Text Mining and Information Retrieval

DC Universe is a fictional universe in which a collection of superheroes and super villains based on characters that appear in comic books by DC Comics is in it. DC Comics itself is the largest and oldest comic book publisher that produces and displays superheroes and super villains. To start a super hero-themed business, there are a number of business examples that can be used as references and can also be used to reap profits from the business, namely, rental of comics, selling merchandise, making clothing lines, making cosplay costumes, making superhero-themed foods, selling action figure. In an effort to start a super hero-themed business, especially the DC Universe theme, it is necessary to pay attention / listen to DC Universe consumers in Indonesia. Classification of opinions or sentiment analysis is one way to find out about a person or group of people towards certain products, services, issues or groups from various social media platforms and the internet. Twitter is one of the social media that is loved by the people of Indonesia. This research tries to utilize what was written by Twitter social media users or better known as a tweet. Tweets will be processed by text mining and processed again using the Naïve Bayes Classifier algorithm.

In recent years, impressive attention has been given for mining the publically available huge amount of data to gain situational awareness, which may help in preventing or decrease the effect of some disaster by taking the correct responses. In this study, an effective Convolutional Neural Networks (CNN) tweet classification system that fully supports the Turkish language has been developed.
In addition, the first-ever Turkish tweet dataset for crisis response is created. This dataset has been carefully preprocessed, annotated, well organized and suitable to be used by all the well-known natural language processing tools. Furthermore, the performance of some well-known machine learning algorithms, i.e., K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine(SVM) was investigated. Then, the performances of the ensemble systems Random Forest (RF), AdaBoost Classifier (AdaBoost), GradientBoosting Classifier (GBC), when used for text (tweets) classification, has been also observed.
A wide range of experiments was performed to investigate the performance of the developed system. As a result, the developed approach has achieved very good performance, robustness, and stability when processing both Turkish and English languages.
Key Words: Crises Management Systems; Tweet Classification; Turkish language; Convolutional Neural Networks; Natural Language Processing.

The world is intrigued by data. In fact, huge capitals are invested to devise means that implements statistics and extract analytics from these sources. However, when we examine the studies performed on applicant tracking systems that retrieve valuable information from candidates’ CVs and job-descriptions, they are mostly rule-based and
hardly manage to employ contemporary techniques. Even though these documents vary in contents: the structure is almost identical. Accordingly, in this paper, we implement an NLP pipeline for the extraction of such structured information from a wide variety of textual documents. As a reference, textual documents which are used in applicant tracking systems like CV (Curriculum Vitae) and job vacancy information
have been considered. The proposed NLP pipeline is built with several NLP techniques like document classification, document segmentation and text extraction. Initially for the classification of textual documents, Support Vector Machines (SVM) and XGBoost algorithms have been implemented. Different segments of the identified document are categorized using NLP techniques such as chunking, regex matching and POS tagging. Relevant information from every segment is further extracted using techniques like Named Entity Recognition (NER), regex matching and pool parsing. Extraction of such structured information from textual documents can help to gain insights and use those insights in document maintenance, document scoring, matching and auto filling forms.

This paper introduces a three-step methodology of identifying skills demand on labour markets. By accessing publicly available vacancy data, with web and text mining tools, we are able to extract valuable facts about competences and abilities sought by employers. This easily applicable technique provides a new dimension in labour market research. It supplements occupational analysis with detailed information related to specialised knowledge expected from prospective employees. By example of IT jobs publicised through IrishJobs.ie domain, we present how web based data can be successfully acquired and pre-processed to suit our research needs, and how meaningful information can be extracted in efficient and reasonably quick manner. Evidence obtained through this process is a straightforward reflection of current needs, and can be acted on by educational and labour market bodies to bridge the gap between skills demand and supply.

Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.

Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.

