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

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

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

Compilation of Malay criminological terms from online news

A Malay language corpus has been established by the Institute of Language and Literature (Dewan Bahasa dan Pustaka, DBP in Malaysia). Most of the past research on the Malay language corpus has focused on the description, lexicography and translation of the Malay language. However, in the existing literature, there is no list of Malay words that categorizes crime terminologies. This study aims to fill that linguistic gap. First, we aggregated the most frequently used crime terminology words from Malaysian online news sources. Five hundred crime-related words were compiled. No automatic machines were in the initial process, but they were subsequently used to verify the data. Four human coders were used to validate the data and ensure the originality of the semantic understanding of the Malay text. Finally, major crime terminologies were outlined from a set of keywords to serve as taggers in our solution. The ultimate goal of this study is to provide a corpus for forensic linguistics, police investigations, and general crime research. This study has established the first corpus of a criminological text in the Malay language

Superconducting Phase Transition in Inhomogeneous Chains of Superconducting Islands

We study one-dimensional chains of superconducting islands with a particular emphasis on the regime in which every second island is switched into its normal state, thus forming a superconductor-insulator-normal metal (S-I-N) repetition pattern. As is known since Giaever tunneling experiments, tunneling charge transport between a superconductor and a normal metal becomes exponentially suppressed, and zero-bias resistance diverges, as the temperature is reduced and the energy gap of the superconductor grows larger than the thermal energy. Here we demonstrate that this physical phenomenon strongly impacts transport properties of inhomogeneous superconductors made of weakly coupled islands with fluctuating values of the critical temperature. We observe a nonmonotonous dependence of the chain resistance on both temperature and magnetic field, with a pronounced resistance peak at temperatures at which some but not all islands are superconducting. We explain this phenomenon by the inhomogeneity of the chains, in which neighboring superconducting islands have slightly different critical temperatures. We argue that the Giaever\u27s resistance divergence can also occur in the zero-temperature limit. Such quantum transition can occur if the magnetic field is tuned such that it suppresses superconductivity in the islands with the weaker critical field, while the islands with stronger energy gap remain superconducting. In such a field, the system acts as a chain of S-I-N junctions