Form Of Unstructured Data Research Articles

Sensing complicated meanings from unstructured data: a novel hybrid approach

The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. The AAUT-ML has been evaluated using three datasets data mining (DM), operating system (OS), and data base (DB), and compared with the existing models, i.e., YAKE, term frequency-inverse document frequency (TF-IDF) and text-R. The results show better outcomes in terms of precision, recall, and macro-averaged F1-score. This work presents a novel method for identifying complex semantics using unstructured data.

Development and Application of Knowledge Graphs for the Injection Molding Process

Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge stored in books and online articles remains in the form of unstructured data, while some even remains unwritten, resulting in many difficulties in the construction of knowledge bases. Therefore, how to extract knowledge from unstructured data and engineers’ statements is a common goal of many enterprises. This study introduced the concept of a Knowledge Graph, a triplet extraction model based on bidirectional encoder representations from transformers (BERT) which was used to extract injection molding knowledge entities from text data, as well as the relationships between such entities, which were then stored in the form of knowledge graphs after entity alignment and classification with sentence-bidirectional encoder representations from transformers. In a test, the triplet extraction model achieved an F1 score of 0.899, while the entity alignment model and the entity classification model achieved accuracies of 0.92 and 0.93, respectively. Finally, a web platform was built to integrate the functions to allow engineers to expand the knowledge graphs by inputting learning statements.

Pelaksanaan Fardhu Kifayah Bagi Jenazah yang Terinfeksi Covid-19 di Rumah Sakit Putri Bidadari Kabupaten Langkat (Analisis Fatwa Mui No 18 Tahun 2020 Pada Fikih Wabah)

The purpose of the study was to determine the implementation of fardhu kifayah for corpses infected with COVID-19 at Putri Bidadari Langkat General Hospital and to find out the obstacles or obstacles in implementing fardhu kifayah for bodies infected with COVID-19 at Putri Bidadari Langkat General Hospital. The type of research used is empirical research. Empirical research is a study that views law as a reality, including social reality, cultural reality and others. Data collection techniques by means of observation, interviews, literature review and documentation. The data analysis technique in this study uses analytical methods, content analysis techniques aim to explore the content or meaning in the form of documents, literary works, articles and so on in the form of unstructured data. Data absence technique by means of triangulation of sources and methods. The results of this study indicate that the implementation of the Covid-19 corpse at RSU Putri Bidadari Langkat is the basis for the management of Muslim bodies (tajhiz al-jana'iz) exposed to COVID-19, especially in bathing and worshiping. must be carried out according to medical protocol and carried out by the Purti Bidadari Langkat General Hospital while still taking into account the provisions of Islamic law. Meanwhile, praying and burying him is carried out as usual by Islamic law while maintaining health protocols so as not to be exposed to COVID-19. Meanwhile, the inhibiting factor or obstacle in implementing the fardhu kifayah of the Covid-19 corpse is the family's refusal to handle the Covid-19 corpse.

An Overview of Tools and Technologies Used for Opinion Mining and Sentiment Analysis

The endeavor of social media has formed many chances for people to publicly voice their beliefs, simply when they are employed to deliver an opinion hit a vital problem. Sentiment analysis is the process to finding the satisfaction information of a consumer’s perception about product, service or brand. Sentiment analysis is also called as opinion mining because it dealt with the huge amount of customer opinion. The analyzing process of customer opinion is playing a vital role in product sale. Sentiment analysis is to extract the features by the notions from others perception about particular product and buying experience. The Sentiment Analysis tool is to function on a series of expressions for a given item based on the quality and features.. To find the opinion rate in the form of unstructured data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools which are used to make clear opinion mining.

