N-gram Method Research Articles

Detection of COVID-19 Anti-Vaccination from Twitter Data Using Deep Learning and Feature Selection Approaches

The COVID-19 pandemic has evolved into a crisis significantly impacting health, the economy, and social life worldwide. During this crisis, anti-vaccination sentiment poses a considerable obstacle to controlling the epidemic and the effectiveness of vaccination campaigns. This study aimed to detect COVID-19 anti-vaccination sentiment from Twitter data using a combination of deep learning and feature selection approaches. The proposed method integrates a deep learning model with feature selection techniques to identify anti-vaccination sentiment by pinpointing important features in text data. Hybrid TF-IDF and N-gram methods were utilized for feature extraction, followed by Chi-square feature selection. The dataset comprises Twitter text data and two labels. The Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the labels. Long Short-Term Memory (LSTM), a deep learning architecture, was employed for the classification process. The experimental results, obtained by leveraging the proposed feature extraction, feature selection, and LSTM methods, achieved the highest accuracy value of 99.23%. These findings demonstrate the proposed methods' success in effectively detecting COVID-19 anti-vaccination sentiment in text data. The study's results can offer valuable insights for developing health policies and public information strategies, presenting a new and powerful tool for detecting anti-vaccine sentiment in planning vaccination campaigns and public health interventions.

Polish Word Recognition Based on n-Gram Methods

Polish Word Recognition Based on n-Gram Methods

Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining

The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.

COMPARISON OF SPELL CORRECTION IN BAHASA INDONESIA: PETER NORVIG, LSTM, AND N-GRAM

This study conducts a comprehensive comparison of spell-checking methods in Bahasa Indonesia, specifically focusing on three approaches: Peter Norvig's method, Long Short-Term Memory (LSTM), and N-gram. The primary metric for evaluation is the accuracy in correcting spelling errors. Notably, Peter Norvig's method outperforms the others, with N-gram following closely, and LSTM trailing behind. The study draws valuable insights that contribute to the enhancement of spelling correction accuracy in the Bahasa Indonesia language. To carry out the evaluation, the research employs SPECIL data (Spell Error Corpus for Indonesian Language), which includes documents with various error types such as insertion, deletion, transposition, and substitution. The testing dataset consists of 150 words, aligning with the 150-word corpus references from the 'Leipzig Corpora Collection' used for Peter Norvig's and N-gram methods. It's noteworthy that the LSTM method utilizes a reference dataset from SPECIL, comprising 150 data points and specifically focusing on insertion errors for the test data. This research provides valuable insights for researchers, developers, and language technology enthusiasts seeking to refine spell-checking techniques for the Bahasa Indonesia language. By leveraging diverse error types and a standardized testing dataset, the study aims to contribute to the continual improvement of spell-checking tools

Analisis Emosi pada Media Sosial Twitter Menggunakan Metode Multinomial Naive Bayes dan Synthetic Minority Oversampling Technique

Twitter social media is often used to express one's emotions through tweets. Much research has been conducted on emotional analysis in the social media Twitter. Machine learning is a tool that is widely used to categorize emotions. However, an imbalance in the amount of data between classes is often a problem. So, this research aims to determine the performance of the combined Multinomial Naïve Bayes (MNB) and Synthetic Minority Oversampling Technique (SMOTE) methods for emotional analysis of tweets from the social media Twitter. Each tweet through data preprocessing in this research includes case folding, data cleaning, convert slangword, convert negation, tokenization, stopword removal, and stemming. For feature extraction the n-gram method is used and for feature weighting the term frequency method is used. Testing was carried out using K-Fold Cross Validation. Based on the test results, using SMOTE an average accuracy of 0.65 or 65% was obtained and an average f1-score value of 0.66 or 66%. Meanwhile, without SMOTE, an average accuracy of 0.64 or 64% was obtained and an average f1-score of 0.65 or 65%. Although in this study it can be shown that the results using SMOTE are 1% better in categorizing emotions. However, the results obtained are not optimal, and other methods of data balancing and machine learning still need to be studied.

