scholarly journals Efficient Multilevel Polarity Sentiment Classification Algorithm using Support Vector Machine and Fuzzy Logic

2019 ◽  
Vol 8 (12) ◽  
pp. 5048-5051
Keyword(s):  
Support Vector Machine ◽  
Social Networking Sites ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Support Vector ◽  
Data Set ◽  
Inference System

This paper discusses an efficient algorithm for sentiment classification of online text reviews posted in social networking sites and blogs which are mostly in unstructured and ungrammatical in nature. Model proposed in this paper utilizes support vector machine supervised learning algorithm and fuzzy inference system for enhancing the degree of sentiment polarity of text reviews and providing multilevel polarity categories. Model is also able to predict degree of sentiment polarity of online reviews. The model accuracy is validated on twitter data set and compared with another earlier model.

OKO-SVM: Online kernel optimization-based support vector machine for the incremental learning and classification of the sentiments in the train reviews

2018 ◽  
Vol 09 (06) ◽  
pp. 1850054 ◽  
Author(s):  
Rashmi K. Thakur ◽  
Manojkumar V. Deshpande
Keyword(s):  
Support Vector Machine ◽  
Incremental Learning ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Duration ◽  
Online Incremental Learning ◽  
Kernel Optimization

Online incremental learning is one of the emerging research interests among the researchers in the recent years. The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews. This work has introduced an online incremental learning algorithm for classifying the train reviews. The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service. This work proposes the online kernel optimization-based support vector machine (OKO-SVM) classifier for the sentiment classification of the train reviews. This paper is the extension of the previous work kernel optimization-based support vector machine (KO-SVM). The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time duration. The simulation uses the standard train review and the movie review database for the classification. From the simulation results, it is evident that the proposed model has achieved a better performance with the values of 84.42%, 93.86%, and 74.56% regarding the accuracy, sensitivity, and specificity while classifying the train review database.

Kernel Optimized-Support Vector Machine and MapReduce Framework for Sentiment Classification of Train Reviews

2019 ◽  
Vol 27 (06) ◽  
pp. 1025-1050
Author(s):  
Rashmi K. Thakur ◽  
Manojkumar V. Deshpande
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Mapreduce Framework ◽  
Mapreduce Model

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.

Thermal error modelling of motorised spindle in large-sized gear grinding machine

2017 ◽  
Vol 231 (5) ◽  
pp. 768-778 ◽  
Author(s):  
He Dai ◽  
Shilong Wang ◽  
Xin Xiong ◽  
Baocang Zhou ◽  
Shouli Sun ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Tools ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Support Vector ◽  
Inference System ◽  
Thermal Errors ◽  
Gear Grinding ◽  
Neuro Fuzzy ◽  
Grinding Machine

Thermal errors are one of the most significant factors that influence the machining precision of machine tools. For large-sized gear grinding machine tools, thermal errors of beds, columns and rotary tables are decreased by their huge heat capacity. However, different from machine tools of normal sizes, thermal errors increase with greater power in motorised spindles. Thermal error compensation is generally considered as a relatively effective, convenient and cost-efficient approach in thermal error control and reduction. This article proposes two thermal error prediction models for motorised spindles based on an adaptive neuro-fuzzy inference system and support vector machine, respectively. In the adaptive neuro-fuzzy inference system–based model, the temperature values are divided into different groups using subtractive clustering. A hybrid learning scheme is adopted to adjust membership functions so as to learn from the input data. In the particle swarm optimisation support vector machine–based model, particle swarm optimisation is used to optimise the hyperparameters of the established model. Thermal balance experiments are conducted on a large-sized computer numerical control gear grinding machine tool to establish the prediction models. Comparative results show that the adaptive neuro-fuzzy inference system model has higher prediction accuracy (with residual errors within ±2.5 μm in the radial direction and ±3 μm in the axial direction) than the support vector machine model.

Hybrid wavelet-support vector machine approach for modelling rainfall–runoff process

2016 ◽  
Vol 73 (8) ◽  
pp. 1937-1953 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fuzzy Inference ◽  
The Other ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Model Concept ◽  
Inference System ◽  
Svm Model ◽  
Artificial Intelligence Models

Because of the importance of water resources management, the need for accurate modeling of the rainfall–runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall–runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall–runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Recognition and Classification of Human Activity by Posture Sensing and Machine Learning

2012 ◽  
Vol 468-471 ◽  
pp. 2916-2919
Author(s):  
Fan Yang ◽  
Yu Chuan Wu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Human Activity ◽  
Learning Algorithm ◽  
Optimal Parameter ◽  
Support Vector ◽  
Rbf Kernel ◽  
Human Activity Detection ◽  
Near Future

This paper describes how to use a posture sensor to validate human daily activity and by machine learning algorithm - Support Vector Machine (SVM) an outstanding model is built. The optimal parameter σ and c of RBF kernel SVM were obtained by searching automatically. Those kinematic data was carried out through three major steps: wavelet transformation, Principle Component Analysis (PCA) -based dimensionality reduction and k-fold cross-validation, followed by implementing a best classifier to distinguish 6 difference actions. As an activity classifier, the SVM (Support Vector Machine) algorithm is used, and we have achieved over 94.5% of mean accuracy in detecting differential actions. It shows that the verification approach based on the recognition of human activity detection is valuable and will be further explored in the near future.

Support Vector Machine Based Voltage Relays for Voltage Disturbance Detection in Micro-Distribution Systems

2013 ◽  
Vol 291-294 ◽  
pp. 2084-2090
Author(s):  
Whei Min Lin ◽  
Chia Sheng Tu ◽  
Ting Chia Ou
Keyword(s):  
Support Vector Machine ◽  
Distribution Systems ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Characteristic Curves ◽  
Inference System ◽  
Trigger Time ◽  
Voltage Swell ◽  
Dynamic Errors ◽  
Disturbance Detection

This study proposes combining fuzzy inference system and support vector machine based voltage relays for voltage disturbance detection in micro-distribution systems (MDSs). Moreover, the coordination characteristic curves of the trigger time versus dynamic errors are proposed for under-voltage and over-voltage protection. Modified coordination characteristic curves use a critical trigger time to isolate the faults. An support vector machine (SVM) is a multi-layer decision-making model, which detects voltage disturbances, such as voltage swell, voltage sag, voltage unbalance, and faults. Computer simulations are conducted, using an IEEE 30-bus power system and micro-distribution systems, to show the effectiveness of the proposed voltage relays.

Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

2013 ◽  
Vol 27 (10) ◽  
pp. 3803-3823 ◽  
Author(s):  
Afiq Hipni ◽  
Ahmed El-shafie ◽  
Ali Najah ◽  
Othman Abdul Karim ◽  
Aini Hussain ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Water Levels ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Svm Model
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