Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms

Automatic assessment of concept map quality is an important challenge in the field of education, particularly in evaluating students' conceptual understanding objectively and efficiently. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Ordinal Regression, in classifying the quality of concept maps. The evaluation was conducted on three approaches to text feature representation: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a combination of both (TF-IDF + BERT). Additionally, this study compares the performance of the models under two dataset conditions: original data and data balanced using the Synthetic Minority Over-sampling Technique (SMOTE), to address the class imbalance that often occurs in educational data. The data used consists of a collection of propositions from students' concept maps that have been labeled with ordinal scores based on quality. Text representation is extracted using the TF-IDF and BERT approaches, and then used as input to build the classification model. Performance evaluation was conducted using the metrics of Accuracy, Precision, Recall, F1-score, Cohen’s Kappa, and MAE. The results show that the Ordinal Regression model with TF-IDF representation combined with SMOTE achieved the best performance, with an accuracy of 0.8777, an F1-score of 0.8773, and a Cohen’s Kappa of 0.7701. These results indicate that classical feature representations like TF-IDF remain effective in limited data scenarios, and that the SMOTE technique successfully improved the model's performance by reducing bias towards the majority class. This research contributes to the development of an automatic concept map assessment system and suggests optimal classification strategies for educational datasets with ordinal and imbalanced characteristics

Background: Text classification techniques are increasingly important with the exponential growth of textual data on the internet. Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorizer(CV) are commonly used methods for feature extraction. TF-IDF assigning weights to terms based on their frequency. CV simply counts the occurrences of terms. The performance of CV as well as TF-IDF are evaluated and compared with KNN and DT classifiers across text datasets. Methodology: The investigation begins with preprocessing. The feature vectors are created using both TF-IDF and CV. Feature vectors are passed into the KNN and DT classifiers at in training stage. Experiments are executed the usage of Kaggle's public database Ukraine 10K tweets sentiment_analysis dataset and the Womens ecommerce clothing reviews dataset. Findings: The average of precision, recall, f1 score and accuracy of KNN with TF-IDF were 84.5%, 87%, 83%, 87% respectively and KNN with CV were 83.5%, 87%, 83.5%, 87% respectively. Similarly, average of precision, recall, f1 score and accuracy of DT with TF-IDF were 89%, 89%, 89%, 89% respectively and DT with CV were 89%, 89.5%, 89.5%, 89.5% respectively. The results obtained in this research is consistent with previous similar research result. Conclusions: The performance of TF-IDF is almost similar as CV for a particular dataset and a particular classifier in this study. Novelty: The experiment performed using these classifiers and feature extraction methods on the datasets is a novelty and contribution of this research.

The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 92% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake news detection accuracy.

This paper discusses a novel hybrid approach to text classification that integrates a machine learning algorithm along with DistilBERT, a pre-trained deep learning framework for natural language processing, offers a base model fine-tuned on Indian Banking News-Events with a rule-based method that is used by filtering false positives and dealing with false negatives to enhance the results given by the previous classifier. The major benefit is that by incorporating unique rules for certain chaotic or overlapping categories that have not been effectively trained, the system can be quickly fine-tuned. This research also compares the effectiveness of state-of-art deep contextual language representation DistilBERT used in our proposed hybrid model with the most preferred context independent language representation TFIDF, on supervised learning of the classification of multiclass Banking News-Events. Both representations are fed into the machine learning classifiers, Logistic Regression, Linear SVC, Decision Tree, and Random Forest. The findings indicate that DistilBERT can better transfer generic domain knowledge to other domains as compared to the baseline TFIDF and results into higher accuracy with Random Forest. The result is further fed into the Rules based method as per our proposed model. And, our proposed hybrid model resulted into improved accuracy.