A study on methods for revising dependency treebanks: in search of gold

Reliably annotated corpora with reliable annotation are a valuable resource for Natural Language Processing, which justifies the search for methods capable of assisting linguistic revision. In this context, we present a study on methods for revising dependency treebanks, investigating the contribution of three different strategies to the corpus review: (i) linguistic rules; (ii) an adaptation of the n-grams method proposed by Boyd et al. (2008) applied to Portuguese; and (iii) Inter-Annotator Disagreement, a linguistically motivated approach that draws inspiration from the human annotation process. The results are promising, and taken together the three methods can lead to the revision of up to 58% of the errors in a specific corpus at the cost of revising only 20% of the corpus. We also present a tool that integrates treebank editing, evaluation and search capabilities with the review methods, as well as a gold-standard Portuguese corpus from the oil and gas domain.

Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos

Writing errors on e-essay exams reduce scores. Thus, detecting and correcting errors automatically in writing answers is necessary. The implementation of Levenshtein Distance and N-Gram can detect writing errors. However, this process needed a long time because of the distance method used. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically. This process required preprocessing and finding the best word recommendations by the Jaro Winkler method, which refers to Kamus Besar Bahasa Indonesia (KBBI). The N-Gram method refers to the corpus. The final scoring used the Vector Space Model (VSM) method based on the similarity of words between the answer keys and the respondent’s answers. Datasets used 115 answers from 23 respondents with some writing errors. The results of Jaro Winkler and N-Gram methods are good in detecting and correcting Indonesian words with the accuracy of detection averages of 83.64% (minimum of 57.14% and maximum of 100.00%). In contrast, the error correction accuracy averages 78.44% (minimum of 40.00% and maximum of 100.00%). However, Natural Language Processing (NLP) needs to improve these results for word recommendations.

Dzongkha Next Words Prediction Using Bidirectional LSTM

Dzongkha Development Commission of Bhutan (DDC) is trying to computerize Dzongkha. However, the computerization of Dzongkha poses numerous challenges. Currently, the support for Dzongkha in modern technology is limited to printing, typing, and storage. Typewriting a single Dzongkha word requires several keypresses. As a result, typing Dzongkha is tedious. In this paper, the Dzongkha word label prediction was studied. The purpose of the study was to further reduce keystrokes and make Dzongkha typing much faster. The dataset encompasses different genres curated by DDC. The dataset consisted of 10000 sentences and 4820 unique words. Next, 52150 sequences were generated using N-gram methods followed by vectorizing text using embedding techniques. Different RNN-based models were evaluated for the next Dzongkha words prediction. Two Bi-LSTM layers with 512 hidden layer neurons gave the best accuracy of 73.89% with a loss of 1.0722.

Deep Learning Algorithm for Detection of Protein Remote Homology

The study aims to find a successful solution by using computer algorithms to detect remote homologous proteins, which is a significant problem in the bioinformatics field. In this experimental study, structural classification of proteins (SCOP) 1.53, SCOP benchmark, and the newly created SCOP protein database from the structural classification of proteins—extended (SCOPe) 2.07 were used to detect remote homolog proteins. N-gram method and then Term Frequency-Inverse Document Frequency (TF-IDF) weighting were performed to extract features of the protein sequences taken from these databases. Next, a smoothing process on the obtained features was performed to avoid misclassification. Finally, the proteins with balanced features were classified into remote homologs using the built deep learning architecture. As a result, remote homologous proteins have been detected with novel deep learning architecture using both negative and positive protein instances with a mean accuracy of 89.13% and a mean relative operating characteristic (ROC) score of 88.39%. This experiment demonstrated the following: 1) The successful outcome of this study in detecting remote homology is auspicious in discovering new proteins and thus in drug discovery in medicine. 2) Natural language processing (NLP) techniques were used successfully in bioinformatics, 3) the importance of choosing the correct n-value in the n-gram process, 4) the necessity of using not only positive but negative instances in a classification problem, and 5) how effective the processes, such as smoothing, are in the classification accuracy in an imbalanced dataset. 6) The deep learning architecture gives better results than the support vector machine (SVM) model on the smoothed data to detect proteins’ remote homology.

