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

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

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

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

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

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

Hidden sentiment behind letter repetition in online reviews

Minimal research has been done on how letter repetition affects readers’ perception of expressed sentiment within a text. To the best of the researchers’ knowledge, no studies have tested samples of text with letter repetition using sentiment tools. The main aim of this paper is to investigate whether letter repetition in product reviews are perceived to have any sentiment value, based on ratings by individual participants and analyses using sentiment tools. This study collected and analysed 1,041 consumer reviews in the form of online comments using the UCREL Wmatrix system, and simulated emotional words within the comments to contain repeated letters. A group of 500 participants rated 15 positive comments and 15 negative comments and their respective simulated counterparts, while 32 sentiment tools are used to analyse a pair of positive comment and its simulated counterpart and a pair of negative comment and its simulated counterpart. Results indicate that readers perceive letter repetition to amplify a comment’s sentiment value, in which the effect was found more strongly in negative comments than positive comments. On the other hand, analyses using sentiment tools show that a majority of these tools are unable to detect letter repetition within a word and instead, treats the word as a spelling mistake. As consumers or online users, in general, have been found to use letter repetition to intensify and express their sentiments in their comments, this study’s findings suggest that letter repetition processing in any text-based mechanism needs to be enhanced. The outcome of this paper is useful for improving the measurement of sentiment analysis for the use of marketing applications