Background and Aims Large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), marking a shift from traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF). We developed a traditional NLP model to predict arteriovenous fistula (AVF) failure within next 30 days using clinical notes. The goal of this analysis was to investigate whether LLMs would outperform traditional NLP techniques, specifically in the context of predicting AVF failure within the next 30 days using clinical notes. Method We defined AVF failure as the change in status from active to permanently unusable status or temporarily unusable status. We used data from a large kidney care network from January 2021 to December 2021. Two models were created using LLMs and traditional TF-IDF technique. We used “distilbert-base-uncased”, a distilled version of BERT base model [1], and compared its performance with traditional TF-IDF-based NLP techniques. The dataset was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising of unseen patients’ data was used to evaluate the performance of the model. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Results The incidence of 30 days AVF failure rate was 2.3% in the population. Both LLMs and traditional showed similar overall performance as summarized in Table 1. Notably, LLMs showed marginally better performance in certain evaluation metrics. Both models had same AUROC of 0.64 on test data. The accuracy and balanced accuracy for LLMs were 72.9% and 59.7%, respectively, compared to 70.9% and 59.6% for the traditional TF-IDF approach. In terms of specificity, LLMs scored 73.2%, slightly higher than the 71.2% observed for traditional NLP methods. However, LLMs had a lower sensitivity of 46.1% compared to 48% for traditional NLP. However, it is worth noting that training on LLMs took considerably longer than TF-IDF. Moreover, it also used higher computational resources such as utilization of graphics processing units (GPU) instances in cloud-based services, leading to higher cost. Conclusion In our study, we discovered that advanced LLMs perform comparably to traditional TF-IDF modeling techniques in predicting the failure of AVF. Both models demonstrated identical AUROC. While specificity was higher in LLMs compared to traditional NLP, sensitivity was higher in traditional NLP compared to LLMs. LLM was fine-tuned with a limited dataset, which could have influenced its performance to be similar to that of traditional NLP methods. This finding suggests that while LLMs may excel in certain scenarios, such as performing in-depth sentiment analysis of patient data for complex tasks, their effectiveness is highly dependent on the specific use case. It is crucial to weigh the benefits against the resources required for LLMs, as they can be significantly more resource-intensive and costly compared to traditional TF-IDF methods. This highlights the importance of a use-case-driven approach in selecting the appropriate NLP technique for healthcare applications.

The proliferation of fake news across online media platforms poses significant challenges to public trust,societal harmony,and information integrity.Many of the things that people read online, particularly on social media, seem true, but they are frequently not. Fake news is defined as news, articles, or hoaxes that are purposefully created to mislead or deceive readers..This study develops a machine learning-based system for detecting fake news on digital platforms. Utilizing algorithms such as K-Nearest Neighbors (KNN), Naive Bayes, Logistic Regression, Random Forest, Gradient Boosting and Support Vector Machines (SVM), the system processes textual data using natural language processing (NLP) techniques like term frequency-inverse document frequency (TF-IDF) and bag-of- words for feature extraction.The performance of each algorithm is evaluated using metrics such as accuracy, precision, recall, and F1-score to identify the most effective model. By identifying the most effective machine learning model, this research aims to provide a scalable solution that can help reduce misinformation and enhance the credibility of online information.By distinguishing the best A1 model,this expectation expects to give a versatile arrangement that can assist with lessening deception and improve the believability of online data. Keywords- News and Fake,Natural Language Processing,Feature Extraction,a classification and Model Evaluation

Sentiment refers to emotions. Sentiment analysis, often known as opinion mining, is the technique of identifying and extracting subjective data from pre-web and post-web reviews using text analytics, computational linguistics, and natural language processing. Hindi is an Indian language which is used by many of Indians. Due to phenomenal growth of online product reviews in Hindi post-web Hindi reviews are also increasing rapidly. A machine learning based method in this paper to analysis postweb text data. The present method is divided into four steps. First of all, an annotated Hindi review data set is developed from post-web sources. In second step, feature extraction is performed on annotated Hindi review dataset using the Term-Frequency/ Inverse-Document Frequency (TF-IDF) and count vectorization techniques. In the third step, the retrieved features are given to the classifier so it can make predictions. Moreover, annotated dataset translated into English. Second step and third step are performed on annotated English dataset in last step. A range of evaluation criteria, including precision, recall, and F1- score, are presented in the results. In both instances, the support vector machine produced the most pertinent results.

