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With the emerging new technologies based on Artificial Intelligence (AI) for the generation of new and paraphrasing of existing text, the identification of genuinely written text has become an important research undertaking. Past approaches to address this issue, need a significant volume of human-labeled data. Most of the approaches used in literature are either for noisy text or for clean text. Conversations in chats, text in blogs, text messages on cell phones, text exchange on Messengers, etc., are examples of noisy text that may contain misspelled words or incomplete words. The second approach focuses on clean text, which is free from the mentioned characteristics in the noisy text. As research articles do not contain noisy data, we propose a model that focuses on clean text for the identification of paraphrases in research articles. To address the problem of paraphrase detection, this work presents a novel Bidirectional Encoder Representation from Transformers (BERT) based model with fine-tuning. For word representation, Global Vectors (Glove) embeddings and contextualized Embeddings From Language Models (ELMo) are employed in this work. Initially, the model is evaluated without performing preprocessing. Later, the preprocessing step is performed before evaluating the model. Extensive experimentations are performed to evaluate the proposed model utilizing two benchmark datasets, namely, Microsoft Research Paraphrase (MSRP) and Quora Question Pairs (Quora). A comparison of the proposed model is done with four closely related state-of-the-art works. The obtained results show that Fine-tuned BERT using ELMo embeddings with preprocessing produces promising outcomes. Paraphrase identification rates achieved on MSRP and Quora datasets are 86.51% and 94.32%, respectively, which are better than the other competing methods. The proposed solution enables the identification of paraphrased text with a higher accuracy having its application in multiple domains requiring genuinely written documents. [ABSTRACT FROM AUTHOR]

Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN with word embedding techniques introduced an additional layer to the landscape of emotion detection from text. The synergy between word embedding techniques and CNN harnesses the strengths of both approaches. CNNs extract local patterns and features from sequential data, making them well-suited for capturing relevant information within the embeddings. The results obtained with various embeddings highlight the significance of choosing synergistic combinations for optimum performance. The combination of CNNs and word embeddings proved a versatile and effective approach. [ABSTRACT FROM AUTHOR]

Analysis of the impact of the contextual embeddings usage on the text classification accuracy

Sentiment analysis (SA) involves utilizing natural language processing (NLP) methods to identify the sentiment conveyed by a given text. This study is grounded on the dataset sourced from the internet movie database (IMDB), encompassing evaluations of films and their corresponding positive or negative classifications. Our research experiment aims to ascertain the model with the highest accuracy and generality. Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Naïve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. Finally, GloVe embeddings achieve the best performance with an accuracy of 90.64% and a sensitivity of 91.07%.

Abstract Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications.

In recent years, cloud computing applications have facilitated the distribution of heterogeneous, unstructured digitized data among users' social networks of varying opinions. Text processing at a large scale requires high-precision computational techniques, which increases the computational burden. The advent of big data analytics along with Natural Language Processing is a powerful factor in improving the efficiency of processing large-scale text data, using the kernel of the MapReduce big data analytics frameworks that allow parallelization of large computational operations. In this paper, we propose an intelligent sentiment prediction approach based on deep learning, batch, and streaming big data analytics. In fact, our main objective is to take advantages of the powerful tools provided by the distributed platforms, such as, Hadoop and Spark to preprocess streaming data. This involves various tasks such as cleaning the data, reducing its size, minimizing access time, and decreasing storage volume. This step prepares the big streaming data to be fully exploited by Deep Learning models. This work includes a research study on processing big data related to short volume scripts based on batch and streaming distributed frameworks as well as deep learning approaches in Natural Language Processing. We detail our idea for analyzing short texts to determine their semantic context and categorize them into pros and cons poles. There were different stages in building this model, the first involving data reduction and refinement using selected features and big data analysis tools. In the second stage, words are embedded by global vector to be computed in layers of convolutional and recurrent neural networks. The experimental study and the analysis of the results confirm the usefulness of our proposed model and its superiority over the main approaches studied in the literature. Our model achieved a performance of 96% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) involves utilizing natural language processing (NLP) methods to identify the sentiment conveyed by a given text. This study is grounded on the dataset sourced from the internet movie database (IMDB), encompassing evaluations of films and their corresponding positive or negative classifications. Our research experiment aims to ascertain the model with the highest accuracy and generality. Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Naïve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. Finally, GloVe embeddings achieve the best performance with an accuracy of 90.64% and a sensitivity of 91.07%. [ABSTRACT FROM AUTHOR]

Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications. [ABSTRACT FROM AUTHOR]

Abstract Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology’s reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. Graphical Abstract

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers' decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer's opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product's features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer's opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. [ABSTRACT FROM AUTHOR]

Many questions and answers about Islamic law are scattered on the internet and have been explained repeatedly by various sites. One solution is presented by the website www.piss-ktb.com, which creates a web-based source of information in the form of Frequently Asked Questions (FAQ). However, web-based FAQs have a weakness: users still have to browse through the available questions one by one according to the questions they want to know the answers to. Browsing through thousands of FAQs is inefficient and exhausting. Thus, a chatbot system can become a better alternative to the FAQ website. Still, chatbots are difficult to use because most of their conversations are hard to understand. A single character error will cause the system to misunderstand its meaning. In reality, users expect a chatbot that can understand everyday language. Thus, it is necessary to develop a chatbot system that can understand various common sentence combinations in everyday language and understand the meaning of words. In addition, it should be able to predict answers automatically to various kinds of questions and requests, even though the initial training data is relatively low. Therefore, this study aims to develop a system that can provide answers automatically based on user commands in natural language using Global Vectors for Word Representations (GloVe), Convolutional Neural Networks (CNN), and Transfer Learning techniques. The result shows that the use of transfer learning and the Nadam optimizer can improve the system’s performance. [ABSTRACT FROM AUTHOR]

Call centers play a key role in the management of customer relationships in the modern business world. However, the growing demand for their services presents significant challenges, particularly in terms of staffing and handling increasing call volumes. This paper addresses these issues by presenting an AI-driven text classification framework tailored for the Republic of Turkiye Ministry of Trade Call Centre (MTCC), with the aim of automatically routing calls to relevant departments. Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. Through a comprehensive analysis, the study answers key research questions regarding optimal classifiers and vectorization methods. The proposed solution not only improves the efficiency of MTCC's call routing but also provides researchers with practical insights regarding Turkish text classification. The results indicate that a combination of the Random Forest classifier and Word2Vec text vectorization method is the optimal model that can manage to route calls in real-time. [ABSTRACT FROM AUTHOR]

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology's reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. [ABSTRACT FROM AUTHOR]

Writing essays is an important skill which enables one to clearly write the ideas and understanding of certain topic with the help of language articulation and examples. Writing essay is a skill so is the grading of those essays. It requires a lot of efforts to grade these essays and the task becomes tedious and repetitive when the student to teacher ratio is high. As with any other repetitive task, the intervention of technology for automated essay grading has been thought of long back. However, the main challenge in automated essay grading lies in the understanding of language construction, word usage and presentation of idea/ argument/ narration. Language complexity makes natural language understanding a challenging task. In this work, we show our experiments with pre-trained static word embeddings like GloVe, fastText and pre-trained contextual model Bidirectional Encoder Representations from Transformers (BERT) for the task of automated essay grading. For the regression task, we have used Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) models under various feature settings framed from the learnt embeddings. The results are shown with the ASAP-AES dataset on all 8 prompts. Our work shows average Quadratic Weighted Kappa (QWK) of 0.81 and 0.71 with SVR and LSTM on in-domain test set essays, respectively. The SVR model shows a better QWK than the human-human agreement of 0.75. To the best of our knowledge, our SVR model with pre-trained BERT embeddings achieve the highest average QWK reported on ASAP-AES data set. We further show the performance of our approach with adversary samples generated using permuted essays and off-topic essays. We experimentally show that our LSTM model though does not show high QWK score with human assigned grade but is robust against the adversarial settings considered. [ABSTRACT FROM AUTHOR]

