Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis

Researches on sentiment analysis are growing to a great extent and attracting wide ranges of attention from academics and industries as well. Feature generation and selection are consequent for text mining as the high dimensional feature set can affect the performance of sentiment analysis. This paper exhibits the efficacy of the proposed combined feature selection technique on machine learning classification algorithms over their individual usefulness. Initially, we transform the review datasets into the feature vector of unigram features along with bi-tagged features based on POS pattern. Next, information gain (IG), Chi squared (χ2) and minimum redundancy maximum relevancy (mRMR) feature selection methods are applied to obtain an optimal feature subset for further functionality. These features are then given input to multiple machine learning classifiers, namely, support vector machine (SVM), multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB) and logistic regression (LR) on multi domain product review datasets. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the feature selection method mRMR with SVM achieved a better accuracy of 91.39, which is encouraging and comparable to the related research.

Sentiment analysis in its simplest form is the classification of a piece of text into positive or negative class based on the polarity of the text. Horoscopes consist of future predictions for each of the twelve zodiac signs and are very popular in India. All major TV channels and newspapers publish their horoscope expert's predictions on a daily basis. These daily horoscopes are well suited for the task of sentiment analysis as they have a high percentage of strong sentiment bearing sentences. This work deals with sentiment analysis of Bengali daily horoscope. A corpus of 6000 sentences is created by crawling through the website of a leading Bengali newspaper's daily horoscope section. Each sentence is annotated with polarity (positive or negative) by a team of three independent annotators. A lexicon of 58 stop words is also created from the frequently occurring words in the corpus. A comparative analysis of five well known classification algorithms namely Naive Bayes, Support Vector Machines, k-Nearest Neighbours, Decision Tree and Random Forest is done. For each classification algorithm three different input features (unigram, bigram and trigram presence) are experimented with. Stop word removal and feature selection using information gain metric are also used. SVM with all unigram features neither removing stop words nor using information gain metric for feature selection proves to be the best combination producing an accuracy of 98.7%.

Document sentiment analysis is the task of determining whether a document has a positive, negative or neutral sentiment. It is made up of subtasks including feature extraction, feature selection and sentiment classification. Feature selection is the task of selecting relevant features that can aid the classifier to produce better results. This paper focuses on comparing the classification performances based on several feature selection methods used to select relevant features and also to minimize the document-term matrix representation of the documents. The purpose of applying feature selection besides selecting relevant features is also to reduce the number of features to preserve the efficiency of the whole system. In this work, the experiment setup is designed in order to investigate the effectiveness of several selected feature selection methods in improving the sentiment analysis results. Based on the findings from the experiment, although common feature selection methods such as Document Frequency (DF), Information Gain (IG) and Chi-Squared Statistics (CSS) are found to be able to produce high sentiment analysis accuracy, the Categorical Probability Proportional Difference (CPPD) method is found to be more effective as it produces higher performance accuracy in classifying the documents based on the sentiments. Although, the Categorical Proportional Difference (CPD) method produces acceptable classification results, it is weak in reducing the number of features. In short, the CPPD method enables the sentiment analysis task to be conducted with higher accuracy rate couples with high feature reduction rate too.

Online social media is used to show the sentiments of different individuals about various subjects. Sentiment analysis or opinion mining has recently been considered as one of the highly dynamic research fields in natural language processing, Web mining, and machine learning. There has been a very limited amount of research that focuses on sentiment analysis in the Malay language. This study investigates how feature selection methods contribute to the improvement of Malay sentiment classification performance. Three supervised machine-learning classifiers and seven feature selection methods are used to conduct a series of experiments for the effective selection of the appropriate methods for the automatic sentiment classification of online Malay-written reviews. Findings show that the classifications of Malay sentiment improve using feature selections approaches. This work demonstrates that all feature reduction methods generally improve classifier performance. Support Vector Machine (SVM) approach provide the highest accuracy performance of features selection in order to classify Malay sentiment comparing with other classifications approaches such as PCA and CHI square. SVM records 87% as experimental accuracy result of feature selection.

High-dimensional datasets can lead to overfitting and computationally expensive model building on machine learning. This study uses a dimensionality reduction technique, namely feature selection techniques, to overcome these problems. Five feature selection methods were used, i.e., Chi-Square (CS), Information Gain (IG), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Least Absolute Shrinkage and Selection Operator (LASSO), and three classifier methods viz. Naïve Bayes, Extreme Gradient Boosting (XGB), and RF Classifier. The dataset used is the Heart Attack Analysis & Prediction Dataset. In this study, three scenarios of the best feature selection were carried out, namely: 1. selection of the best feature using a specific feature selection, 2. the intersection of selection of the best feature from the same category, 3. the intersection of selection of the best feature from the five proposed feature selection methods. The performance model is measured using accuracy, precision, recall, f1-score, AUC, and training time. This study reveals that feature selection is very effective in improving the performance of prediction models. Based on the experiment results, the best feature selection is CS and IG in the Filter Category with the XGB model. The best feature selected improved the performance of accuracy, precision, recall, f1-score, and AUC, i.e., 1.7%, 1%, 2.3%, 1.6%, and 0.2%, respectively. Meanwhile, training time requirements decreased by 23.5%. Feature selection with specific techniques performs better than feature selection by selecting the best features from the same category feature selection technique or various other feature selection methods.

