TF-IDF Technique Research Articles

Detecting refactoring type of software commit messages based on ensemble machine learning algorithms

Refactoring is a well-established topic in contemporary software engineering, focusing on enhancing software's structural design without altering its external behavior. Commit messages play a vital role in tracking changes to the codebase. However, determining the exact refactoring required in the code can be challenging due to various refactoring types. Prior studies have attempted to classify refactoring documentation by type, achieving acceptable results in accuracy, precision, recall, F1-Score, and other performance metrics. Nevertheless, there is room for improvement. To address this, we propose a novel approach using four ensemble Machine Learning algorithms to detect refactoring types. Our experimentation utilized a dataset containing 573 commits, with text cleaning and preprocessing applied to address data imbalances. Various techniques, including hyperparameter optimization, feature engineering with TF-IDF and bag-of-words, and binary transformation using one-vs-one and one-vs-rest classifiers, were employed to enhance accuracy. Results indicate that the experiment involving feature engineering using the TF-IDF technique outperformed other methods. Notably, the XGBoost algorithm with the same technique achieved superior performance across all metrics, attaining 100% accuracy. Moreover, our results surpass the current state-of-the-art performance using the same dataset. Our proposed approach bears significant implications for software engineering, particularly in enhancing the internal quality of software.

TEXT MINING UNTUK ANALISIS SENTIMEN REVIEW FILM MENGGUNAKAN ALGORITMA NAÏVE BAYES

In its development, information technology brought about many changes and advances in the context of everyday life. One of the benefits of information technology to process large amounts of data is text mining. A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. Movie comments are many and varied on the IMDB website, we can see comments based on the movie title. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users and will be classified using the naïve bayes classifier method. Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. Naïve Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. In addition to the Naïve Bayes Classifier, TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the Naïve Bayes method are 87,2% accuracy, 92,4% recall, 80,9% precision, and 12,8% error rate. Keywords : Classification, IMDb, Naïve Bayes, Sentiment Analysis

Unveiling ChatGPT text using writing style

Extensive use of AI-generated texts culminated recently after the advent of large language models. Although the use of AI text generators, such as ChatGPT, is beneficial, it also threatens the academic level as students may resort to it. In this work, we propose a technique leveraging the intrinsic stylometric features of documents to detect ChatGPT-based plagiarism. The stylometric features were normalized and fed to classical classifiers, such as k-Nearest Neighbors, Decision Tree, and Naïve Bayes, as well as ensemble classifiers, such as XGBoost and Stacking. A thorough examination of the classifier was conducted by using Cross-Fold validation, hyperparameters tuning, and multiple training iterations. The results show the efficacy of both classical and ensemble learning classifiers in distinguishing between human and ChatGPT writing styles with a noteworthy performance of XGBoost where 100 % was achieved for accuracy, recall, and precision metrics. Moreover, the proposed XGBoost classifier outperformed the state-of-the-art result on the same dataset and same classifier highlighting the superiority of the proposed feature style extraction method over TF-IDF techniques. The ensemble learning classifiers were also applied to the generated dataset with mixed texts, where paragraphs are written by ChatGPT and humans. The results show that 98 % of the documents were classified correctly as either mixed or human. The last contribution consists in the authorship attribution of the paragraphs of a single document where the accuracy reached 92.3 %.

텍스트 마이닝을 활용한 중학교 음악과 교육과정 원문 분석

The purpose of this study is to examine the characteristic changes in music curriculum over time using scientific analysis methods. To this end, the original texts of the Middle School music curriculum from the revised 2007 to the revised 2022 were analyzed using the TF-IDF technique of Text Mining, a big data analysis method based on artificial intelligence, to extract the core concepts of each curriculum and to examine the trends. As a result of the analysis, the following conclusions were drawn. First, Korean music curriculum have been in a state of flux, reflecting various factors such as global trends in music education, education policies, and needs of the educational community. Second, the paradigm of ‘curriculum reconceptualization’ has been increasingly accepted over time. Third, text mining can be used to analyze and visualize curricula in an objective way, making it easy to intuitively understand curriculum changes over time. It can be a useful tool for curriculum-related research.

Analysis of SMS Spam Detection using Tf-Idf: A Study On SMS Spam Collection Dataset

This study explores the detection of SMS spam utilizing TF-IDF analysis on a dataset containing a collection of text messages labeled as spam or ham (non-spam). The dataset comprises messages suitable for spam detection analysis using TF-IDF techniques. The research aims to evaluate the effectiveness of TF-IDF in distinguishing between spam and spam (non-spam) messages. The analysis involves examining the precision, recall, and F1-score metrics to assess the performance of the classification model. The results demonstrate promising outcomes, with a high accuracy rate achieved in classifying spam and ham (non-spam) messages. Additionally, the study provides insights into the distribution of spam and ham (non-spam) labels in the test data, further enhancing our understanding of SMS spam detection techniques.

