Capturing user sentiments for online Indian movie reviews.

Sentiment analysis and opinion mining are emerging areas of research for analysing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers. In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time).The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM. This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.N

Visualizing the hotspots and emerging trends of 3D printing through scientometrics

Purpose - 3D printing is believed to be driving the third industrial revolution. A comprehensive understanding of the hotspots and trends of 3D printing may promote the theory development of 3D printing, help researchers to determine the research direction, and provide a reference for enterprises and government to plan the development of 3D printing industry. However, a scientometric visualizing of 3D printing research and an exploration its hotspots and emerging trends are lacking. Therefore, it was necessary to carry out this relevant research. Design/methodology/approach – Based on the theory of scientometrics, 2769 literatures on the 3D printing theme were found in the Web of Science Core Collection’ SCI indexes between 1995-2016. These were analyzed to explore the research hotspots and emerging trends of 3D printing with the software CiteSpaceIII. Findings – (1) hotspots appeared first in 1993, grow rapidly from 2005, and peaked in 2013; (2) hotspots in the "medical field" appeared earliest and have remained extremely active; (3) hotspots have evolved from “drug”, "printer", "rapid prototyping" and "3D printing" in the 1990s, through "laser-induced consolidation", "scaffolds", "sintering" and "metal matrix composites" in the 2000s, to the current hotspots of "stereolithography", "laser additive manufacturing", "medical images", etc.; (4) "3D bioprinting",“titanium”, “stem cell” and "chemical reaction" were the emerging hotspots in recent years; (5) "commercial operation" and "fusion with emerging technology such as big data" may create future hotspots. Research limitations/implications - It is hard to avoid the possibility of missing important research results on 3D printing. The relevant records could be missing if the query phrases for topic search do not appear in records. Besides, in order to improve the quality of data, this study selected articles and reviews as the research objects, which may also omit some records. Originality/value - First, this is the first paper visualizing the hotspots and emerging trends of 3D printing using scientometric tools. Second, not only "burst reference" and "burst keywords", but also "cluster" and "landmark article" are also selected as the evaluation factors to judge the hotspots and trends of a domain comprehensively. Third, overall perspective of hotspots and trends of 3D printing is put forward for the first time

New Working Practices: A Scientometric Review

Study on New Working Practices (NWPs), which is the subject of this review paper, has created a large body of literature. Studies in this research area are progressing quickly, and it is important to stay abreast of new trends and essential factors in the growth of mutual awareness. This study evaluates the global scientific output of New Working Practices (NWPs) research and explores their hotspots and frontiers from 1980 to 2018 (pre-COVID-19), using bibliometric methods. 850 relevant articles were retrieved from the Web of Science Core Collection (WoSCC) and analysed. Scientometric method and Citespace VI were used to analyse the bibliometric data. Reference citation and cocitation networks were plotted, while keywords were used to analyse the research hotspots and trends. There is a significant increase in the number of annual publications with time. The United Kingdom (UK) ranked highest in the countries with most publications, and the leading author is Friedhelm Nachreiner based on publication counts. The most cited author/organisation is the UK Department of Health. Performance, work, and flexible working are the research hotspots, while flexible working arrangement represents the prominent research domain. The study offers valuable references for researchers, industry practitioners and policymakers

Deep Learning for Learning Representation and Its Application to Natural Language Processing

As the web evolves even faster than expected, the exponential growth of data becomes overwhelming. Textual data is being generated at an ever-increasing pace via emails, documents on the web, tweets, online user reviews, blogs, and so on. As the amount of unstructured text data grows, so does the need for intelligently processing and understanding it. The focus of this dissertation is on developing learning models that automatically induce representations of human language to solve higher level language tasks. In contrast to most conventional learning techniques, which employ certain shallow-structured learning architectures, deep learning is a newly developed machine learning technique which uses supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures and has been employed in varied tasks such as classification or regression. Deep learning was inspired by biological observations on human brain mechanisms for processing natural signals and has attracted the tremendous attention of both academia and industry in recent years due to its state-of-the-art performance in many research domains such as computer vision, speech recognition, and natural language processing. This dissertation focuses on how to represent the unstructured text data and how to model it with deep learning models in different natural language processing viii applications such as sequence tagging, sentiment analysis, semantic similarity and etc. Specifically, my dissertation addresses the following research topics: In Chapter 3, we examine one of the fundamental problems in NLP, text classification, by leveraging contextual information [MLX18a]; In Chapter 4, we propose a unified framework for generating an informative map from review corpus [MLX18b]; Chapter 5 discusses the tagging address queries in map search [Mok18]. This research was performed in collaboration with Microsoft; and In Chapter 6, we discuss an ongoing research work in the neural language sentence matching problem. We are working on extending this work to a recommendation system