In the contemporary digital marketplace, the proliferation of online consumer reviews has a pivotal influence on purchasing decisions. Concurrently, the prevalence of spurious reviews poses a substantial risk to the integrity of e-commerce, misleading consumers, and detrimentally impacting businesses. This paper delineates a pioneering methodology for the identification of counterfeit reviews, which is based on the combination of deep learning attributes and aspect-based analytical features. The main contribution of this research is (1) proposing an aspect fusion network based on the hierarchical attention mechanism to address the problem of multiple aspects of representing review content. The aspect fusion network can help select important aspect words and fuse aspect dictionaries with word-level attention weights. (2) We build a cardinality fusion model so that the heuristic can mitigate the negative impact of random weights and intervals on the auxiliary model. The methodology integrates advanced deep learning paradigms with aspect-based sentiment analysis to detect fraudulent reviews. Specifically, the approach encompasses a dual-method strategy: initially utilizing a Convolutional Neural Network (CNN) for the extraction of profound characteristics from review texts, followed by employing aspect-based sentiment analysis tools, including Part-of-Speech (PoS) tagging and GloVe embedding, for the distillation of aspectual features. Subsequently, these split sets of features are synergized and applied in the training of various classifier layers. Eextensive experiments have been conducted on six public review datasets contrasting the previous work on authenticity and aspect analysis. The effectiveness and performance of the proposed authenticity fusion model have been verified by the detailed analyses. The proposed model outperforms the competitors with remarkable improvement on both review authenticity and aspect analysis. This innovative approach was rigorously evaluated using a dataset of Amazon reviews that encompassed both authentic and counterfeit reviews. The empirical results demonstrate that our proposed method attains a remarkable accuracy rate of 97.73%, substantially surpassing existing state-of-the-art methodologies. The study posits that the strategic fusion of deep learning attributes and aspect-based features significantly enhances the efficacy of counterfeit review detection systems, presenting a formidable tool in the arsenal against e-commerce fraud.

Social media platforms have opened avenues for communication, information sharing, and engaging with others online. Automated accounts, known as social media bots, have been observed to engage in harmful activities such as disseminating misinformation, participating in online propaganda and election interference, spreading spam, cyberbullying, and harassing people. This paper proposes a new hybrid architecture based on semantic word embedding and Recurrent Neural Networks (RNNs) to detect social media bots. The research methodology includes the use of Global Vectors (GloVe) for text representation to convert tweets into vectors and combining the Bidirectional Gated Recurrent Units (BiGRU) and Long Short-Term Memory (LSTM) algorithms for semantic text-based classification. Using the proposed architecture, the training process was conducted with two datasets, Cresci-2017 and Twibot-20. The effectiveness of the approach in detecting automated accounts was assessed using five evaluation metrics: Precision, Accuracy, Recall, and F1-score. The proposed approach showed outstanding results in identifying social media bots based only on text-based content, achieving a Precision of 100%, Accuracy of 99.73%, Recall of 99.56%, and F1-Score of 99.63% using the Twibot-20 dataset. Moreover, the proposed architecture surpassed the results obtained by the state-of-the-art approach and showed resilience to overfitting and the ability to detect social media bots effectively in unseen and recent data. This highlights the importance of utilizing deep learning methods and semantic word representations to effectively address issues related to detecting and managing social media bot operations.

Online platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbullying involves using aggressive and offensive language including rude, insulting, hateful, and teasing comments to harm individuals on social media platforms. Human moderation is both slow and expensive, making it impractical in the face of rapidly growing data. Automatic detection systems are essential to curb trolling effectively. This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. The proposed approach is further compared with the state-of-the-art machine, deep, and transformer-based learning approaches with the FastText word embedding approach. Statistical results demonstrate that the proposed model outperforms others, achieving a 95% accuracy for detecting cyberbullying tweets. In addition, the model obtains 95%, 97%, and 96% for precision, recall, and F1 score, respectively. Results from k-fold cross-validation further affirm the supremacy of the proposed model with a mean accuracy of 95.07%.

Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time. [ABSTRACT FROM AUTHOR]

Defect classification is a process to classify defects based on predefined categories. It is time consuming and manual process. Many automatic defect classification methods have been proposed to speed up the process of defect classification. However, these methods have not utilized the inter relations among the defect reports. In the literature for defect classification, Term Frequency-Inverse Document Frequency and Bag of words based approaches have been proposed. In this paper, we have proposed word embedding based model for the defect classification which is proven to be better in comparison with the existing methods. We have also proposed models for inter project defect classification by considering combination of different datasets of the same domain. We tested the proposed approach on 4096 defect reports using K nearest neighbor, Random forest, Decision tree, Support vector machine, Stochastic gradient descent and Ada boost classifiers in terms of accuracy, precision, recall and F1-score. Experimental results show that Decision tree achieves highest accuracy 98.21% while trained and tested on GloVe word embedding. We have also generated new word embedding using the bug reports corpus. Further, we compare the proposed model with Lopes et.al., 2020 and results show that our model outperforms. [ABSTRACT FROM AUTHOR]