Deep learning-based approaches for abusive content detection and classification for multi-class online user-generated data

With the rapid growth of social media culture, the use of offensive or hateful language has surged, which necessitates the development of effective abusive language detection models for online platforms. This paper focuses on developing a multi-class classification model to identify different types of offensive language. The input data is taken in the form of labeled tweets and is classified into offensive language detection, offensive language categorization, and offensive language target identification. The data undergoes pre-processing, which removes NaN value and punctuation, as well as performs tokenization followed by the generation of a word cloud to assess data quality. Further, the tf-idf technique is used for the selection of features. In the case of classifiers, multiple deep learning techniques, namely, bidirectional gated recurrent unit, multi-dense long short-term memory, bidirectional long short-term memory, gated recurrent unit, and long short-term memory, are applied where it has been found that all the models, except long short-term memory, achieved a high accuracy of 99.9 % for offensive language target identification. Bidirectional LSTM and multi-dense LSTM obtained the lowest loss and RMSE values of 0.01 and 0.1, respectively. This research provides valuable insights and contributes to the development of effective abusive language detection methods to promote a safe and respectful online environment. The insights gained can aid platform administrators in efficiently moderating content and taking appropriate actions against offensive language.

Anomalous traffic identification method for post messages based on gambling website templates

Malicious websites pose significant social risks, necessitating automatic, efficient, and accurate identification methods. This paper proposes a POST traffic classification method based on website templates to identify abnormal traffic from gambling websites. Using Fiddler, POST message data is collected from several gambling sites, extracting features like URLs, cookie parameters, and request body parameters to create a Gambling Website Single POST Message Dataset (GSPD). These features are converted into vector representations withWord2Vec and TF-IDF techniques. Hierarchical clustering identifies template-generated types, achieving unsupervised template recognition. Using clustering results, individual POST messages are labeled and features are extracted using TF-IDF and mutual information methods. The parameters of a Support Vector Machine (SVM) are then optimized with the Particle Swarm Optimization (PSO) algorithm for optimal classification. Experimental results show the model?s excellent performance, with a test set accuracy of 0.9985 and high precision, recall, and F1-scores, effectively identifying gambling and other illegal websites.

Twitter-sentiment analysis of Moroccan diabetic using Fuzzy C-means SMOTE and deep neural network

Effectively managing diabetes as a lifestyle condition involves fostering awareness, and social media is a powerful tool for this purpose. Analyzing the content of tweets on platforms like Twitter can greatly inform health communication strategies aimed at raising awareness about diabetes within the Moroccan community. Unfortunately, the corpus of tweets is imbalanced and the feature extraction leads to data sets with a very high dimension which affects the quality of sentiment analysis. This study focused on analyzing the content, sentiment, and reach of tweets specifically related to diabetes in Morocco. The proposed strategy processes in five steps: (a) data collection from Twitter platforms and manual labilization, (b) feature extraction using TF-IDF technique, (c) dimension reduction using deep neural network, (d) data balancing using Fuzzy C-Means SMOTE, and (e) tweets classification using five well-known classifiers. The proposed approach was compared with the classic system, which works directly on very large, unbalanced tweets. In terms of recall, precision, F1-score, and CPU time, the proposed system can perform highly accurate sentiment analysis in a reasonable CPU time.

Lexicon-based Language Relatedness Analysis

The field of computational linguistics has been impacting various issues in language disciplines. The enormous growth of machine learning algorithms and Natural Language Processing (NLP) empowers its advancement and brings huge benefits to societies. For instance, machine translation, text summarization, sentence auto-completion, and sentiment analysis are a few of its benefits. However, leveraging this opportunity for low-resourced languages is challenging due to the lack of available electronic datasets. This paper presents a lexicon-based language relatedness analysis on Ethiopian low-resourced languages. The languages Wolaita, Dawuro, Gamo, and Gofa belong to the Ethiopian Omotic language family and share rich linguistic cultures and similarities. However, the extent of their inter-relatedness remains unknown. To address this gap, we collected and prepared novel corpora from the Bible and academic texts. We employed the TF-IDF technique for feature extraction and used the cosine similarity method to measure the similarities among these languages. In addition to cosine similarity, we used Euclidean distance to measure the spatial distances between the languages. The experiment results showed that Wolaita and Gofa exhibited high relatedness (33.4%), while Dawuro and Gamo demonstrated low relatedness (12.1%).