Sentiment analysis in a resource scarce language: Hindi

A common human behavior is to take other’s opinion before taking any decision. With the tremendous availability of documents which express opinions on different issues, the challenge arises to analyze it and produce useful knowledge from it. Many works in the area of Sentiment Analysis is available for English language. From last few years, opinion-rich resources are booming in other languages and hence there is a need to perform Sentiment Analysis in those languages. In this paper, a Sentiment Analysis in Hindi Languag

Delineating the citation impact of scientific discoveries

IEEE/ACM Joint Conference on Digital Libraries (JCDL 2007). June 17-22, 2007. Vancouver, BC, Canada.Identifying the significance of specific concepts in the diffusion of scientific knowledge is a challenging issue concerning many theoretical and practical areas. We introduce an innovative visual analytic approach to integrate microscopic and macroscopic perspectives of a rapidly growing scientific knowledge domain. Specifically, our approach focuses on statistically unexpected phrases extracted from unstructured text of titles and abstracts at the microscopic level in association with the magnitude and timeliness of their citation impact at the macroscopic level. The H-index, originally defined to measure individual scientists’ productivity in terms of their citation profiles, is extended in two ways: 1) to papers and terms as a means of dividing these items into two groups so as to replace the less optimal threshold-based divisions, and 2) to take into account the timeliness of the impact of knowledge diffusion in terms of the timing of citations and publications so that attention is particularly drawn towards potentially significant and timely papers. The selected terms are connected to higher-level performance indicators, such as measures derived from the H-index, in the form of decision trees. A top-down traversal of such decision trees provides an intuitive walkthrough of concepts and phrases that may underline potentially significant but currently still latent scientific discoveries. Timeliness measures can also help to identify institutions that are at the forefront of a research field. We illustrate how widely accessible tools such as Google Earth can be utilized to disseminate such insights. The practical significance for digital libraries and fostering scientific discoveries is demonstrated through the astronomical literature related to the Sloan Digital Sky Survey (SDSS)

Cognitive Conflict in Science: Demonstrations in what scientists talk about and study.

The concept of cognitive conflict, that being two competing ideas in the mind at the same time, encompasses a large number of instantiations throughout Psychology (Festinger, 1964; Heine, Proulx, & Vohs, 2006), even playing an important role in many philosophies considering how science works best (Kuhn, 1962; Platt, 1964; Popper, 1934/ 2005). This experience of cognitive conflict is widely considered to be aversive, but also motivating, for individuals across a wide range of contexts. Here I examined two ways cognitive conflict affects what topics receive scientific attention. Pairing the philosophies of science with Festinger’s (1950) hypotheses about informal social communication, it was hypothesized that: 1. Scientists will discuss things they disagree about more than things they agree about. 2. Scientists will study more those topics which threaten individual or group outcomes. Utilizing publicly available data about scientific publications, I tested these hypotheses within a number of contexts, including public comments on papers, Tweets about papers and topics, and the author and automatically generated keywords describing scientific papers themselves (as a measure of what scientists write about and study). Two studies suggested that more negations in the texts (e.g., but, not, however) were related to larger discussions, more views, and more media attention. Two other studies examined the keywords describing papers, first all papers published across science by PLoS, and then all papers across publishers within Psychology. Both studies suggested that there are more unique negative keywords studied, and that these keywords have more papers written about them, on average. Overall, the results suggest that scientists talk more when they disagree, and that they speak more about threats to the group and individual. This more generally implies that cognitive conflict plays a role in determining what scientists talk about and study, and more generally that general psychological principles can be applied within the context of science