Analysis of survey data by main indicators of descriptive statistics: arithmetic mean, mode, median, and standard deviations to get an impression of evaluations on analysed aspects by survey respondents. Analysis of variance – ANOVA – is applied to study the difference of the assessments between female and male school children and the differences between the ninth and tenth classes. The existence of correlations between the intensity of use of social network sites (SNS) and the apprenticeship seeking process have been investigated. The results of the research have shown that there are differences in evaluations, on analysed aspects, between female and male school children in the analysed classes on the occasion career entry by the apprenticeship

Reasons of individuals to trust what they read on social network sites

The rationality of believing a piece of information depends on the level of trust of each individual. To make a purchase decision, an individual has to trust the information they have collected from social media content. That said, to influence an individual’s decision, companies have to obtain an individual’s trust successfully. This study aims to investigate what makes an individual trust what they read on social media (SM). Participants of this study are from Germany and United Kingdom (UK). A model is created and tested with regression analysis. That helps to identify the impact experience with social network sites (SNSs) on trust of information in SNSs. The practical outcome is to provide advice to companies whereby to communicate with potential clients to transfer their information successfully and trustworthy

The use of social networking sites for the employment seeking process

Social networks are becoming more and more important in employment seeking process. The importance of social networks in this respect has been researched also in academic research worldwide and discussed on scientific conferences. The aim of the paper is to analyse the experience of the use of social network sites (SNS) with empirical results of 28 interviews with employment seeking individuals to identify the behaviour of employment seeking individuals and to identify further information regarding the employment seeking process in SNSs. In addition is an objective of the paper to falsify the dimensions of Sander / Teh. That the framework of the dimensions can be used to investigate SNSs and to describe the social capital theory of SNSs(Sander & Teh 2014a). The importance of real social networks are presented in many papers but the perspective of the employment seeking individual in SNSs needs further and deeper research