Each argument begins with a conclusion, which is followed by one or more premises supporting the conclusion. The warrant is a critical component of Toulmin's argument model; it explains why the premises support the claim. Despite its critical role in establishing the claim's veracity, it is frequently omitted or left implicit, leaving readers to infer. We consider the problem of producing more diverse and high-quality warrants in response to a claim and evidence. To begin, we employ BART [1] as a conditional sequence tosequence language model to guide the output generation process. On the ARCT dataset [2], we fine-tune the BART model. Second, we propose the Multi-Agent Network for Warrant Generation as a model for producing more diverse and high-quality warrants by combining Reinforcement Learning (RL) and Generative Adversarial Networks (GAN) with the mechanism of mutual awareness of agents. In terms of warrant generation, our model generates a greater variety of warrants than other baseline models. The experimental results validate the effectiveness of our proposed hybrid model for generating warrants.

The availability of lexical resources is huge to accelerate and simplify the sentiment analysis in English. In Arabic, there are few resources and these resources are not comprehensive. Most of the current research efforts for constructing Arabic Sentiment Lexicon (ASL) depend on a large number of lexical entities. However, the coverage of all Arabic sentiment expressions can be applied using refined regular expressions rather than a large number of lexical entities. This paper presents an ASL that more comprehensive than the existing lexicons, for covering many expressions with different dialects including Franco-Arabic, and in the same time more compact. Also, this paper shows how to integrate different lexicons and to refine them. To enrich lexical entries with very robust morphological syntactical information, regular expressions, the weight of sentiment polarity and n-gram terms have been augmented to each

Natural  Language Processing is a programmed approach to  analyze text that is based on both  a
set of theories and a set of technologies. This forum aims to bring together researchers who have
designed  and  build  software  that  will  analyze,  understand,  and  generate  languages  that  humans
use naturally to address computers.

O presente estudo teve por objetivo propor um processo de mineração de conteúdos em mídias sociais para auxiliar na gestão de destinos turísticos composto por sete fases, elaborado com base nas metodologias propostas por Neves (2013), Hea, Zha e Li (2013), Kalampokis, Tambouris e Tarabanis (2013), Abrahams, Jiao, Fan, Wang e Zhang (2013) e nos modelos de descoberta de conhecimento propostos por Fayyad, Piatetsky-Shapiro e Smyth (1996), Chapman et al. (2000) e Han, Kamber e Pei (2012). Caracteriza-se como pesquisa exploratória e descritiva e como método de investigação foram utilizados métodos mistos. Embora explore o monitoramento nas mídias sociais Facebook, Twitter e YouTube, o processo proposto foi verificado a partir da mineração de conteúdos do Twitter que tivessem os termos da ontologia de aplicação de atrativos e serviços turísticos (hospedagem, alimentação e transportes) das cidades de Curitiba (PR) e Foz do Iguaçu (PR), por opção metodológica e pela dificuldade em obter dados relevantes nas demais mídias sociais investigadas devido a limitações em suas Application Programming Interface (API). O presente processo mostrou-se ser eficaz para coletar conteúdos relevantes e identificar assuntos populares nas mídias sociais, realizar análises quantitativas e qualitativas, bem como auxiliar às Destination Management Organizations – DMO na gestão de destinos turísticos e no processo de tomada de decisões estratégicas e operacionais. Como resultado das análises da utilização das mídias sociais pelas DMO investigadas, constatou-se que o Facebook e o Twitter são mais utilizadas do que o YouTube, que ainda é pouco explorado em relação às demais. Identificou-se ainda que apesar das ações, estratégias e conteúdos publicados serem semelhantes, as abordagens e objetivos variam e os esforços e ações das DMO nas mídias sociais ainda são experimentais. Através das entrevistas semiestruturadas pessoais realizadas com os responsáveis pela gestão e atualização dos perfis em mídias sociais das DMO constatou-se que nenhuma DMO monitora as mídias sociais efetivamente utilizando softwares de monitoramento de mídias sociais ou técnicas de mineração de conteúdos. Entretanto, ainda que superficialmente, as DMO utilizam-se da ferramenta analítica do Facebook para monitorar e analisar o desempenho das ações e publicações. Por fim, foi possível identificar a inexperiência, a falta de conhecimento técnico e de recursos humanos e financeiros como as principais limitações frente a utilização e monitoramento de mídias sociais pelas DMO investigadas. Como sugestão de trabalhos futuros, sugere-se a elaboração do modelo teórico de gestão do conhecimento para que os resultados e conhecimentos obtidos sejam explicitados às instâncias de governança (federal, estadual e municipal), para os demais atores públicos e privados envolvidos na atividade turística, a ampliação das ontologias de aplicação elaboradas e ao monitoramento e mineração de conteúdos em mídias sociais sobre outras organizações turísticas públicas e privadas ou outros eventos como as Olimpíadas no Rio de Janeiro em 2016.