Automated Evaluation of Students’ Feedbacks using Text Mining Methods

In this era of competition there is a culture of online reviews or feedbacks. These feedbacks may be about any product or service. However, major issues are their unstructured textual form and big number. It means every user gives feedback in own style. Study and analyzing of such unorganized big number of feedbacks that are growing every year becomes herculean task. This paper describes about mining of structured data (table) and unstructured data (text) both. An application from academic environment for structured and unstructured form of data is considered and discussed to enhance understanding and easiness of researcher. Stanford Parser plays a very useful role to understand the semantic of a sentence. It gives a base that how to separate data from the wellspring of information accessible in the literary structure like web based life, tweets, news, books and so on. It is also helpful to judge a teaching learning process in terms of teacher’s performance and subject’s weakness if any. This paper has five sections first about introduction, second about literature of text mining and its techniques, third about proposed work and result, fourth about future perspectives and finally fifth as a conclusion.

Historical Relation Extraction from Buddhist Temple Documents of the Lanna Kingdom

A substantial amount of valuable knowledge has been recorded, namely historical documents, in the form of unstructured data. The Extraction of such information will provide useful historical knowledge. The objective of this study, is to propose a relation extraction technique of unstructured data from Buddhist temple documents in the Lanna Kingdom, consisting of relationships including the name of the kings who built the temple, the name of the king who created or restored a historical site within its grounds and the Buddhist era the temple was built. The research process began with collecting temple data located in the upper northern part of Thailand, with published documentation of temple history in electronic form from Temples Thailand online website. The data was transformed into an electronic document or a text file, then passed to the LEXTO program for word segmentation, while retaining the original meaning. We designed and developed an algorithm to extract relationships between named entities from Buddhist temple documents, and the application of information extraction principles to the process. We then evaluated the 1,093 original documents by using the algorithm to extract the required information. From the research, the results indicated that the proposed technique can be applied effectively, to accurately extract the relations related to Lanna kingdom history from Buddhist temple documents.

A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks

With explosive development of the World Wide Web, an enormous amount of text information containing users’ feeling, emotions and opinions has been generated and is increasingly employed by individuals and companies for making decisions. Whereas unstructured form of data must be analyzed to extract and summarize the opinions in them, sentiment analysis has changed to a significant research area in the field of Natural Language Processing. In this regard, deep learning methods have attracted a lot of attentions in recent years and various deep learning models have been proven as effective network architectures for the task of sentiment analysis. However, each of them has its potentials and weak points. To eliminate their drawbacks and make optimal use of their benefits, convolutional and recursive neural network are merged into a new robust model in this paper. The proposed model employs recursive neural network due to its tree structure as a substitute of pooling layer in the convolutional network with the aim of capturing long-term dependencies and reducing the loss of local information. The proposed model is validated on Stanford Sentiment Treebank by conducting a series of experiments and empirical results revealed that our model outperforms basic convolutional and recursive neural networks while requires fewer parameters.

A Information Retrieval Based on Question and Answering and NER for Unstructured Information Without Using SQL

In today’s world, the availability of information in the form of unstructured data is in abundance. The unstructured information received is more often than not in the form of natural language text. For any defense establishment, the spy data or any sensitive information received may be best utilized when the information can be extracted efficiently and easily. The proposed model is applicable wherever the influx of text-heavy (unstructured data) is high like the information from the world wide web, documents related to a particular domain, or any other source where the information is in the form of natural language. The proposed Natural Language Information Interpretation and Representation System (NLIIRS) accepts the information in the form of natural language text, processes the information and allows the user to retrieve information by rendering questions in natural language. The questions thus asked by the user are responded by NLIIRS in the form of factoid or phrase based answers. In comparison to the conventional question and answering systems the proposed NLIIRS uses the advantages of both named entity recognition as well as sequential pattern matching based answer search technique. The proposed technique helps us to avoid the use of structured query language (SQL) at the back-end for information processing, storage and extraction. The conversion of user query to SQL statements and also storing the unstructured text in the form of relation tables can be avoided by using NLIIRS. By using this approach in our novel text processing algorithm, after every execution step, the pattern matching and extraction process of the answers to the queries becomes concise and faster. The whole system has been designed on natural language tool kit of Stanford University which helped us to generate parts of speech tag, tokenize the data, and forming tree structure. The novel text processing algorithm utilizes the lemmatizer, stemmer and ne_chunker to prepare the text for information retrieval via Q&A. The advantage of this system is that it does not need training. This system will enable the user to retrieve any information of his/her choice from the available unstructured information.

Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Information

In transportation, nature, economy, environment, and many other settings, there are multiple simultaneous phenomena happening that are of interest to model and predict. Over the last few years, the traffic data that we have at our disposal have significantly increased, and we have truly entered the era of big data for transportation. Most existing travel demand prediction methods mainly focus on capturing recurrent mobility trends that relate to habitual/routine behavior, and on exploiting short-term correlations with recent observation patterns. However, valuable information that is often available in the form of unstructured data is neglected when attempting to improve forecasting results. Particularly, under non-recurrent conditions, such as large events, or incidents, we need much better models. In this paper, we explore time-series data and semantic information combinations using machine learning and deep learning techniques in the context of creating a prediction model that is able to capture in real-time future stressful situations of the studied transportation system. We apply the proposed approaches in event areas in New York using publicly available taxi data. We empirically show that the proposed models are able to significantly reduce the error in the forecasts. The importance of semantic information is highlighted in all presented methods and the final mean absolute error of our prediction is decreased by 23.8% for a three months testing period.

Content enrichment with expressive document modelling to leverage the understanding of unstructured data

Most information in an enterprise is in the form of unstructured data which is usually managed using a document database. One of the key challenges is to define a generalized data model for this unstructured data and any information extracted from it using content enrichment algorithms. It is more challenging to incorporate provenance and temporal capabilities to such data models. Semantic databases use ontologies such as PROV-O to represent their provenance information expressively, and relational databases use for example Slowly Changing Dimensions (SCDs) concepts to represent temporal information. In this paper, we present a document model which has features inspired from Dublin core, PROV-O and temporal methodologies to generalize information extracted from unstructured data using content enrichment algorithms. Provenance information enables comparison of enrichment models, allows reproducibility and facilitates complex filtering on the enriched data. Temporal metadata helps in versioning the document and enables point-intime and history queries conveniently.

Knowing what you have: the road to effective data discovery

It's hard to keep data secure when you're not sure what information you have. While your databases might be neatly ordered and well understood, much of the information washing around in organisations is likely to be in the form of unstructured data – emails, PDFs, word processor and spreadsheet files and many other data types in a wide variety of formats. In this interview, Colin Tankard, managing director of Digital Pathways, explains the security challenge posed by this ad hoc information and what you can do about it. It's hard to keep data secure when you're not sure what information you have. While your databases might be neatly ordered and well understood, much of the information washing around in organisations is likely to be in the form of unstructured data – emails, PDFs, word processor and spreadsheet files and many other data types in a wide variety of formats. In this interview, Colin Tankard of Digital Pathways explains the security challenge posed by this ad hoc information and what you can do about it.

Big Data in Healthcare Management: A Review of Literature

A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.

SENTIMENTAL ANALYSIS USING LEAST SQUARES TWIN SUPPORT VECTOR MACHINE

Sentiment analysis is field of text mining in which reviews are in form of unstructured data so opinions can be extracted from overall opinion. This paper works on finding approaches that generate output with good accuracy. Least squares twin support vector machine (LSTSVM) is a quite new version of support vector machine (SVM) based on non-parallel twin hyperplanes. LSTSVM is an extremely efficient and fast algorithm for binary classification and its parameters depend on the nature of the problem. The goal of this paper is to improve the accuracy through LSTSVM. A result on several benchmark datasets is applied to train a sentiment classifier inorder to demonstrate the accuracy of the proposed algorithm. N-grams and different weighting scheme were used to take out the most classical features. It also analyzes Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection in LSTSVM may provide significant improvement on classification accuracy.

Sentiment Analysis Using Deep Learning Techniques: A Review

The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.

