scholarly journals Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains

2018 ◽  
Vol 1 (1) ◽  
pp. 224-234 ◽  
Author(s):  
Donia Gamal ◽  
Marco Alfonse ◽  
El-Sayed M. El-Horbaty ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Second Phase ◽  
Adaptive Boosting ◽  
Two Phases ◽  
Textual Content

Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses on the various machine learning (ML) algorithms which are utilized in the analyzation of sentiments and in the mining of reviews in different datasets. Overall, an SC task consists of two phases. The first phase deals with feature extraction (FE). Three different FE algorithms are applied in this research. The second phase covers the classification of the reviews by using various ML algorithms. These are Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Passive Aggressive (PA), Maximum Entropy (ME), Adaptive Boosting (AdaBoost), Multinomial NB (MNB), Bernoulli NB (BNB), Ridge Regression (RR) and Logistic Regression (LR). The performance of PA with a unigram is the best among other algorithms for all used datasets (IMDB, Cornell Movies, Amazon and Twitter) and provides values that range from 87% to 99.96% for all evaluation metrics.

scholarly journals Feature-Based Opinion Mining and Managed Machine Learning with Sentiment Classification Models

2019 ◽  
Vol 9 (2) ◽  
pp. 3992-3998
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Learning Tasks ◽  
Feature Based

Sentiment Analysis is individuals' opinions and feedbacks study towards a substance, which can be items, services, movies, people or events. The opinions are mostly expressed as remarks or reviews. With the social network, gatherings and websites, these reviews rose as a significant factor for the client’s decision to buy anything or not. These days, a vast scalable computing environment provides us with very sophisticated way of carrying out various data-intensive natural language processing (NLP) and machine-learning tasks to examine these reviews. One such example is text classification, a compelling method for predicting the clients' sentiment. In this paper, we attempt to center our work of sentiment analysis on movie review database. We look at the sentiment expression to order the extremity of the movie reviews on a size of 0(highly disliked) to 4(highly preferred) and perform feature extraction and ranking and utilize these features to prepare our multilabel classifier to group the movie review into its right rating. This paper incorporates sentiment analysis utilizing feature-based opinion mining and managed machine learning. The principle center is to decide the extremity of reviews utilizing nouns, verbs, and adjectives as opinion words. In addition, a comparative study on different classification approaches has been performed to determine the most appropriate classifier to suit our concern problem space. In our study, we utilized six distinctive machine learning algorithms – Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), RF (Random Forest) KNN (K nearest neighbors) and SoftMax Regression.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

scholarly journals Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3532 ◽  
Author(s):  
Nicola Mansbridge ◽  
Jurgen Mitsch ◽  
Nicola Bollard ◽  
Keith Ellis ◽  
Giuliana Miguel-Pacheco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Budget ◽  
Learning Algorithms ◽  
Eating Behaviour ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Optimum Number ◽  
Eating Behaviours ◽  
Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

scholarly journals A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Machine Learning Algorithms ◽  
Electricity Sector ◽  
Stochastic Gradient Descent ◽  
Energy Output ◽  
Gradient Boosting ◽  
Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

scholarly journals APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PROCESSING COMMENTS FROM THE YOUTUBE VIDEO HOSTING UNDER TRAINING VIDEOS

2021 ◽  
pp. 33-42
Author(s):  
L. S. Koriashkina ◽  
H. V. Symonets
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Russian Language ◽  
Stochastic Gradient ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Gradient Enhancement

Purpose. Detecting toxic comments on YouTube video hosting under training videos by classifying unstructured text using a combination of machine learning methods. Methodology. To work with the specified type of data, machine learning methods were used for cleaning, normalizing, and presenting textual data in a form acceptable for processing on a computer. Directly to classify comments as “toxic”, we used a logistic regression classifier, a linear support vector classification method without and with a learning method – stochastic gradient descent, a random forest classifier and a gradient enhancement classifier. In order to assess the work of the classifiers, the methods of calculating the matrix of errors, accuracy, completeness and F-measure were used. For a more generalized assessment, a cross-validation method was used. Python programming language. Findings. Based on the assessment indicators, the most optimal methods were selected – support vector machine (Linear SVM), without and with the training method using stochastic gradient descent. The described technologies can be used to analyze the textual comments under any training videos to detect toxic reviews. Also, the approach can be useful for identifying unwanted or even aggressive information on social networks or services where reviews are provided. Originality. It consists in a combination of methods for preprocessing a specific type of text, taking into account such features as the possibility of having a timecode, emoji, links, and the like, as well as in the adaptation of classification methods of machine learning for the analysis of Russian-language comments. Practical value. It is about optimizing (simplification) the comment analysis process. The need for this processing is due to the growing volumes of text data, especially in the field of education through quarantine conditions and the transition to distance learning. The volume of educational Internet content already needs to automate the processing and analysis of feedback, over time this need will only grow.

scholarly journals Application of machine learning algorithms for flood susceptibility assessment and risk management

2021 ◽  
Author(s):  
R. Madhuri ◽  
S. Sistla ◽  
K. Srinivasa Raju
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Influencing Factors ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Climate Change Scenarios ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Normalised Difference Vegetation Index