Palavras-chave: turismo; gestão de destinos turísticos; mídias sociais; monitoramento em mídias sociais; mineração de conteúdos em mídias sociais.

Information digitization makes information dissemination faster, actual, and cheaper. The information disseminated occurs in the form of text, which contains much of the information contained in it. Because of the vast amount of important information contained in text documents (news), it takes certain methods to classify them. Several studies have been conducted, but none have implemented Particle Swarm Optimization (PSO) for feature selection on document classification. So, in this research will be applied PSO to perform feature selection, and also Naïve Bayes Classifier (NBC) for its classification. The data used comes from 20 Newsgroups. The trial model divides training documents from 10% to 90%. This is done to find out which model will produce the highest accuracy. From the experiments with the model is known, the highest accuracy achieved is 85.42% with training documents of 80% (15,077 documents). Meanwhile, experiments using different document samples, with a predetermined class yielding accuracy of up to 99.87%. Test document used is 20% (3770 documents).

An essential part of bioinformatic research concerns the iterative process of validating hypotheses by analyzing facts stored in databases and in published literature. This process can be enhanced by language technology methods, in particular by automatic text understanding. Since it is becoming increasingly difficult to keep up with the vast number of scientific articles being published, there is a need for more easily accessible representations of the current knowledge. The goal of the research described in this paper is to develop a system aimed to support the large-scale research on metabolic and regulatory pathways by extracting relations between biological objects from descriptions found in literature. We present and evaluate the procedures for semantico-syntactic tagging, dividing the text into parts concerning previous research and current research, syntactic parsing, and transformation of syntactic trees into logical representations similar to the pathway graphs utilized in the Kyoto Encyclopaedia of Genes and Genomes.

Natural Language Processing is a programmed approach to analyze text that is based on both a
set of theories and a set of technologies. This forum aims to bring together researchers who have
designed and build software that will analyze, understand, and generate languages that humans
use naturally to address computers.

Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.

Belakangan ini masyarakat banyak disuguhkan berbagai macam situs berita mulai dari thin-content dengan kategori berita yang sedikit sampai dengan yang rich-content dengan berbagai macam kategori. Tapi biasanya situs berita tersebut dibebani oleh banyaknya banner yang ditampilkan sehingga memperlambat akses untuk membaca berita, terlebih lagi jika bandwitdth untuk koneksi internet yang digunakan terbatas. Selain itu, tidak setiap orang memiliki banyak waktu untuk membaca semua berita yang disuguhkan pada situs berita tersebut. Menemukan berita yang relevan sesuai dengan yang diinginkan secara cepat saat ini menjadi masalah yang serius karena ketersediaan berita di internet semakin meningkat jumlahnya. Saat ini aplikasi Internet Agent menjadi populer digunakan untuk mencari informasi yang berguna di Internet. Internet Agent memiliki kemampuan untuk menjelajah internet untuk melakukan pencarian, pemfilteran, dan penyajian informasi bagi user, secara mandiri. Penerapan Internet Agent dan penggunaan metode Fuzzy Search dan Association Rule untuk pengklasifikasian berita menjadi pilihan solusi untuk mengatasi permasalahan yang terjadi. Berdasarkan hasil uji coba, dapat diketahui aplikasi ini dapat mengambil berita dari provider-provider situs berita yang ditentukan sekaligus mengklasifikasikannya sesuai dengan yang diinginkan oleh pengguna.
Kata kunci : internet agent, web mining, text mining, fuzzy search, association rule