A Multiclass Approach to Estimating Software Vulnerability Severity Rating with Statistical and Word Embedding Methods

The analysis and grading of software vulnerabilities is an important process that is done manually by experts today. For this reason, there are time delays, human errors, and excessive costs involved with the process. The final result of these software vulnerability reports created by experts is the calculation of a severity score and a severity rating. The severity rating is the first and foremost value of the software’s vulnerability. The vulnerabilities that can be exploited are only 20% of the total vulnerabilities. The vast majority of exploitations take place within the first two weeks. It is therefore imperative to determine the severity rating without time delays. Our proposed model uses statistical methods and deep learning-based word embedding methods from natural language processing techniques, and machine learning algorithms that perform multi-class classification. Bag of Words, Term Frequency Inverse Document Frequency and Ngram methods, which are statistical methods, were used for feature extraction. Word2Vec, Doc2Vec and Fasttext algorithms are included in the study for deep learning based Word embedding. In the classification stage, Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi-Layer Perceptron, and Random Forest algorithms that can make multi-class classification were preferred. With this aspect, our model proposes a hybrid method. The database used is open to the public and is the most reliable data set in the field. The results obtained in our study are quite promising. By helping experts in this field, procedures will speed up. In addition, our study is one of the first studies containing the latest version of the data size and scoring systems it covers.

Research on fake online review recognition method based on feature engineering

In the context of the explosive growth of online reviews in e-commerce, some merchants began to hire consumers to make fake purchases to increase sales, which caused the difficulty in identifying fake reviews. Therefore, from the perspective of consumers’ shopping behavior, a feature recognition method based on comment text, reviewer attribute and reviewer’s shopping behavior (referred to as TAB) is proposed to solve the problem of fake reviews caused by fake shopping behavior. Compared with BOW, TF-IDF, N-gram, FSP and other traditional feature extraction methods, experiments were carried out on Naive Bayes, SVM, Logistic Regression, Random Forest, CNN and other classification models. By changing the data dimension, the stability of each false comment recognition model was analyzed. The experimental results show that the TAB feature recognition method has better classification performance on logistic regression and support vector machine models, and the model is relatively stable, and the classification effect is not easily affected by dimension change.

The Use of N-Gram Language Model in Predicting Nepali Words

This paper aims to study the problems of automated generation and understanding of natural human languages. The word prediction and word completion from a tab-complete in typing is particularly useful to minimized keystrokes for the users with specific necessaries, and to reduce mistakes, and typographic errors. The word prediction techniques are well-established methods that are frequently used as communication aids for people with disabilities to accelerate the writing, to reduce the effort needed to type and to suggest the correct words. It is something that is skillful at doing prediction according to the previous context. Projection can either be established on word figures or verbal rules. The N-gram model is about predicting nth word from N-1 words. It assigns the probabilities to sentences and sequences of words of all possible combination of n words. To meet the objective, this research uses statistics amount of Nepali language of diverse word kinds to expect right word with as much precision as possible. Under the statistical method, this research will deal with the N-gram method to predict the next word for the Nepali language using Viterbi as decoding algorithm.

Text Mining for Employee Candidates Automatic Profiling Based on Application Documents