Using the Term Frequency-Inverse Document Frequency for the Problem of Identifying Shrimp Diseases with State Description Text

With the increasing demand for research on shrimp disease recognition to assist far-off farmers who need the proper assistance for their shrimp farming, shrimp disease prediction research is still in the initial stage. Most current methods utilize vision-based models, which mainly face challenges: symptom detection and image quality. Meanwhile, there are few researches which are language-based to get over the issues. In this study, we will experiment with natural language processing based on recognizing shrimp diseases; based on descriptions of shrimp status. This study provides an efficient solution for classifying multiple diseases in shrimp. We will compare different machine learning models and deep learning models (SVM, Logistic Regression, Multinomial Naive Bayes, (a4) Bernoulli Naive Bayes, Random forest, DNN, LSTM, GRU, BRNN, RCNN) in terms of accuracy and performance. The study also evaluates the TF-IDF technique in feature extraction. Data were collected for 12 types of shrimp diseases with 1,037 descriptions. Firstly, the data is preprocessed with standardised Vietnamese accent typing, tokenized words, converted to lowercase, removed unnecessary characters and stopwords. Then, TF-IDF is utilized to express the text feature weight. Machine learning-based and deep learning-based models are trained. The experimental results show that Random forest (F1-Score micro: 98%) and DNN (Validation accuracy: 84%) are the most efficient models.

Application of a Combined Approach of Text Mining and QFD Methodology Based on Single Valued Neutrosophic Numbers for Efficient Curriculum Design

In this study, an alternate curriculum design for an undergraduate program of Statistics is suggested carrying out a combined approach of the QFD methodology, text mining techniques under single valued neutrosophic set environment. To capture the employers’ expectations from their potential employees, 640 job advertisements, obtained from two of the most important career and job posting sites in Turkey, were analyzed using TF-IDF technique, which is one of the text mining methods. By using single-valued neutrophic set (SVNS) theory in QFD, the technical requirements representing the courses included in the curriculum were found their priorities. Hence, the technical characteristics that play a critical role in evaluating the curriculum quality of the undergraduate program were revealed. In addition, single valued neutrosophic sets have provided a flexible decision-making procedure to improve the quality of individuals’ subjective assessments. Consequently, this is expected to be a good reference for researchers working on these issues, both in terms of the proposed approach and the problem addressed.

Text Classification of News Using Transformer-based Models for Portuguese

This work proposes the use of a fine-tuned Transformers-based Natural Language Processing (NLP) model called BERTimbau to generate the word embeddings from texts published in a Brazilian newspaper, to create a robust NLP model to classify news in Portuguese, a task that is costly for humans to perform for big amounts of data. To assess this approach, besides the generation of embeddings by the fine-tuned BERTimbau, a comparative analysis was conducted using the Word2Vec technique. The first step of the work was to rearrange news from nineteen to ten categories to reduce the existence of class imbalance in the corpus, using the K-means and TF-IDF techniques. In the Word2Vec step, the CBOW and Skip-gram architectures were applied. In BERTimbau and Word2Vec steps, the Doc2Vec method was used to represent each news as a unique embedding, generating a document embedding for each news. Metrics accuracy, weighted accuracy, precision, recall, F1-Score, AUC ROC and AUC PRC were applied to evaluate the results. It was noticed that the fine-tuned BERTimbau captured distinctions in the texts of the different categories, showing that the classification model based on this model has a superior performance than the other explored techniques.

Investigation of the applicability of natural language processing methods to problems of searching and matching of machinery drawing images

In this work it is shown that the application of the technique of local feature descriptors in its pure form to the task of searching and matching of drawings is ineffective. It is revealed that this is mainly due to the presence in the drawings of a large number of identical elements (frames, a title block, extension lines, font elements, etc.). It is proposed that this problem should be solved using a tf-idf (term frequency-inverse document frequency) method, which is widely known in natural language processing. In the study, instead of the word vectors used in the original tf-idf technique, descriptors of image feature points calculated using the ORB and BRISK algorithms were used. The study has led to the following conclusions: 1) the proposed approach offers high efficiency in finding a copy of the image-query in the database. Thus, copies of all images presented for search and having their full analogs in the database are revealed. 2) The identification rate of modified image-queries varies, depending on the algorithm used for finding keypoints and descriptors. So, the maximum percentage of identified modified analogs is 60% when using ORB and 80% when using BRISK - out of all image analogs in the database. 3) The proposed approach shows a limited efficiency in finding images that can be attributed to the same class as the image queries (for example, a drawing of an excavator, a bulldozer, or a truck crane). Here, the maximum proportion of false identification has reached 60%.

Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words

Comparing textual content is becoming more and more problematic due to the fact that nowadays data is very dynamic. The application of sophisticated methods enables us to compare how similar the documents are to each other. In our research we apply the Cosine Similarity method to compare the similarity of several documents with each other. We also apply the TF-IDF technique which enables us to normalize the results. Normalization of these results is necessary for the fact that there are some words that are repeated several times and from this repetition is determined their importance. Finally we can see a comparison between the similarity of documents with normalized and non-normalized results. As can be seen in the research, the normalization of results has a great value in comparing documents with textual content.

Komparasi Model Klasifikasi Sentimen Issue Vaksin Covid-19 Berbasis Platform Instagram

Information about COVID-19 or regulations regarding it has been massively shared by the Ministry of Health, via kemenkes_ri account. Including the topic of vaccinations. Although health research has been conducted to support the covid-19 vaccine campaign, however there are still people who give negative comments. Sentiment is also found in the kemenkes_ri account. Sentiment analysis is an opinion classification process to determine the responses given are included in the positive, negative or neutral categories. In this study, it is proposed to compare the performance of the SVM and KNN algorithms to classify sentiment in the kemenkes_ri account related to vaccine policy in Indonesia. Sentiment is classified into 3 polarities namely neutral, positive, and negative. The purpose of this comparison is to compare the best classification model on the topic of vaccine issue sentiment analysis, especially the Instagram platform. In this study, the stages started from the comment scrapping technique which resulted in 2,925 records. Preprocessing using NLP technique and weighting using TF-IDF technique. Next, the SMOTE technique was performed to avoid imbalancing class. The ratio of training and testing data is 9:1. The results showed that the best classification is SVM = 98.30%, while the KNN method is 80.03%.

Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. The website that is often used to view movie comments today is IMDB. Movie comments are many and varied on the IMDB website, we can see comments based on the star rating aspect. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users using the star rating aspect and will be classified using the support vector machine method (SVM). Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. SVM is very efficient for many applications in science and engineering, especially for classification (pattern recognition) problems. In addition to the SVM method, the TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the SVM model are 79% accuracy, 75% precision, and 87% recall. The SVM classification is also superior to other methods, namely logistic regression.

Morphological Tagging and Lemmatization in the Albanian Language

Abstract An important element of Natural Language Processing is parts of speech tagging. With fine-grained word-class annotations, the word forms in a text can be enhanced and can also be used in downstream processes, such as dependency parsing. The improved search options that tagged data offers also greatly benefit linguists and lexicographers. Natural language processing research is becoming increasingly popular and important as unsupervised learning methods are developed. There are some aspects of the Albanian language that make the creation of a part-of-speech tag set challenging. This research provides a discussion of those issues linguistic phenomena and presents a proposal for a part-of-speech tag set that can adequately represent them. The corpus contains more than 250,000 tokens, each annotated with a medium-sized tag set. The Albanian language’s syntagmatic aspects are adequately represented. Additionally, in this paper are morphologically and part-of-speech tagged corpora for the Albanian language, as well as lemmatize and neural morphological tagger trained on these corpora. Based on the held-out evaluation set, the model achieves 93.65% accuracy on part-of-speech tagging, The morphological tagging rate was 85.31 % and the lemmatization rate was 88.95%. Furthermore, the TF-IDF technique weighs terms and with the scores are highlighted words that have additional information for the Albanian corpus.

Improving fake news classification using dependency grammar.

Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.

Sentiment Analysis for Thai Language in Hotel Domain Using Machine Learning Algorithms

Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level. Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency. However, the users’ reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language. The dataset was collected manually from two online agencies (Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%. The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.

Abstract Classification Using Support Vector Machine Algorithm (Case Study: Abstract in a Computer Science Journal)

Jurnal Komputasi is an online journal written by researchers and published by the Department of Computer Science, University of Lampung. Specific scientific information contained in journals is difficult to find because journals have not been structured and are classified into more specialized categories of computer science. Text mining can convert the shape of a journal into structured by homogeneous data form in it. 144 journal abstracts are collected into one corpus document in CSV format used as a research dataset. Journal abstract classification is done using one of the supervised machine learning methods, namely Support Vector Machine (SVM) so that the classification process is faster than the manual method. The TF-IDF technique is used to transform sentences in the abstract into vector so that they can be modelled with SVM. The classification model will be validated by applying the 10-fold cross validation technique. From these classifications a calculation of the resulting performance will be calculated based on the confusion matrix calculation of the resulting performance will be calculated based on the confusion matrix calculation and the use of 3 SVM kernels. The conclusion based on this research is that there are two factors that affect classification accuracy, that is the number of members between scientific classes that are not balanced and the number of features generated from text mining. The highest accuracy of testing result obtained on the use of 205 features and SVM Linear kernel with a value of 58,3%.