Dokumen teks tergolong dalam data tidak terstruktur. Jika dibandingkan dengan informasi yang tersimpan dalam bentuk yang terstruktur (misalnya pada tabel dalam sebuah database), maka data tidak terstruktur relatif lebih sulit dalam hal pengelolaan, penyimpanan, pencarian ulang maupun pengamanannya. Dalam paper ini dipaparkan sebuah metode indexing dan retrieval yang mampu menyimpan dokumen teks sebagai inverted index yang memiliki berbagai keunggulan penyimpanan data terstruktur.
Proses indexing melibatkan beberapa tahap yaitu parsing, stopping, stemming, sorting dan merging. Proses indexing dilakukan terhadap 6464 buah file txt dalam Alquran Terjemahan Indonesia. Setelah itu indeks yang dihasilkan digunakan dalam proses pencarian dokumen yang hasilnya dibandingkan dengan hasil pencarian dokumen konvensional secara full text search. Baik hasil pencarian maupun waktu yang dibutuhkan semuanya dicatat untuk mengukur performa retrieval engine dengan parameter precision, recall dan waktu.
Hasil pengujian menunjukkan bahwa proses indexing tidak mengurangi nilai recall, namun menurunkan nilai precision hingga 41,88% demi meningkatkan kecepatan pencarian hingga 3800 kali lipat.

Kata kunci : inverted index, indexing and retrieval, precision, recall

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.

Письменные источники - это документы, с которыми связана история как наука. Документы истории и история документов тесно переплетены. Автоматический контент-анализ и компьютерная обработка исторических текстов обрели свое собственное место в мире методов источниковедения и конкретно-исторического анализа.

There are two pre-nominal anaphoric demonstratives in Japanese: kono and sono. I had revealed some differences between the demonstratives in previous papers. Some of them are as follows: kono can be used in rewording, case in which the head of the antecedent and that of the anaphoric NP are different, while sono cannot; kono can be used globally in text but sono can only be used in an adjacent position; when the antecedent is a proper NP, sono is more statistically significantly used than kono. I pointed out in this paper one more important syntactic-semantic phenomenon: sono is more used with ga while kono more with wa (both in a statistically significant level). In sentence level, ga is a subject marker and wa marks the theme of sentence and the former indicates non-theme in text level. The finding of this paper is important because ga in such anaphoric use had been totally ignored. This paper explains the familiarity between kono and wa and that between sono and ga as follows: Since wa is the unmarked choice as a marker of anaphoric NP while ga is the marked one and kono is the unmarked demonstrative chosen in anaphoric usage while sono is marked, such properties in markedness combines kono with wa and sono with ga. This paper is one of the earliest contributions of Japanese text linguistics which has a sound refutability.

This study applied text mining techniques, machine learning approaches and statistical methods to construct a predictive model of a prioritized English vocabulary list to help nonnative English speakers prepare for college entrance English exams. Developing a method for efficiently learning English vocabulary in a limited time is an import issue. This study suggests that highly relevant and frequently repeated test items should be learned first. Although the College Entrance Examination Center (CEEC) in Taiwan has provided an approximately 7,000-word vocabulary list, the list's suitability requires verification. Furthermore, this study constructed a vocabulary learning process model to establish a prioritized English vocabulary list for future examinees. Experimental results show that the proposed model can achieve a 78% hit ratio, which is higher than the 69% of the CEEC's provided list.

