Twitter Sentiment Analysis using an Ensemble Weighted Majority Vote Classifier

Highlights A hybrid CNN–WE model markedly improves the accuracy of barley disease classification. Adaptive weighting enhances decision stability by reducing the influence of weaker classifiers. The proposed ensemble achieves 98.72% accuracy, outperforming individual models. The framework supports accurate barley disease detection and contributes to the next generation of smart agriculture. ABSTRACT. Rhynchosporium commune and Pyrenophora teres are two of the most prevalent barley diseases, and their highly similar visual symptoms make accurate diagnosis and differentiation particularly challenging. Artificial intelligence (AI) and machine learning (ML) methods have been widely applied to plant disease detection; however, their performance often degrades under diverse field conditions and noisy image data. Moreover, reliance on a single model increases vulnerability to its specific limitations, especially when diseases exhibit overlapping visual characteristics. To address these challenges, this study proposes a hybrid model that integrates deep feature extraction using a Convolutional Neural Network (CNN) with a weighted ensemble (WE) of ML classifiers. The CNN-extracted features are fed into four classifiers: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Decision Tree (DT), each leveraging distinct strengths in handling complex, nonlinear, and interpretable data. A dynamic weighting strategy is then applied, assigning higher weights to classifiers with superior validation accuracy, and the final prediction is obtained through weighted majority voting. Unlike conventional ensemble methods that treat all classifiers equally, this adaptive weighting enhances decision stability, reduces the influence of weaker models, and significantly improves overall accuracy. Experimental results demonstrate classification accuracies of 82.5% for CNN, 87.3% for k-NN, 83.7% for DT, 91.6% for SVM, and 93.5% for RF, while the proposed WE achieves 98.72% accuracy. Furthermore, the ensemble model attains class-wise precision of 98.5% for R. commune, 97.5% for P. teres, and 98.5% for healthy samples, marking a substantial improvement over individual classifiers. These findings highlight the effectiveness of combining automatic deep feature extraction with adaptive ensemble learning, offering a robust framework for accurate barley disease detection and contributing to the advancement of intelligent plant health monitoring and precision agriculture systems. Keywords: Barley disease classification, Convolutional neural networks, Deep feature extraction, Ensemble classifiers, Machine learning, Weighted ensemble.

In the past few decades, the growth of data on the Internet has increased significantly, and even today, tons of data get generated with each passing day. The World Wide Web has become a great source of e-learning, sharing ideas, and interchanging school of thoughts and views. Internet community sites like Twitter, Facebook, and Instagram have gained immense attraction and have gathered a huge pool of daily active users over the past few decades, as they provide a medium to exchange or express their opinions or ideas about everything and anything. Many studies have been conducted on the topic of sentiment analysis, particularly on Twitter data. Sentiment analysis is effective for analyzing data in tweets where suppositions are very unstructured, varied, and positive, negative, or neutral. This research examines the evaluation of coronavirus-related Twitter data. We looked into the feelings of a large number of tweets gathered by Web scraping of tweets using various hashtags. The experimental results were presented using the bidirectional encoder representations from transformers (BERT) model, which was compared to the performance of traditional classification models like stochastic gradient descent, Naive Bayes, random forest, decision tree, logistic regression, and XG Boost. With an overall accuracy of 95.12%, the BERT model outperformed all other standard models.KeywordsTwitterSentiment analysisCoronavirusCOVID-19ClassificationMachine learningNLPBERT

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).

Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In this work, Parkinson’s disease is classified with the help of human voice signals. Six different machine learning (ML) algorithms are used in the classification: Stochastic Gradient Descent (SGD) Classifier, Extreme Gradient Boosting (XGB) Classifier, Logistic Regression Classifier, Random Forest Classifier, K-Nearest Neighbour (KNN) Classifier, and Decision Tree (DT) Classifier. This research aims to classify Parkinson’s disease using human voice signals and extract essential features to reduce the complexity of the dataset. Then, human voice signals are analyzed to check the voice intensity and spectrum for PD patients. Then, machine learning classifiers are applied to classify the PD patients based on the extracted features. The results show that SGD-Classifier has 91% accuracy, XGB-Classifier has 95% accuracy, Logistic Regression has 91% accuracy, Random Forest shows 97% accuracy, KNN shows 95% accuracy, and Decision Tree has 95% accuracy. Hence, Random Forest has the highest accuracy. The disease can be studied more by looking for more characteristics of PD patients to enhance its proper use in the medical field.