Social media platforms, particularly Twitter, generate vast volumes of user-generated content that reflect real-time human emotions and opinions. Emotion analysis of such data offers significant value in areas including mental health monitoring, public sentiment tracking, and opinion mining. In this paper, we present a machine learning-based approach for automatically classifying emotions expressed in tweets into six distinct categories: joy, sadness, anger, fear, love, and surprise. The proposed system employs Natural Language Processing (NLP) techniques for text preprocessing, including tokenization, stopword removal, and lemmatization using the NLTK library. Feature extraction is performed using the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization method with unigram and bigram representations. Three supervised machine learning algorithms are evaluated — Logistic Regression, Multinomial Naive Bayes, and Linear Support Vector Machine (LinearSVC) — and compared based on accuracy, precision, recall, and F1-score metrics. Experiments are conducted on the publicly available dairai/emotion dataset comprising over 20,000 labeled tweets. Results demonstrate that Logistic Regression with TF-IDF achieves the highest classification accuracy, outperforming the other models. The study confirms that classical machine learning models, when combined with effective text preprocessing and feature engineering, provide a reliable and computationally efficient framework for multi-class emotion detection in short social media text.

Rumors are typically described as remarks whose true value is unknown. A rumor on social media has the potential to spread erroneous information to a large group of individuals. Those false facts will influence decision-making in a variety of societies. In online social media, where enormous amounts of information are simply distributed over a large network of sources with unverified authority, detecting rumors is critical. This research proposes that rumor detection be done using Natural Language Processing (NLP) tools as well as six distinct Machine Learning (ML) methods (Nave Bayes (NB), random forest (RF), K-nearest neighbor (KNN), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and Decision Tree (DT)). The data set size for the suggested experiment was 16,865 samples. For pre-processing tokenization was used to separates each one of the tokens from the others. Normalization that removes all non-word tokens, deleting stop words was utilized to remove all unnecessary words, and stemming was used to obtain the stem of the tokens. Prior to using the six classification algorithms, the major feature extraction approach Term Frequency- Inverse Document Frequency (TF-IDF) was applied. The RF classifier performed better compared to all other classifiers with an accuracy of 99%, according to the data. Keywords: Machine learning, Text classification, Naïve Byes, RF, KNN, DT, Natural language processing, SGD).

As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text’s context and blindness to the topic of the corpus. This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis. A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered. To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study. The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates.

Text data is one of the dominating data types in Big Data driven services and applications. The performance of text classification largely depends on the quality of feature extraction over the text corpus. For supervised learning over text documents, the TF-IDF (Term Frequency-Inverse Document Frequency) weighting factor is one of the most frequently used features in text classification. In this paper, we address two known limitations of TF-IDF based feature extraction method: First, the conventional TF-IDF weighting factor lacks of consideration about the synonymous relationship between feature terms. Second, for big corpus with large number of text documents and large number of feature terms, the computational complexity of text classification increases with the dimensionality of the feature space. We address these problems by introducing an optimization technique based on the Inter-Category Distributions (ICD) of terms and the Inter-Category Distributions of documents. We call this new weighting factor TF-IDF-ICD, namely TF-IDF with Inter-Category Distributions. To further enhance the effectiveness of our TF-IDF-ICD method, we describe a TF-IDF-ICD threshold based Dimensionality Reduction (DR) optimization. We test the text classifier with a corpus of 10, 000 articles. The evaluation results show that the proposed TF-IDF-ICD based text classification method outperforms the conventional TF-IDF based classification solution by \(7.84\%\) at only about \(43.19\%\) of the training time used by the conventional TF-IDF based text classification methods.