With the advancement of technology and reduced storage costs, individuals and organizations are tending towards the usage of electronic media for storing textual information and documents. It is time consuming for readers to retrieve relevant information from unstructured document collection. It is easier and less time consuming to find documents from a large collection when the collection is ordered or classified by group or category. The problem of finding best such grouping is still there. This paper discusses the implementation of k-Means clustering algorithm for clustering unstructured text documents that we implemented, beginning with the representation of unstructured text and reaching the resulting set of clusters. Based on the analysis of resulting clusters for a sample set of documents, we have also proposed a technique to represent documents that can further improve the clustering result.

The study provides the frequency distribution of significant
terms over past time periods for text trend analysis
via an Indonisai newspaper. The approach consists
of two steps:(1) Data Preprocessing (2) significant
term extraction and term history generation. The former
adoptes agent techniques to download the news articles
autimatically and extracts the contents of these
articles. The later uses an existing external memory
approach to extract significant terms while computing
the term histroy simutaneously. One significant term,
in this study, is a series of words that were significant
enough to present one event, action or concept. The
term history of one term is the frequency distribution of
that term over consecutive time periods as a time series
data. The experimental resourse includes one year of
Indonesia newspaper, ”Serambi”, containg 28, 071 articles.
Experimental result shows that it is attractive and
meanful for foreigners who desire to know the trend
and situation happened in Aceh province of Indonesia,
where the majority of Serambi newspaper concerned
with.

Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from large amounts of data. The important term in data mining is text mining. Text mining extracts the quality information highly from text. Statistical pattern learning is used to high quality information. High –quality in text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language processing and analytical methods are highly preferred to turn text into data for analysis. This survey is about the various techniques and algorithms used in text mining.

Volcanic activity may influence climate parameters and impact people safety, and hence monitoring its characteristic indicators and their temporal evolution is crucial. Several databases, communications and literature providing data, information and updates on active volcanoes worldwide are available, and will likely increase in the future. Consequently, information extraction and text mining techniques aiming to efficiently analyze such databases and gather data and parameters of interest on a specific volcano can play an important role in this applied science field. This work presents a natural language processing (NLP) system that we developed to extract geochemical and geophysical data from free unstructured text included in monitoring reports and operational bulletins issued by volcanological observatories in HTML, PDF and MS Word formats. The NLP system enables the extraction of relevant gas parameters (e.g., SO2 and CO2 flux) from the text, and was tested on a series of 2839 daily and weekly bulletins published online between 2015 and 2021 for the Stromboli volcano (Italy). The experiment shows that the system proves capable in the extraction of the time series of a set of user-defined parameters that can be later analyzed and interpreted by specialists in relation with other monitoring and geospatial data. The text mining system can potentially be tuned to extract other target parameters from this and other databases.

Since its inception in 2001, the Center for Management and Strategic Studies (CGEE) has as its main activity the conduct of foresight studies in support of the decision making process related to the establishment of ST&I policies and activities in Brazil. The methodology used by the center combines quantitative and qualitative methods. Explicit and tacit knowledge are mobilized in the process of developing complementary or differentiated visions of the future.

Now a day's sentiment analysis performs a very vital role in text mining. In essence web mining is a very broad area in a data mining field for extracts the sentiment of the text. To identify the sentiment of the textual data is a very challenging task. The present work focuses on sentence level negation identification and calculation from the News articles and Blogs. Two step approaches generally used for analysis namely preprocessing and post processing. Preprocessing consists of the tasks like stop word removing, punctuation mark removal, number removal, white space removal etc. Post processing comprises identification of sentiments from the text and calculation of score. The work analyses the performance of support vector machine, Naïve Bayes for the dataset collected online.

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.