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pre-trained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of Apple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise

This paper explores patterns in Beijing's Particulate Matter 2.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</sup> concentration and forecasts future concentrations. Air quality has been an enormous health concern in recent decades as the place has become further industrialized and more and more of its citizens have begun driving automobiles. The occurenece of air pollution takes place in the following ways. 1. release and generation of pollutants from their source. 2. carry of pollutants in the atmosphere. 3. penetrating and negatively impacting human health and ecosystems. We tend to minimise the effects of these emissions as there is no practical, economical or technical method for zero emissions. PM 2.5 is especially dangerous because it can pass through the human body's natural filters and enter the lungs. Health concerns related to PM 2.5 include heart and lung disease, asthma, bronchitis, and other respiratory problems. Machine learning, as one of the most accepted techniques, is capable to efficiently train a model using regression models to predict the hourly air pollution concentration <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . Following six regressors chosen for this problem were Linear Regression, K-Nearnest Neighbor, Stochastic Gradient Descent, Decision Tree, Random Forest and Multi-layer Perceptron. Although performance of all models was comparable, Multi-layer Perceptron Algorithm model successfully bring about better accuracy and true positive rate with 95.4 accuracy.

Chapter 9 - An intelligent deep learning approach for colon cancer diagnosis

Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.

Background/Objectives: Recent studies emphasized on using ensemble models over single ones to solve credit scoring problems. The objective of this study is to build a heterogeneous ensemble classifier model with an improved classification accuracy. Methods: This study focuses on developing a heterogeneous ensemble classifier using Logistic Regression, K-nearest neighbor, Decision tree, Random Forest, Naïve Base and Support vector machine as base classifiers and Random Forest, Logistic Regression and Support vector machine as meta-classifiers. The proposed model is built using these six base classifiers for ensemble aggregation. A feature selection algorithm based on the random forest technique is used for selecting the best features. A stacking and voting method are used for building ensemble model. Findings: The ensemble classifier gives superior predictive performance than single classifiers SVM, DT, RF, NB, KNN and LR with an accuracy of 91.56% for Australian dataset and 84.35% for German dataset. Novelty: The proposed model uses stacking and majority voting method for ensemble classification. Initially, stacking is applied to the base classifiers. This is done in two levels. First the training dataset is split into 10 folds for cross validation. The output of each classifier is taken, and the dataset is updated with the meta-features. In the second level, three meta-classifiers (MC), namely LR, SVM and RF are used. Majority voting is applied to the output of these meta-classifiers for the prediction. Keywords: Credit scoring; ensemble model; SVM; DT; RF; NB; KNN; LR

To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are used as combining rules in bagging and boosting. However, other combining rules such as mean, product and average are possible. In this paper, we study bagging and boosting in Linear Discriminant Analysis (LDA) and the role of combining rules in bagging and boosting. Simulation studies, carried out for two artificial data sets and one real data set, show that bagging and boosting might be useful in LDA: bagging for critical training sample sizes and boosting for large training sample sizes. In contrast to a common opinion, we demonstrate that the usefulness of boosting does not directly depend on the instability of a classifier. It is also shown that the choice of the combining rule may affect the performance of bagging and boosting.KeywordsLinear Discriminant AnalysisMajority VoteGeneralization ErrorTraining ObjectBearing FaultThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The objective of this paper is to compare different classifiers’ recognition accuracy for the 28 Arabic alphabet letters gestured by participants as Sign Language and captured by two depth sensors. The accuracy results of three individual classifiers: (1) the support vector machine (SVM), (2) random forest (RF), and (3) nearest neighbour (kNN), using the original gestured dataset were compared with the accuracy results using an ensemble of the results of each classifier, as recommended by the literature. SVM produced higher overall accuracy when running as an individual classifier regardless of the number of observations for each letter. However, for letters with fewer than 65 observations each, which created a far smaller dataset, RF had higher accuracy than SVM did when using the ensemble approach. Although RF produced higher accuracy results for classes with limited class observation data, the difference between the accuracy results of RF in phase 2 and SVM in phase 1 was negligible. The researchers conclude that such a difference does not warrant using the ensemble approach for this experiment, which adds more processing complexity without a significant increase in accuracy.