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Examinations are one way of evaluating students. To ensure the production of valid exams, frameworks such as Bloom's taxonomy are utilised when preparing questions. Bloom's taxonomy is a well-known framework that categorises educational objectives into six hierarchical levels of cognitive complexity. However, manually categorising exam questions can be time-consuming and subjective. The extant literature has yet to leverage advanced deep learning methods and state-of-the-art word embedding techniques. This study utilises the effectiveness of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) models along with GloVe, BERT and TF-IDF for automating the classification of exam questions according to the revised Bloom's taxonomy. The study collected various question types from online sources and multiple state universities in Sri Lanka, resulting in a dataset of 16,584 questions labelled manually with the aid of domain experts. The dataset was cleaned using natural language processing techniques. Three models were proposed: ANN+TF-IDF, LSTM+GloVe, and LSTM+BERT. The results of the ANOVA and post hoc pairwise comparisons using Bonferroni correction indicate that the LSTM+BERT model outperformed the other models significantly. The proposed approach provides a reliable and consistent way of evaluating students, and educators can use it to improve their teaching strategies. The findings of this study have important implications for educational institutions and can lead to more effective and efficient evaluations. [ABSTRACT FROM AUTHOR]

Each week, thousands of newspaper articles on real estate topics are read by market participants. While the market is comparatively intransparent, readers hope to find valuable information. This raises the question of whether this investment of time pays off and whether different types of newspapers are an equivalent source of information. This paper examines the relationship between news-based coverage of real estate topics respectively news-based market sentiment and total returns of the asset classes of residential, office and retail. Using methods of natural language processing, including word embedding, topic modelling and sentiment analysis, three sentiment indicators for each asset class can be derived from 137,000 articles of two trade and two daily newspapers. Our results suggest that trade newspapers outperform daily newspapers in the prediction of future total returns and that the generated sentiment indicators Granger-cause total returns. Moreover, the results indicate that daily newspapers report more negatively on rising returns in the residential market than the trade press. To the best knowledge of the authors, this is the first study to quantify news coverage and sentiment for the main real estate asset classes through means of textual analysis, and to assess different sentiments in trade and daily press. [ABSTRACT FROM AUTHOR]

One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively, BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts. [ABSTRACT FROM AUTHOR]

Background: Patients are hospitalized for extended periods, particularly in intensive care units (ICUs). As a result, the saturation probe (pulse oximeter) remains attached for an extended period and microorganisms can grow in the wet environment. If the pulse oximeters are not reprocessed, cross-infection may occur. The literature contaiudies in which gloves were used for the measurement while various SpO (peripheral arterial oxygen saturation) measurements were compared with each other. However, such comparisons have yet to be made with the results of arterial blood gas SpO measurements by pulse oximeter, considered as the gold standard. The present study aimed to compare arterial blood gas values with the fingertip saturation measurement performed by having adult patients wear gloves of different colors, one after the other, on their fingers and determining the effect of the differently colored gloves (transparent, white, black, light blue) on saturation values.Methods: The study was conducted on 54 patients in an ICU. Intra-arerial blood gas SpO results were measured. Oxygen saturatieasured while the patient 1. did not wear gloves and 2. sequentially wore a series of gloves of different colors. Paired t-test, correlation analysis, and Bland Altman charts were used to evaluate the results.Results: The mean SpO% value of the participants’ intra-arterial blood gas measurements was 97.76±2.04. The mean SpO% value obtained from the measurements of the fingers with a transparent glove was 0.43 points lower than the mean SpO% value of the intra-arterial blood gas measurements (t=0.986, p=0.61). The mean SpO% value obtained from the measurements of the fingers with a white glove was 0.9oints lower than the mean SpO% value of the intra-arterial blood gas measurements (t=1.157, p=0.093).Conclusion: Of the measurements performed with a glove, the mean SpO% value obtained from the measurements of the fingers with a transparent glove was more consistent with the mean SpO% the intra-arterial blood gas measurements than measurement of the fingers without a glove.