Opening job vacancies using the Internet will receive many applications quickly. Manually filtering resumes takes a lot of time and incurs huge costs. In addition, this manual screening process tends to be inaccurate due to fatigue conditions and fails in obtaining the right candidate for the job. This paper proposed a solution to automatically generate the most suitable candidate from the application document. In this study, 126 application documents from a private company were used for the experiment. The documents consist of 41 documents for Human Resource and Development (HRD) staff, 42 documents for IT (Data Developer), and 43 documents for the Marketing position. Text Processing is implemented to extract relevant information such as skills, education, experiences from the unstructured resumes and summarize each application. A specific dictionary for each vacancy is generated based on terms used in each profession. Two methods are implemented and compared to match and score the application document, namely Document Vector and N-gram analysis. The highest the score obtained by one document, the highest the possibility of application to be accepted. The two methods’ results are then validated by the real selection process by the company. The highest accuracy was achieved by the N-Gram method in IT vacancy with 87,5%, while the Document Vector showed 75% accuracy. For Marketing staff vacancy, both methods achieved the same accuracy as 78%. In HRD staff vacancy, the N-Gram method showed 68%, while Document Vector showed 74%. In conclusion, overall the N-gram method showed slightly better accuracy compared to the Document Vector method.

Исследование применения графов знаний для генерации вопросов аудита информационной безопасности

The article presents approaches to research on the use of knowledge graphs to generate information security audit issues. The significance of the human factor in the audit of information security as an element of socially significant activity is shown. As a solution to the formation of an objective research base, a method of automatically generating questions based on knowledge graphs capable of identifying a set of cartoons that are subject-object relations is proposed. Existing models and a set of dynamically generated metrics were analyzed. In particular, the subjective method of the five-point Makoto Nagao scale and the machine method of the automatic evaluation system based on the N-gram method. Advantages and disadvantages of research methods are analyzed, task setting for further research is presented.

SUPPORT OF INFORMAL CARERS FOR PEOPLE AFTER A STROKE WITH CROWDSOURCING AND NATURAL LANGUAGE PROCESSING

For a long time, both professionals and the lay public showed little interest in informal carers. Yet these people deals with multiple and common issues in their everyday lives. As the population is aging we can observe a change of this attitude. And thanks to the advances in computer science, we can offer them some effective assistance and support by providing necessary information and connecting them with both professional and lay public community. In this work we describe a project called “Research and development of support networks and information systems for informal carers for persons after stroke” producing an information system visible to public as a web portal. It does not provide just simple a set of information but using means of artificial intelligence, text document classification and crowdsourcing further improving its accuracy, it also provides means of effective visualization and navigation over the content made by most by the community itself and personalized on a level of informal carer’s phase of the care-taking timeline. In can be beneficial for informal carers as it allows to find a content specific to their current situation. This work describes our approach to classification of text documents and its improvement through crowdsourcing. Its goal is to test text documents classifier based on documents similarity measured by N-grams method and to design evaluation and crowdsourcing-based classification improvement mechanism. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful.

Anomaly Detection in Arabic Texts using Ngrams and Self Organizing Maps

Every written text in any language has one author or more authors (authors have their individual sublanguage). An analysis of text if authors are not known could be done using methods of data analysis, data mining, and structural analysis. In this paper, two methods are described for anomaly detections: ngrams method and a system of Self-Organizing Maps working on sequences built from a text. there are analyzed and compared results of usable methods for discrepancies detection based on character n-gram profiles (the set of character n-gram normalized frequencies of a text) for Arabic texts. Arabic texts were analyzed from many statistical characteristics point of view. We applied some heuristics for measurements of text parts dissimilarities. We evaluate some Arabic texts and show its parts they contain discrepancies and they need some following analysis for anomaly detection. The analysis depends on selected parameters prepared in xperiments. The system is trained to input sequences after which it determines text parts with anomalies using a cumulative error and winner analysis in the networks. Both methods have been tested on Arabic texts and they have a perspective contribution to text analysis.

Semi supervised machine learning approach for DDOS detection

The appearance of malicious apps is a serious threat to the Android platform. In this paper, we propose an effective and automatic malware detection method using the text semantics of network traffic. In particular, we consider each HTTP flow generated by mobile apps as a text document, which can be processed by natural language processing (NLP) to extract text-level features. Later, the use of network traffic is used to create a useful malware detection model. We examine the traffic flow header using the N-gram method from the NLP. Then, we propose an automatic feature selection algorithm based on the Chi-square test to identify meaningful features. It is used to determine whether there is a significant association between the two variables. We propose a novel solution to perform malware detection using NLP methods by treating mobile traffic as documents. We apply an automatic feature selection algorithm based on the N-gram sequence to obtain meaningful features from the semantics of traffic flows. Our methods reveal some malware that can prevent the detection of antiviral scanners. In addition, we design a detection system to drive traffic to your own-institutional enterprise network, home network, and 3G/4G mobile network. Integrating the system connected to the computer to find suspicious network behaviors. Keywords: Semi supervised, machine, learning approach, detection, android platform.