Chronic kidney disease (CKD) involves numerous variables, but only a few significantly impact the classification task. The statistically equivalent signature (SES) method, inspired by constraint-based learning of Bayesian networks, is employed to identify essential features in CKD. Unlike conventional feature selection methods, which typically focus on a single set of features with the highest predictive potential, the SES method can identify multiple predictive feature subsets with similar performance. However, most feature selection (FS) classifiers perform suboptimally with strongly correlated data. The FS approach faces challenges in identifying crucial features and selecting the most effective classifier, particularly in high-dimensional data. This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) in conjunction with the SES method for feature selection in CKD identification. Following this, an ensemble deep-learning model combining long short-term memory (LSTM) and gated recurrent unit (GRU) networks is proposed for CKD classification. The features selected by the hybrid feature selection method are fed into the ensemble deep-learning model. The model’s performance is evaluated using accuracy, precision, recall, and F1 score metrics. The experimental results are compared with individual classifiers, including decision tree (DT), Random Forest (RF), logistic regression (LR), and support vector machine (SVM). The findings indicate a 2% improvement in classification accuracy when using the proposed hybrid feature selection method combined with the LSTM and GRU ensemble deep-learning model. Further analysis reveals that certain features, such as HEMO, POT, bacteria, and coronary artery disease, contribute minimally to the classification task. Future research could explore additional feature selection methods, including dynamic feature selection that adapts to evolving datasets and incorporates clinical knowledge to enhance CKD classification accuracy further.

In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. The main purpose of this proposed work is to develop a system that can determine whether a tweet is “spam” or “ham” and evaluate the emotion of the tweet. The extracted features after preprocessing the tweets are classified using various classifiers, namely, decision tree, logistic regression, multinomial naïve Bayes, support vector machine, random forest, and Bernoulli naïve Bayes for spam detection. The stochastic gradient descent, support vector machine, logistic regression, random forest, naïve Bayes, and deep learning methods, namely, simple recurrent neural network (RNN) model, long short-term memory (LSTM) model, bidirectional long short-term memory (BiLSTM) model, and 1D convolutional neural network (CNN) model are used for sentiment analysis. The performance of each classifier is analyzed. The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets with a particular sentiment.

Breast cancer is a leading global cause of cancer-related deaths highlighting the need for an accurate diagnostic system. Up to now, computer-aided diagnosis (CAD) system plays an essential role in supporting pathologists with prompt and accurate classification. Feature selection within the CAD system is crucial as it helps identify the most relevant data for subsequent classification tasks. This paper proposed a novel method that focuses on fuzzy discretization in handling continuous features and selecting relevant and interactive features while eliminating redundancy using Class Association Rule Mining (CARM). The proposed method, FD-CARI was compared with other feature selection techniques including CFS, FCBF, Consistency, Relief-F, and mRMR using five different machine learning classifiers such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). Performance evaluation metrics such as Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), Precision, F1-Score, and AUC were then utilized. Results: The experimental findings consistently showed that the proposed method achieved high performance with an ACC of 96.21%, SEN of 94.26%, and SPE of 97.38% on the SVM classifiers, and an ACC of 96.05%, SEN of 93.82%, and SPE of 97.38% on the LR classifiers. It demonstrated similar effectiveness to Relief-F for DT and RF classifiers. However, FCBF achieved the highest performance on NB with ACC, SEN, and SPE values of 96.21%, 92%, and 96.60%, respectively. The proposed method efficiently selects relevant and interactive features while enabling classifiers to achieve better classification accuracy.

Internet acts as a media to publicite news, which divided into various categories, such as politics, economics, sports, and health. Each has tendency to publish the news with positive or negative sentiment. News sentiment has ability to affect people's opinion about a topic or government policy. Economy is one interesting topic which has direct impact to Indonesian citizen. Hence, this research focused on economic news from Indonesian online media. This research tested the performance of some classifiers to classify the news in Bahasa Indonesia from various online news media. Popular classifiers such as decision tree, random forests, and support vector machine will be compared each other. After that, they will be combined using majority vote classifier. Majority vote classifier votes the result between them, the voted result then compared again with the other three classifiers. Experiments has been made by this research by combining parameters and classifiers, concluded that combining multiple classifier using Majority Vote scoring better precision and accuracy than decision tree, random forests, and support vector machine alone.