Document Retrieval has seen significant advancements in the last few decades. Latest developments in Natural Language Processing have made it possible to incorporate context and complex lexical patterns to document representations. This opens new possibilities for developing advanced retrieval systems. Traditional approaches for indexing documents suggest averaging word and sentence encoding to form fixed-length document embeddings. However, the common bag-of-word approach fails to incorporate the semantic context, which can be critical for understanding document-query relevancy. We address this by leveraging Bidirectional Encoder Representations from Transformers (BERT) to create semantically rich document embeddings. BERT compensates the limitations of the Term Frequency Inverse Document Frequency (TF-IDF) by incorporating contextual embeddings. In this paper, we propose an ensemble of BERT and TF-IDF for a document retrieval system, where TFIDF and BERT together score the documents against a query, to retrieve a final set of top K documents. We critically compare our model against the standard TF-IDF method and demonstrate a significant performance improvement on MS MARCO data (Microsoft-curated data of Bing queries).

Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization

The widespread dissemination of misinformation on online platforms has become a significant societal challenge, influencing public opinion, political landscapes, and social stability. Traditional rule-based and statistical methods for fake news classification often struggle to generalize across different datasets due to the evolving nature of misinformation. To address this, deep learning and natural language processing (NLP) techniques have emerged as effective solutions for detecting deceptive content. In this study, a novel fake news classification framework is proposed, integrating Transformer-based feature extraction with an XGBoost classifier. The methodology leverages RoBERTa embeddings, Term Frequency-Inverse Document Frequency (TF-IDF)-based tokenization, and metadata processing to capture both linguistic and contextual cues essential for accurate classification. The model is trained and evaluated on the PolitiFact and GossipCop datasets, achieving state-of-the-art performance with an accuracy of 0.9930 and 0.9764, respectively. Comparative analysis with existing methods demonstrates the effectiveness of our approach in improving precision, recall, and F1-score. The findings underscore the importance of combining deep learning-based feature extraction with ensemble learning techniques for robust and scalable fake news detection.

The focus of this work is on developing a cognitive tool that predicts the most frequent HSE hazards with the highest potential severity levels. The tool identifies these risks using a natural language processing algorithm on HSE leading and lagging indicator reports submitted to an oilfield services company’s global HSE reporting system. The purpose of the tool is to prioritize proactive actions and provide focus to raise workforce awareness. A natural language processing algorithm was developed to identify priority HSE risks based on potential severity levels and frequency of occurrence. The algorithm uses vectorization, compression, and clustering methods to categorize the risks by potential severity and frequency using a formulated risk index methodology. In the pilot study, a user interface was developed to configure the frequency and the number of the prioritized HSE risks that are to be communicated from the tool to those employees who opted to receive the information in a given location. From this pilot study using data reported in the company’s online HSE reporting system, the algorithm successfully identified five priority HSE risks across different hazard categories based on the risk index. Using a high volume of reporting data, the risk index factored multiple coefficients such as severity levels, frequency and cluster tightness to prioritize the HSE risks. The observations at each stage of the developed algorithm are as follows:In the data cleaning stage, all stop words (such as a, and, the) were removed, followed by tokenization to divide text in the HSE reports into tokens and remove punctuation.In the vectorization stage, many vectors were formed using the Term Frequency - Inverse Document Frequency (TF-IDF) method.In the compression stage, an autoencoder removed the noise from the input data.In the agglomerative clustering stage, HSE reports with similar words were grouped into clusters and the number of clusters generated per category were in the range of three to five. The novelty of this approach is its ability to prioritize a location’s HSE risks using an algorithm containing natural language processing techniques. This cognitive tool treats reported HSE information as data to identify and flag priority HSE risks factoring in the frequency of similar reports and their associated severity levels. The proof of concept has demonstrated the potential ability of the tool. The next stage would be to test predictive capabilities for injury prevention.