ANALYSIS OF SOME CONCEPTS RELATED TO THE ENVIRONMENT AND HEALTH WITH THE N-GRAM METHOD

Problem of the Study: Environmental health affects the quality of life. Therefore, there is a close relationship between the environment and health, and it is important to perceive, consider and understand this relationship together.
 Purpose of the Study: The purpose of this study is to examine the relationship between the change in the use of environmental and health-related concepts and the change in social, economic and environmental factors, using the n-gram method.
 Method: In this study, the n-gram analysis was used. Some concepts such as mining, industrialization, urbanization, environment, environmental factors, environmental conditions, environmental protection, environmental issues, environmental issues, environmental impact, environmental policy, environmental quality, medical geology, public health, etc., were studied. The historical development process of the concepts has been extracted and interpreted through the n-gram graph.
 Findings and Results: The concept of mining in the literature showed a continuous increase after the 1820s. The downward trend that started in the 1920s started to rise again after the second half of the 1920s, but after the 1980s it started to fall again.
 The concept of public health, which came to the fore in the 1880s, was frequently used in the 1940s in the literature. The fact that the concept of industrialization has always been on the rise after the 1920s can be explained by the industrial development moves of the countries that gained their independence in the First World War.
 Concepts related to the environment have increased starting from the 1900s. Some of these concepts started to be used in the literature in the 1950s.
 When the findings obtained in the study are evaluated together, it can be said that the prevalence of the use of environmental and health-related concepts in social life has increased especially after the 1950s.

A Disease Interaction Retrieval and Recommendation System based on NLP Technology

There is a great request for a Chatbot system that uses natural language processing for disease retrieval and recommendation in medical treatment with interactive functions. This paper uses cMedQA2 data for training, developing and testing. The Chinese word segmentation tool jieba to reassemble the text data of consecutive words into the word sequences, and uses Bag of Words to count the frequency of keywords appearing in sentences, and uses N-gram method to predict whether the evaluation and prediction of a sentence is reasonable. This paper proposes an improved TextCNN network framework structure, which uses overlap technology to ensure the contextual coherence of information. The final experimental results show that there are 96.2% on the training dataset and 87.5% on the testing dataset.

PENGGUNAAN SPELLING CORRECTION DENGAN METODE PETER NORVIG DAN N-GRAM

Typing errors in a document are human errors that are difficult to avoid as a result of which the message to be conveyed is not optimal. Using the Spelling Corrector feature is one way to check typing errors. The Spelling Corrector feature is able to provide only one word suggestion and correct it immediately. The combination of Peter Norvig and Ngram's methods was able to come up with one word suggestion and correct it right away. Both methods of looking for word suggestions use the probability value of the words that appear most frequently in the dictionary. The difference between the two methods is that Peter Norvig uses an algorithm that combines the delete, insert, replace, and transpose processes for the wrong word. Meanwhile, Ngram uses an algorithm that pays attention to the words before and after them based on the sentences in the dictionary. The dictionary used is a dictionary of word lists from KBBI and also a dictionary of sentences taken from several articles on the internet. This combination of methods was tested using 55 documents containing sentences that had one misspelled word. The test results show that the combination of the two methods provides an accuracy rate of 73,684% and a success rate of 37,037% for the total accuracy of this application is 69.09%. These two methods can be used to correct typing errors, although they cannot correct words with an error rate of two or more letters. This is because Peter Norvig was unable to correct a word with a two-letter error rate and needed a good corpus.