Abstract Assessing floods and their likely impact in climate change scenarios will enable the facilitation of sustainable management strategies. In this study, five machine learning (ML) algorithms, namely (i) Logistic Regression, (ii) Support Vector Machine, (iii) K-nearest neighbor, (iv) Adaptive Boosting (AdaBoost) and (v) Extreme Gradient Boosting (XGBoost), were tested for Greater Hyderabad Municipal Corporation (GHMC), India, to evaluate their clustering abilities to classify locations (flooded or non-flooded) for climate change scenarios. A geo-spatial database, with eight flood influencing factors, namely, rainfall, elevation, slope, distance from nearest stream, evapotranspiration, land surface temperature, normalised difference vegetation index and curve number, was developed for 2000, 2006 and 2016. XGBoost performed the best, with the highest mean area under curve score of 0.83. Hence, XGBoost was adopted to simulate the future flood locations corresponding to probable highest rainfall events under four Representative Concentration Pathways (RCPs), namely, 2.6, 4.5, 6.0 and 8.5 along with other flood influencing factors for 2040, 2056, 2050 and 2064, respectively. The resulting ranges of flood risk probabilities are predicted as 39–77%, 16–39%, 42–63% and 39–77% for the respective years.

scholarly journals Analyzing Fake News Based on Machine Learning Algorithms

2020 ◽  
Author(s):  
Pawar A B ◽  
Jawale M A ◽  
Kyatanavar D N
Keyword(s):  
Language Processing ◽  
Gradient Descent ◽  
Human Life ◽  
Stochastic Gradient ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fake News ◽  
Processing Techniques

Usages of Natural Language Processing techniques in the field of detection of fake news is analyzed in this research paper. Fake news are misleading concepts spread by invalid resources can provide damages to human-life, society. To carry out this analysis work, dataset obtained from web resource OpenSources.co is used which is mainly part of Signal Media. The document frequency terms as TF-IDF of bi-grams used in correlation with PCFG (Probabilistic Context Free Grammar) on a set of 11,000 documents extracted as news articles. This set tested on classification algorithms namely SVM (Support Vector Machines), Stochastic Gradient Descent, Bounded Decision Trees, Gradient Boosting algorithm with Random Forests. In experimental analysis, found that combination of Stochastic Gradient Descent with TF-IDF of bi-grams gives an accuracy of 77.2% in detecting fake contents, which observes with PCFGs having slight recalling defects

scholarly journals Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning

10.2196/16042 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e16042
Author(s):  
Emily R Pfaff ◽  
Miles Crosskey ◽  
Kenneth Morton ◽  
Ashok Krishnamurthy
Keyword(s):  
Machine Learning ◽  
Clinical Features ◽  
Language Processing ◽  
Predictive Value ◽  
Cross Validation ◽  
Uterine Fibroids ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Nonalcoholic Fatty Liver ◽  
Clinical Annotation

Computable phenotypes are algorithms that translate clinical features into code that can be run against electronic health record (EHR) data to define patient cohorts. However, computable phenotypes that only make use of structured EHR data do not capture the full richness of a patient’s medical record. While natural language processing (NLP) methods have shown success in extracting clinical features from text, the use of such tools has generally been limited to research groups with substantial NLP expertise. Our goal was to develop an open-source phenotyping software, Clinical Annotation Research Kit (CLARK), that would enable clinical and translational researchers to use machine learning–based NLP for computable phenotyping without requiring deep informatics expertise. CLARK enables nonexpert users to mine text using machine learning classifiers by specifying features for the software to match in clinical notes. Once the features are defined, the user-friendly CLARK interface allows the user to choose from a variety of standard machine learning algorithms (linear support vector machine, Gaussian Naïve Bayes, decision tree, and random forest), cross-validation methods, and the number of folds (cross-validation splits) to be used in evaluation of the classifier. Example phenotypes where CLARK has been applied include pediatric diabetes (sensitivity=0.91; specificity=0.98), symptomatic uterine fibroids (positive predictive value=0.81; negative predictive value=0.54), nonalcoholic fatty liver disease (sensitivity=0.90; specificity=0.94), and primary ciliary dyskinesia (sensitivity=0.88; specificity=1.0). In each of these use cases, CLARK allowed investigators to incorporate variables into their phenotype algorithm that would not be available as structured data. Moreover, the fact that nonexpert users can get started with machine learning–based NLP with limited informatics involvement is a significant improvement over the status quo. We hope to disseminate CLARK to other organizations that may not have NLP or machine learning specialists available, enabling wider use of these methods.

scholarly journals Detection of Online Fake News Using Blending Ensemble Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Arvin Hansrajh ◽  
Timothy T. Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Fake News ◽  
Learning Models ◽  
Linear Discriminant ◽  
Proposed Model ◽  
Machine Learning Models

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

scholarly journals Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

Logistics ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Nikolaos Servos ◽  
Xiaodi Liu ◽  
Michael Teucke ◽  
Michael Freitag
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tracking Data ◽  
Travel Time Prediction ◽  
Adaptive Boosting ◽  
Time Prediction ◽  
The Usa

Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches.

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