A huge array of personalized healthcare and wellness systems are introduced into the portfolio of digital health and quantified-self movement in recent years. These systems share common capabilities including self-tracking/monitoring and self-quantifications, based on the raw sensory data. These capabilities provide solid ground for the users to be more aware of their health; however, such measures are inefficient for changing the unhealthy habits of the users. In order to induce healthy habits in the users, a system must be capable of generating context-aware personalized recommendations. The main obstacle in this regard is the contextual interpretation of recommendations based on user's current context and contextual preferences. To resolve these issues, we propose a methodology of cross-context interpretation of recommendations (CCIR) for personalized health and wellness services. The CCIR method adds additional capabilities to the traditional reasoning methods and builds advanced form of the reasoning with the incorporation of contextual factors in the process of interpretations of the recommendations. With CCIR, the self-quantification systems can be enhanced to generate personalized recommendations in addition to tracking, quantifying, and monitoring user activities. In order to validate the proposed CCIR methodology, a set of 40 contextual scenarios and corresponding recommendations are presented for the evaluation collected from 40 different end users and 10 domain experts. Using chi-square test evaluation, the results demonstrated acceptable " goodness of fit " indices for the system developed on proposed CCIR methodology with respect to the end users' opinion. Also from the statistical observation, it is found that there exists a higher level agreement towards the system between the participants of both end users and experts.

Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative. Observations show that RF and SVM perform well in all types of testing. Naïve Bayes gives good result but not compared to the other two. Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in comparison with news random labelling with 50% of accuracy; the model has increased the accuracy by 30%.

Vector Space Model, Cosine Similarity, Part of Speech Tagging (POS - Tagging) Hidden Markov Model (HMM) Information Extraction dengan Algortima Naive Bayes Based NER dan Peringkasan Teks atau text summarization pada Text Mining Teknik Informatika Fakultas Ilmu Komputer Universitas Brawijaya

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 analyzed 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 analyze 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.

This paper analyses the features of Web Search Engines, Vertical Search Engines, Meta Search Engines,
and proposes a Meta Search Engine for searching and retrieving documents on Multiple Domains in the
World Wide Web (WWW). A web search engine searches for information in WWW. A Vertical Search
provides the user with results for queries on that domain. Meta Search Engines send the user’s search
queries to various search engines and combine the search results. This paper introduces intelligent user
interfaces for selecting domain, category and search engines for the proposed Multi-Domain Meta Search
Engine. An intelligent User Interface is also designed to get the user query and to send it to appropriate
search engines. Few algorithms are designed to combine results from various search engines and also to
display the results.

The survey conducted for this study reveals that more than 84% of respondents have never encountered the term " agile commerce " and do not understand its meaning. At the same time, they are active participants of this strategy. Using digital channels as customers more often than ever before, they have already been included in the agile philosophy. Based on the above, the purpose of the study is to analyse major text sets containing the " agile commerce " term, using the text data mining methods and tools. Data for analyses were sourced through creating an " agile commerce " query on websites with English as the content language. The texts retrieved in this way were used for building a corpus of documents. As a result of the corpus text exploration, words appearing most frequently in association with the agile commerce concept were separated and their most typical contexts were identified. The mining of unstructured data – the text mining process – revealed the essential meaning of the term being studied, while exploring knowledge from large information resources.

The world is intrigued by data. In fact, huge capitals are invested to devise means that implements statistics and extract analytics from these sources. However, when we examine the studies performed on applicant tracking systems that retrieve valuable information from candidates’ CVs and job-descriptions, they are mostly rule-based and hardly manage to employ contemporary techniques. Even though these documents vary in contents: the structure is almost identical. Accordingly, in this paper, we implement an NLP pipeline for the extraction of such structured information from a wide variety of textual documents. As a reference, textual documents which are used in applicant tracking systems like CV (Curriculum Vitae) and job vacancy information have been considered. The proposed NLP pipeline is built with several NLP techniques like document classification, document segmentation and text extraction. Initially for the classification of textual documents, Support Vector Machines (SVM) and XGBoost algorithms have been implemented. Different segments of the identified document are categorized using NLP techniques such as chunking, regex matching and POS tagging. Relevant information from every segment is further extracted using techniques like Named Entity Recognition (NER), regex matching and pool parsing. Extraction of such structured information from textual documents can help to gain insights and use those insights in document maintenance, document scoring, matching and auto filling forms.

