Feature Selection using Random Forest Method for Sentiment Analysis

Jotheeswaran, Jeevanandam; Koteeswaran, S.

doi:10.17485/ijst/2016/v9i3/86387

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…An experiment was performed using the WEKA Explorer classification tool [29]. Some of the well-known classifiers used for our comparative study included Decision tree, Sequential minimal optimization (SMO), Naive Bayes, Random Forests, K-Nearest neighbor (KNN) [30][31] etc. The output feature matrix was converted into an (Attribute-Relation File Format (ARFF) file, which describes a list of instances, sharing a set of attributes.…”

Section: Resultsmentioning

confidence: 99%

“…The pre-process tab in WEKA [29] enables loading and processing the feature matrix. All the classifiers such Decision tree, SMO, Naï ve Bayes, Random Forests, K-Nearest Neighbor [30][31] chose default parameter settings Naive Bayes [30][31] is the most commonly used classifier for its simple probabilistic classification. It is based on Bayes theorem with strong independent assumptions.…”

Section: Resultsmentioning

confidence: 99%

“…It is based on Bayes theorem with strong independent assumptions. SMO classifier [30][31] was used to resolve quadratic programming complications that emerge via the training instructions of support vector machines. SMO globally changes all lost values and translates formal attributes into binary values.…”

Section: Resultsmentioning

confidence: 99%

“…Random Forest is a random tree and it is an ensemble learning algorithm that generates many individual learners [30][31]. It employs a bagging idea to produce a random set of data for the construction of a decision tree.…”

Section: Resultsmentioning

confidence: 99%

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Aspect Sentiment Identification using Random Fourier Features

Thara¹,

S²

2018

IJISA

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The objective of the paper was to show the effectiveness of using random Fourier features in detection of sentiment polarities. The method presented in this paper proves that detection of aspect based polarities can be improved by selective choice of relevant features and mapping them to lower dimensions. In this study, random Fourier features were prepared corresponding to the polarity data. A regularized least square strategy was adopted to fit a model and perform the task of polarity detection Experiments were performed with 10 cross-validations. The proposed method with random Fourier features yielded 90% accuracy over conventional classifiers. Precision, Recall, and F-measure were deployed in our empirical evaluations.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Aspect Sentiment Identification using Random Fourier Features

Thara¹,

S²

2018

IJISA

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“…This is the fundamentalreason for the increased use of random forest (RF) techniques in variable selection. However, methods like multiple imputation and complete case analysiscould be employed when the data embodies missing values.In theirstudy (Jotheeswaran & Koteeswaran, 2016) compared RF with Principal component analysis (PCA) and Decision Trees (DT) in variable selection and concluded that using RF, the precision of classifiers improved than the others. In his study to downscale temperatures on the land surface, (Hutengs & Vohland, 2016), used RF to select the critical variables suggesting the number of variables included influenced the importance score, while a change in the importance score could also be attributed to predictors changing or replaced (Aldrich & Auret, 2010), while investigating fault conditions, employed RF to identify variables in the process that had high contribution to faultiness.…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree-Based Classification Framework for Used Oil Analysis Applying Random Forest Feature Selection

2018

JASETD

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Lubricant condition monitoring (LCM), part of condition monitoring techniques under Condition Based Maintenance, monitors the condition and state of the lubricant which reveal the condition and state of the equipment. LCM has proved and evidenced to represent a key concept driving maintenance decision making involving sizeable number of parameter (variables) tests requiring classification and interpretation based on the lubricant’s condition. Reduction of the variables to a manageable and admissible level and utilization for prediction is key to ensuring optimization of equipment performance and lubricant condition. This study advances a methodology on feature selection and predictive modelling of in-service oil analysis data to assist in maintenance decision making of critical equipment. Proposed methodology includes data pre-processing involving cleaning, expert assessment and standardization due to the different measurement scales. Limits provided by the Original Equipment Manufacturers (OEM) are used by the analysts to manually classify and indicate samples with significant lubricant deterioration. In the last part of the methodology, Random Forest (RF) is used as a feature selection tool and a Decision Tree-based (DT) classification of the in-service oil samples. A case study of a thermal power plant is advanced, to which the framework is applied. The selection of admissible variables using Random Forest exposes critical used oil analysis (UOA) variables indicative of lubricant/machine degradation, while DT model, besides predicting the classification of samples, offers visual interpretability of parametric impact to the classification outcome. The model evaluation returned acceptable predictive, while the framework renders speedy classification with insights for maintenance decision making, thus ensuring timely interventions. Moreover, the framework highlights critical and relevant oil analysis parameters that are indicative of lubricant degradation; hence, by addressing such critical parameters, organizations can better enhance the reliability of their critical operable equipment.

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Prediction of Face Emotion with Labelled Selective Transfer Machine as a Generalized Emotion Classifier

Pandit

Jadhav

2023

Communications in Computer and Information Science

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Feature Selection using Random Forest Method for Sentiment Analysis

Cited by 15 publications

References 16 publications

Aspect Sentiment Identification using Random Fourier Features

Aspect Sentiment Identification using Random Fourier Features

A Decision Tree-Based Classification Framework for Used Oil Analysis Applying Random Forest Feature Selection

Prediction of Face Emotion with Labelled Selective Transfer Machine as a Generalized Emotion Classifier

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