A Novel Approach in Feature Selection Method for Text Document Classification

In this paper, a novel approach is proposed for extract eminence features for classifier. Instead of traditional feature selection techniques used for text document classification. We introduce a new model based on probability and over all class frequency of term. We applied this new technique to extract features from training text documents to generate training set for machine learning. Using these machine learning training set to automatic classify documents into corresponding class labels and improve the classification accuracy. The results on these proposed feature selection method illustrates that the proposed method performs much better than traditional methods. DOI: 10.17762/ijritcc2321-8169.15075

Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection

Social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Social networks, such as Twitter, are common mechanisms through which people can share information. The utilization of data that are available through social media for many applications is gradually increasing. Redundancy and noise in short texts are common problems in social media and in different applications that use short text. However, the shortness and high sparsity of short text lead to poor classification performance. Employing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. This research aims to investigate and develop solutions for feature discrimination and selection in short texts classification. For feature discrimination, we introduce a term weighting approach namely, simple supervised weight (SW), which considers the special nature of short text in terms of term strength and distribution. To address the drawbacks of using existing feature selection with short text, this thesis proposes a filter-wrapper feature selection approach. In the first stage, we propose an adaptive filter-based feature selection method that is derived from the odd ratio method, used in reducing the dimensionality of feature space. In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature

Effective Feature Selection Methods for User Sentiment Analysis using Machine Learning

Text classification is the method of allocating a particular piece of text to one or more of a number of predetermined categories or labels. This is done by training a machine learning model on a labeled dataset, where the texts and their corresponding labels are provided. The model then learns to predict the labels of new, unseen texts. Feature selection is a significant step in text classification as it helps to identify the most relevant features or words in the text that are useful for predicting the label. This can include things like specific keywords or phrases, or even the frequency or placement of certain words in the text. The performance of the model can be improved by focusing on the features that are most important to the information that is most likely to be useful for classification. Additionally, feature selection can also help to reduce the dimensionality of the dataset, making the model more efficient and easier to interpret. A method for extracting aspect terms from product reviews is presented in the research paper. This method makes use of the Gini index, information gain, and feature selection in conjunction with the Machine learning classifiers. In the proposed method, which is referred to as wRMR, the Gini index and information gain are utilized for feature selection. Following that, machine learning classifiers are utilized in order to extract aspect terms from product reviews. A set of customer testimonials is used to assess how well the projected method works, and the findings indicate that in terms of the extraction of aspect terms, the method that has been proposed is superior to the method that has been traditionally used. In addition, the recommended approach is contrasted with methods that are currently thought of as being state-of-the-art, and the comparison reveals that the proposed method achieves superior performance compared to the other methods. In general, the method that was presented provides a promising solution for the extraction of aspect terms, and it can also be utilized for other natural language processing tasks

Optimal feature selection for learning-based algorithms for sentiment classification

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment classification performance of the learning-based methods. Therefore, we investigate the relationship between the number of features selected and the sentiment classification performance of the learning-based methods. A new method for the selection of a suitable number of features is proposed in which the Chi Square feature selection algorithm is employed and the features are selected using a preset score threshold. It is discovered that there is a relationship between the logarithm of the number of features selected and the sentiment classification performance of the learning-based method, and it is also found that this relationship is independent of the learning-based method involved. The new findings in this research indicate that it is always possible for researchers to select the appropriate number of features for learning-based methods to obtain the best sentiment classification performance. This can guide researchers to select the proper features for optimizing the performance of learning-based algorithms. (A preliminary version of this paper received a Best Paper Award at the International Conference on Extreme Learning Machines 2018.)Accepted versio

An Approach for Optimal Feature Subset Selection using a New Term Weighting Scheme and Mutual Information

With the development of the web, large numbers of documents are available on the Internet and they are growing drastically day by day. Hence automatic text categorization becomes more and more important for dealing with massive data. However the major problem of document categorization is the high dimensionality of feature space.  The measures to decrease the feature dimension under not decreasing recognition effect are called the problems of feature optimum extraction or selection. Dealing with reduced relevant feature set can be more efficient and effective. The objective of feature selection is to find a subset of features that have all characteristics of the full features set. Instead Dependency among features is also important for classification. During past years, various metrics have been proposed to measure the dependency among different features. A popular approach to realize dependency is maximal relevance feature selection: selecting the features with the highest relevance to the target class. A new feature weighting scheme, we proposed have got a tremendous improvements in dimensionality reduction of the feature space. The experimental results clearly show that this integrated method works far better than the others

Improving the Operation of Text Categorization Systems with Selecting Proper Features Based on PSO-LA

With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

 The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data

TEXT CLASSIFICATION USING NAIVE BAYES UPDATEABLE ALGORITHM IN SBMPTN TEST QUESTIONS

Document classification is a growing interest in the research of text mining. Classification can be done based on the topics, languages, and so on. This study was conducted to determine how Naive Bayes Updateable performs in classifying the SBMPTN exam questions based on its theme. Increment model of one classification algorithm often used in text classification Naive Bayes classifier has the ability to learn from new data introduces with the system even after the classifier has been produced with the existing data. Naive Bayes Classifier classifies the exam questions based on the theme of the field of study by analyzing keywords that appear on the exam questions. One of feature selection method DF-Thresholding is implemented for improving the classification performance. Evaluation of the classification with Naive Bayes classifier algorithm produces 84,61% accuracy

A pipeline and comparative study of 12 machine learning models for text classification

Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text classification have been proposed and incorporated into the services of most email providers. However, optimising text classification algorithms and finding the right tradeoff on their aggressiveness is still a major research problem. We present an updated survey of 12 machine learning text classifiers applied to a public spam corpus. A new pipeline is proposed to optimise hyperparameter selection and improve the models' performance by applying specific methods (based on natural language processing) in the preprocessing stage. Our study aims to provide a new methodology to investigate and optimise the effect of different feature sizes and hyperparameters in machine learning classifiers that are widely used in text classification problems. The classifiers are tested and evaluated on different metrics including F-score (accuracy), precision, recall, and run time. By analysing all these aspects, we show how the proposed pipeline can be used to achieve a good accuracy towards spam filtering on the Enron dataset, a widely used public email corpus. Statistical tests and explainability techniques are applied to provide a robust analysis of the proposed pipeline and interpret the classification outcomes of the 12 machine learning models, also identifying words that drive the classification results. Our analysis shows that it is possible to identify an effective machine learning model to classify the Enron dataset with an F-score of 94%.Comment: This article has been accepted for publication in Expert Systems with Applications, April 2022. Published by Elsevier. All data, models, and code used in this work are available on GitHub at https://github.com/Angione-Lab/12-machine-learning-models-for-text-classificatio