Rich information is scattered under Indonesian Choral Lovers (ICL) mailing list and many of its members prefer posting a query-mail to using the available search engine. A text retrieval system based on ontology is then proposed. However, considering the continual number of emails, developing an automatic ontology constructor (OC) will be the focus of the study while the retrieval system becomes an evaluation tool of the OC developed. Besides using 3,000 emails of ICL as the corpus, this study is challenging for it takes a cognitive approach as the heart of the process and employs Linear Model known in Expert System field as the system life cycle. The effectiveness of OC will be determined based on the automatic-thesaurus effectiveness. Performance measure of information retrieval is going to be computed in technical evaluation while qualitative measures of ontology (consistency, completeness, and conciseness) are going to be used in user's evaluation.

Various embodiments of the present invention provide systems, methods, and computer programs for generating a hypothesis. Specifically, some method embodiments include steps for accessing a system for extracting relationships and determining a relationship rule defining a relationship among a plurality of phrases and a plurality of concepts stored in the system for extracting relationships. Such embodiments further provide steps for parsing a plurality of documents in a data repository according to the relationship rule and generating a hypothesis comprising a previously unknown combination of phrases and concepts being at least partially determined from the parsed plurality of documents. Various mbodiments also provide a step for presenting the hypothesis to a user so as to indicate the previously unknown combination.

Now a days, huge amount data has generated on the internet and it is important to extract useful information from that huge data. Different data mining techniques are used to extract and implement to solve divers types of problems. In the era of News and blogs, there is need to extract news and need to analyze to determine opinion of that news reviews. Sentiment analysis finds an opinion i.e. positive or negative about particular subject. Negation is a very common morphological creation that affects polarity and therefore, needs to be taken into reflection in sentiment analysis. Automatic detection of negation from News article is a need for different types of text processing applications including Sentiment Analysis and Opinion Mining. Our system uses online news databases from one resources namely BBC news. While handling news articles, we executed three subtasks namely categorizing the objective, separation of good and bad news content from the articles and performed preprocessing of data is cleaned to get only what is required for analysis, Steps like tokenization, stop word removal etc. The currently work focuses on different computational methods modeling negation in sentiment analysis. Especially, on aspects level of representation used for sentiment analysis, negation word recognition and scope of negation and identification.

Digital text documents are spread in various formats, the most widely used formats today include word format, and PDF format. This research will try to make text search application in text document using vector space approach model. The document format used is a PDF document. Text in PDF will be extracted and then made rank using vector space model. The PDF document consists of ten pages and each page contains a hadith. In general the system can search from the PDF document quite well and able to display the list of results in accordance with the relevance rank with the question.

I. Introduction: This documentation presents the installation notes for ARDA supervisor and analyst interfaces. The main feature of the supervisor interface is to allow a supervisor to assign a task to an analyst. A task is defined to be a set of analyst's name, project name, list of positive examples, and list of negative examples. Upon submission of the task to the web service, the service will invoke the classifier agent to generate a set of weight vectors that determine the permission of each document for the analyst. An analyst assigned to a particular task will be able to check his/her task on the analyst interface by logging in and clicking on the particular task from the list of tasks menu. The task will open with all the documents within the communities of interest that the analyst is assigned to. As the analyst clicks on each document, the authorization agent will determine whether it is viewable to the analyst based on the generated weight vectors. We have shown the accuracy of the classifier with minimal sets of positive and negative examples and the performance of it.