토픽 모델링을 이용한 핀테크 기술 동향 분석

최근 인터넷과 모바일 환경을 기반으로 금융과 IT가 융합된 핀테크(Fintech) 산업이 급속히 성장하고 있으며 간편성, 편리성 등으로 무장한 핀테크 서비스는 모든 금융서비스의 온라인 모바일 화를 주도하고 있다. 그러나 핀테크 산업의 급격한 성장에도 불구하고, 핀테크 기술에 대한 세부기술 분류와 주요 시장국의 기술개발 동향을 분석하고 기술기획을 지원하기 위한 연구는 매우 미흡한 실정이다. 이에 본 연구는 핀테크 기술의 비정형 데이터 형태의 특허 데이터를 이용하여 토픽모델링 기법을 통해, 핀테크 세부 기술을 추출하고 정의한다. 도출된 핀테크 세부 기술에 대해 Hot&Cold topic 을 파악하여 핀테크 기술의 트렌드를 파악한다. 또한 핀테크 산업의 주요 기술에 대한 주요 시장국인 미국, 한국, 중국의 기술개발 동향을 각각 분석한다. 마지막으로 핀테크 세부 기술 간 네트워크 분석을 통해 기술 간의 연계 관계를 살펴본다. 본 연구를 통해 파악된 핀테크 산업 기술 동향은 핀테크 산업분야의 정책 수립과 핀테크 관련 기업의 기술 전략 수립에 효과적으로 활용될 수 있을 것으로 기대된다. Recently, based on Internet and mobile environments, the Fintech industry that fuses finance and IT together has been rapidly growing and Fintech services armed with simplicity and convenience have been leading the conversion of all financial services into online and mobile services. However, despite the rapid growth of the Fintech industry, few studies have classified Fintech technologies into detailed technologies, analyzed the technology development trends of major market countries, and supported technology planning. In this respect, using Fintech technological data in the form of unstructured data, the present study extracts and defines detailed Fintech technologies through the topic modeling technique. Thereafter, hot and cold topics of the derived detailed Fintech technologies are identified to determine the trend of Fintech technologies. In addition, the trends of technology development in the USA, South Korea, and China, which are major market countries for major Fintech industrial technologies, are analyzed. Finally, through the analyses of networks between detailed Fintech technologies, linkages between the technologies are examined. The trends of Fintech industrial technologies identified in the present study are expected to be effectively utilized for the establishment of policies in the area of the Fintech industry and Fintech related enterprises' establishment of technology strategies.

Analysis of Various Sentiment Classification Techniques

Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.

A Study on Sentiment Analysis: Methods and Tools

The purpose of social media has created many chances for people to publicly voice their beliefs, simply when they are employed to deliver an opinion hit a vital problem. Sentiment Analysis is a case of natural language processing which could mark the mood of the people about any specific product by analysis. Sentiment Analysis is a process of automatic extraction of features by mode of notions of others about specific product, services or experience. The Sentiment Analysis tool is to function on a series of expressions for a given item based on the quality and features. Sentiment analysis is also called Opinion mining due to the significant volume of opinion. Analyzing customer opinion is very important to rate the product. To automate rate the opinions in the form of unstructured data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools used.

Web Logs Analysis for Finding Brand Status

Due to rapid development of the web there has been vast increase in the user generated contents available in the form of blogs ,product reviews sites, web- forums and online social networks etc. Such reviews are very useful to the companies, as they contain valuable information about what aspects of the product, are driving the sales up or down.But most of the available content is in the form of unstructured data from where extraction of information is a challenging task. In order to find potential risk, it is essential for companies to gather and analyze information about competitor products and strategies. Companies use opinion mining techniques to track customer's response, in order to efficiently market their products, identify new opportunities, weaknesses and manage their reputations. In this paper, brand status model is proposed based on the various parameters which affect brands reputation. Also, the products belonging to different brands will be compared on the basis of predefined aspects. This model uses aspect based sentiment analysis technique to find sentiments about product's features and brand.