The paper proposes a hybrid approach to reduce dimension in text classification problems, to overcome the issue of Curse of Dimensionality. This hybrid approach is a combination of Feature Selection (FS) and Feature Extraction (FE) methods, considering different aspects of feature relevance, to effectively reduce the dimension in large text datasets. It prevents feature selection biased in favor of a particular FS method. Many FS methods like Term Variance, Document Frequency, Information Gain, Shannons Entropy measure, Mean-Median and Mean Absolute Difference, were implemented and a comparative study was made on their performance when implemented in a hybrid system. The features selected by the individual FS methods are merged using three approaches, namely, Union, Intersection and Modified Union. The sub lists of features further undergo Feature Extraction by PCA, and the reduced feature sub list is clustered with k-means. Finally, the sentiment-score of the individual clusters are calculated using SentiWordNet database which gives the polarity of the data. The experiments were conducted on the benchmark datasets namely Reuters-21,578 and Classic4. The performance evaluation of the system made using the measures like precision, recall, f-score and accuracy shows that the proposed method has improved performance compared to its competitive methods.

Hyperspectral images (HSIs) generally contain a large amount of spectral bands (features), and the redundant information in them will cause the Hughes phenomenon in the classification process. And feature extraction and feature selection are the two main existing methods to effectively reduce the redundancy of spectral information in the field of HSIs classification. Compared with feature extraction methods, feature selection methods can preserve most of the features of the original HSIs data without losing their valuable details. However, most existing feature selection methods based on single scene (domain) perform poorly in some scenes (domains) with insufficient labeled samples. Therefore, how to adopt an efficient feature selection method to select the optimal feature subsets of source scene and target scene and use the sample information of source scene to assist in the classification of target scene so as to improve the classification accuracy of images in the target scene as much as possible is still very challenging. In order to solve the above problem, this paper proposes a new cross-scene algorithm: Improved Ant Colony Optimization Algorithm-Based Cross-Scene Feature Selection Algorithm (IMACO-CSFS). In order to obtain more accurate feature subsets of the two scenes, IMACO-CSFS proposes a priority sorting-based ant colony strategy to make the subsequent search process focus on the global optimal solution (optimal feature subset) found in the previous iteration. In addition, in order to further accelerate the convergence speed of the global optimal solution, an ant colony strategy based on elite ants is proposed in IMACO-CSFS to more efficiently obtain the optimal feature subsets of the two scenes for training the classifier. Furthermore, this paper simultaneously considers overall classification accuracies of the optimal feature subsets for both scenes and dynamically adjusts their scale to ensure the consistency of the selected features between the two scenes, attenuating the effect of spectral shift and achieving the higher image classification accuracy in the target scene. Experimental results on three cross-scene HSI data pairs demonstrate that IMACO-CSFS is superior in cross-scene feature selection and cross-scene HSIs classification.

Sentiment analysis is to extract the opinion of the user from of the text documents. Sentiment classification using machine learning methods face problem of handing huge number of unique terms in a feature vector for the classification. Therefore, a feature selection method is required to eliminate the irrelevant and noisy features from the feature vector for efficient working of ML algorithms. Rought set Theory based feature selection method is not good at Chinese text although it did well in English text. In this paper, improved feature selection methods are proposed which are based on rough set theory and adapt to the Chinese micro blog. We name them as IGAR (IG and Rough set) and CHIAR (CHI and Rough set). The performance of the improved feature selection methods are compared with Information Gain (IG) method which has been identified as one of the best feature selection method for sentiment classification. Experimentation of improved feature selection methods was performed on two datasets which are extracted from Sina microblog. Experimental results show that improved feature selection methods outperform other feature selection.

In today's technology, people are starting to share their opinions, ideas and feelings through many mediums because the internet is used extensively by every segment. These shares have become an important source of work on sentiment analysis and have led to increased work on this field. The sentiment analysis is simply to determine whether the emotion is included or not, and to determine whether the emotion is positive, negative, or neutral. In this study, chi-square, information gain, gain ratio, gini coefficient, oneR and reliefF methods are applied on the data sets according to the contents of movie comments and the obtained data sets are classified by Support Vector Machines (SVM). As a result of the application, it has been observed that the feature selection methods improve the results of sentiment analysis.

