Using Lexicon and Random Forest Classifier for Twitter Sentiment Analysis

Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vectorize the text input. The most important features are extracted by the Convolutional Neural Network (CNN). The extracted features are used by the Random Forest(RF) classifier for sentiment classification. The performance of the proposed hybrid CNNRF model is comparedwith the base model such as Convolutional Neural Network (CNN) and Random Forest (RF) classifier. The experimental result shows that the proposed model far beat the existing base models in terms of classification accuracy and effectively integrated genetically-modified CNN with Random Forest classifier.

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The Performance Improvement on 4-FB Face using Random Forest Classifier

Scientific Visualization ◽

10.26583/sv.10.5.01 ◽

2018 ◽

Vol 10 (5) ◽

pp. 1-12

Author(s):

B. Nassih ◽

A. Amine ◽

M. Ngadi ◽

D. Naji ◽

N. Hmina

Keyword(s):

Random Forest ◽

Performance Improvement ◽

Random Forest Classifier

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Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v5i3.1066 ◽

2020 ◽

pp. 235-242

Author(s):

Farrikh Alzami ◽

Erika Devi Udayanti ◽

Dwi Puji Prabowo ◽

Rama Aria Megantara

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Random Forest ◽

Sentiment Analysis ◽

Classification Performance ◽

Document Preparation ◽

Learning Models ◽

Polarity Classification ◽

Negative Sentiment ◽

Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

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Methodology for Malware Classification using a Random Forest Classifier

2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) ◽

10.1109/ropec.2018.8661441 ◽

2018 ◽

Cited By ~ 4

Author(s):

Carlos Domenick Morales-Molina ◽

Diego Santamaria-Guerrero ◽

Gabriel Sanchez-Perez ◽

Hector Perez-Meana ◽

Aldo Hernandez-Suarez

Keyword(s):

Random Forest ◽

Random Forest Classifier ◽

Malware Classification

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Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

Scientific Reports ◽

10.1038/s41598-021-85168-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Elisabeth Sartoretti ◽

Thomas Sartoretti ◽

Michael Wyss ◽

Carolin Reischauer ◽

Luuk van Smoorenburg ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Brain Tumors ◽

Proton Transfer ◽

Multilayer Perceptron ◽

Random Forest Classifier ◽

Amide Proton ◽

Low Grade ◽

Who Grade ◽

Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

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Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05427-8 ◽

2021 ◽

Author(s):

K. J. Paprottka ◽

S. Kleiner ◽

C. Preibisch ◽

F. Kofler ◽

F. Schmidt-Graf ◽

...

Keyword(s):

Random Forest ◽

Tumor Progression ◽

Automated Analysis ◽

Random Forest Classifier ◽

Imaging Modalities ◽

Amide Proton ◽

Tumor Board ◽

Dynamic Susceptibility ◽

Fet Pet ◽

Promising Tool

Abstract Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

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