Knowledge about climate change is very important in human life. The information gained can be used to learn about time period of weather season from January to December. Indonesia is a tropical country with two weather seasons, they are summer and rainy weather. Weather information and forecasting are usually reported by Indonesian Agency for Meteorology, Climatology and Geophysics, BMKG (Badan Meteorologi Klimatologi dan Geofisika), every day on television. The method used by BMKG i.e. using weather sensor equipment placed in several locations to grab the weather data. This study also grabs the weather data by mining a text in local electronic newspaper. The idea of this research came from reading a few local newspaper articles that tells about impact of weather event such as droughts, floods, crop failure, somewhere then we know each article has timestamp such as the date, month, and year. Therefore by looking “the word that tells about impact of weather event” in electronic newspaper article, from January to December, then we know weather conditions. Those word called weather significant terms. To gain this information, we classified several word which can represent summer and rainy weather condition. These words were collected from three stages of text mining process: (i) Data preprocessing; (ii) Sorting suffixes and saving into file; (iii) Extracting weather significant term candidate along with timestamp. Weather significant term and their timestamps will be stored in a database. Later, an application will query the database to find information of weather season words (weather significant terms). The pattern will use month as the interval period. By entering some weather significant terms related to summer and rainy weather season into application, the application will produce graph explaining summer and rainy weather season pattern period in Aceh province.

We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%.

The world is intrigued by data. In fact, huge capitals are invested to devise means that implements statistics and extract analytics from these sources. However, when we examine the studies performed on applicant tracking systems that retrieve valuable information from candidates’ CVs and job-descriptions, they are mostly rule-based and hardly manage to employ contemporary techniques. Even though these documents vary in contents: the structure is almost identical. Accordingly, in this paper, we implement an NLP pipeline for the extraction of such structured information from a wide variety of textual documents. As a reference, textual documents which are used in applicant tracking systems like CV (Curriculum Vitae) and job vacancy information have been considered. The proposed NLP pipeline is built with several NLP techniques like document classification, document segmentation and text extraction. Initially for the classification of textual documents, Support Vector Machines (SVM) and XGBoost algorithms have been implemented. Different segments of the identified document are categorized using NLP techniques such as chunking, regex matching and POS tagging. Relevant information from every segment is further extracted using techniques like Named Entity Recognition (NER), regex matching and pool parsing. Extraction of such structured information from textual documents can help to gain insights and use those insights in document maintenance, document scoring, matching and auto filling forms.

The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world

[The document is in portuguese] O imenso volume de documentos textuais armazenados em meios eletrônicos e disponíveis em repositórios públicos ou internamente em uma organização são fontes importantes de conhecimento. As áreas de Inteligência Competitiva e Business Intelligence procuram explorar estas informações para descobrir conhecimento novo e útil. Para auxiliar os processos de " garimpagem " de informações em textos, surgiram técnicas e ferramentas para descoberta de conhecimento em textos. Neste artigo, é apresentada uma estratégia de descoberta de conhecimento em textos para auxiliar os trabalhos de análise em Inteligência Competitiva. A estratégia apresentada está voltada para processos proativos. Nesta classe de sistemas, encontram-se aqueles que iniciam a busca sem hipóteses ou sem um objetivo completamente definido e que contam com a ação efetiva do analista interagindo com o sistema computacional. Neste tipo de processo, o usuário tem problemas que quer resolver mas não sabe exatamente por onde começar ou que tipo de conhecimento lhe pode ser útil. Neste artigo são apresentados exemplos de aplicação utilizando a estratégia proposta e discutidos aspectos que podem influenciar o processo de descoberta.

Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. However, there is a lack of publicly available data for doing research on the interaction between language and depression. In this paper, we describe our first steps to fill this gap. We outline the methodology we have adopted to build and make publicly available a test collection on depression and language use. The resulting corpus includes a series of textual interactions written by different subjects. The new collection not only encourages research on differences in language between depressed and non-depressed individuals, but also on the evolution of the language use of depressed individuals. Further, we propose a novel early detection task and define a novel effectiveness measure to systematically compare early detection algorithms. This new measure takes into account both the accuracy of the decisions taken by the algorithm and the delay in detecting positive cases. We also present baseline results with novel detection methods that process users' interactions in different ways.