Computer-aided diagnosis has the potential of increasing diagnostic accuracy by providing a second reading to radiologists. In many computerized schemes, numerous features can be extracted to describe suspect image regions. A subset of these features is then employed in a data classifier to determine whether the suspect region is abnormal or normal. Different subsets of features will, in general, result in different classification performances. A feature selection method is often used to determine an "optimal" subset of features to use with a particular classifier. A classifier performance measure (such as the area under the receiver operating characteristic curve) must be incorporated into this feature selection process. With limited datasets, however, there is a distribution in the classifier performance measure for a given classifier and subset of features. In this paper, we investigate the variation in the selected subset of "optimal" features as compared with the true optimal subset of features caused by this distribution of classifier performance. We consider examples in which the probability that the optimal subset of features is selected can be analytically computed. We show the dependence of this probability on the dataset sample size, the total number of features from which to select, the number of features selected, and the performance of the true optimal subset. Once a subset of features has been selected, the parameters of the data classifier must be determined. We show that, with limited datasets and/or a large number of features from which to choose, bias is introduced if the classifier parameters are determined using the same data that were employed to select the "optimal" subset of features.

Stock market prediction is a very noisy problem and the use of any additional information to increase accuracy is necessary. In this paper, for the stock daily return prediction problem, the set of features is expanded to include indicators not only for the stock to be predicted itself but also a set of other stocks and currencies. Afterwards, different feature selection and classification methods are utilized for prediction. The daily close returns of the 3 most traded stocks (GARAN, THYAO, and ISCTR) in Borsa Istanbul (BIST) are predicted using indicators computed on those stocks, indicators for all the other stocks listed in the BIST100 index, and indicators on the dollar-gold prices. Twenty-five different indicators on daily stock prices are computed to form feature vectors for each trading day. These feature vectors are assigned class labels according to the daily close returns. Expanding the feature space with BIST100 stocks features results in a high dimensional feature space, with possibly noisy or irrelevant features. Therefore, feature selection methods are utilized to select the most informative features. In order to determine relevance scores of features, fast filter-based methods, gain ratio and relief, are used. Experiments are performed based on individual stock features, dollar-gold features (DG), BIST100 stock features (BIST100), and a combination of BIST100 and DG with and without feature selection. Using the gain ratio feature selection with a gradient boosting machine (GBM), the movements of GARAN stock were predicted with an accuracy of 0.599 and an F-measure of 0.614. For THYAO, the relief feature selection with the GBM gave an accuracy of 0.558, and for ISCTR, the gain ratio feature selection with logistic regression achieved an accuracy of 0.581. It was found that using BIST100 stock features boosts classification results for all stocks in terms of accuracy.

The rapid advancement of technology has encouraged the use of Artificial Intelligence in various sectors, including higher education. Among students, Artificial Intelligence was used to support learning activities, although it posed ethical concerns such as technological dependency and the risk of plagiarism. This study aimed to examine students’ sentiment toward the use of Artificial Intelligence in academic contexts. Data were collected through an online survey with a total of 498 responses, consisting of 347 positive sentiments and 151 negative sentiments. The classification process employed the Multinomial Naïve Bayes (MNB) algorithm using training data proportions of 60%, 70%, 80%, and 90%, along with combinations of unigram, bigram, and trigram features. The highest accuracy of 0.84 was obtained using 90% training data with the combination of all three features. As a comparison, the Random Forest algorithm achieved its highest accuracy of 0.86 using unigram features with the same training proportion. The results showed that both algorithms performed well in sentiment classification, with Random Forest slightly outperforming. Furthermore, the findings revealed variations in students’ adherence to academic ethics regarding the use of Artificial Intelligence.

Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base. To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction. This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Sentiment Analysis (SA) research has increased tremendously in recent times. Sentiment analysis deals with the methods that automatically process the text contents and extract the opinion of the users. In this paper, unigram and bi-grams are extracted from the text, and composite features are created using them. Part of Speech (POS) based features adjectives and adverbs are also extracted. Information Gain (IG) and Minimum Redundancy Maximum Relevancy (mRMR) feature selection methods are used to extract prominent features. Further, effect of various feature sets for sentiment classification is investigated using machine learning methods. Effects of different categories of features are investigated on four standard datasets i.e. Movie review, product (book, DVD and electronics) review dataset. Experimental results show that composite features created from prominent features of unigram and bi-gram perform better than other features for sentiment classification. mRMR is better feature selection method as compared to IG for sentiment classification. Boolean Multinomial Naïve Bayes (BMNB) algorithm performs better than Support Vector Machine (SVM) classifier for sentiment analysis in terms of accuracy and execution time.KeywordsSentiment Analysisfeature selection methodsmachine learningInformation GainMinimum Redundancy Maximum Relevancy (mRMR)composite features