Exploring Automated Text Classification to Improve Keyword Corpus Search Results for Bioinspired Design

2014 ◽  
Vol 136 (11) ◽  
Author(s):  
Michael W. Glier ◽  
Daniel A. McAdams ◽  
Julie S. Linsey
Keyword(s):  
Text Mining ◽  
Text Classification ◽  
Keyword Search ◽  
Idea Generation ◽  
Support Vector ◽  
Biological Knowledge ◽  
Svm Classifier ◽  
Search Results ◽  
Bioinspired Design ◽  
Mining Algorithms

Bioinspired design is the adaptation of methods, strategies, or principles found in nature to solve engineering problems. One formalized approach to bioinspired solution seeking is the abstraction of the engineering problem into a functional need and then seeking solutions to this function using a keyword type search method on text based biological knowledge. These function keyword search approaches have shown potential for success, but as with many text based search methods, they produce a large number of results, many of little relevance to the problem in question. In this paper, we develop a method to train a computer to identify text passages more likely to suggest a solution to a human designer. The work presented examines the possibility of filtering biological keyword search results by using text mining algorithms to automatically identify which results are likely to be useful to a designer. The text mining algorithms are trained on a pair of surveys administered to human subjects to empirically identify a large number of sentences that are, or are not, helpful for idea generation. We develop and evaluate three text classification algorithms, namely, a Naïve Bayes (NB) classifier, a k nearest neighbors (kNN) classifier, and a support vector machine (SVM) classifier. Of these methods, the NB classifier generally had the best performance. Based on the analysis of 60 word stems, a NB classifier's precision is 0.87, recall is 0.52, and F score is 0.65. We find that word stem features that describe a physical action or process are correlated with helpful sentences. Similarly, we find biological jargon feature words are correlated with unhelpful sentences.

scholarly journals Text Classification of Cornell Movie Data using Data Mining with Feature Selection

2019 ◽  
Vol 9 (2) ◽  
pp. 2950-2955
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Text Classification ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Chi Square ◽  
Feature Selection Technique ◽  
Data Set

Text Classification is branch of text mining through which we can analyze the sentiment of the movie data. In this research paper we have applied different preprocessing techniques to reduce the features from cornell movie data set. We have also applied the Correlation-based feature subset selection and chi-square feature selection technique for gathering most valuable words of each category in text mining processes. The new cornell movie data set formed after applying the preprocessing steps and feature selection techniques. We have classified the cornell movie data as positive or negative using various classifiers like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naive Bayes (NB), Bays Net (BN) and Random Forest (RF) classifier. We have also compared the classification accuracy among classifiers and achieved better accuracy i. e. 87% in case of SVM classifier with reduced number of features. The suggested classifier can be useful in opinion of movie review, analysis of any blog and documents etc.

scholarly journals The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

2020 ◽  
pp. 3397-3407
Author(s):  
Nur Syafiqah Mohd Nafis ◽  
Suryanti Awang
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Training Dataset ◽  
Recursive Feature Elimination ◽  
High Dimensional ◽  
Significant Feature ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Documents ◽  
Text Document

Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document.

scholarly journals Implementation of Integrated Bayes Formula and Support Vector Machine for Analysing Airline’s Passengers Review

2020 ◽  
Vol 202 ◽  
pp. 15004
Author(s):  
Aditya Tegar Satria ◽  
Mustafid ◽  
Dinar Mutiara Kusumo Nugraheni
Keyword(s):  
Support Vector Machine ◽  
Information System ◽  
Text Mining ◽  
Input Data ◽  
Tourism Industry ◽  
Support Vector ◽  
Accuracy Score ◽  
Bayes Formula ◽  
Performance Results ◽  
Mining Algorithms

Nowadays, the utilization of Internet of Things (IoT) is commonly used in the tourism industry, including aviation, where passengers of flight services can rate their satisfaction levels towards the product and service they use by writing their reviews in the form of text-based data on many popular websites. These passenger reviews are collections of potential big data and can be analyzed in order to extract meaningful informations. Some text mining algorithms are already in common use, including the Bayes formula and Support Vector Machine methods. This research proposes an implementation of the Bayes and SVM methods where these algorithms will operate independently yet integrated with other modules such as input data, text pre-processing and shows output result concisely in one single information system. The proposed system was successfully delivered 1000 documents of passenger reviews as input data, then after implemented the pre-processing method, the Bayes formula was used to classify the document reviews into 5 categories, including plane condition, flight comfort, staff service, food and entertainment, and price. While simultanously, the positive and negative sentiment contained in the review document was analyzed with SVM method and shows the accuracy score of 83.6% for a training to testing set ratio of 50:50, while 82.75% accuracy for the 60:40 ratio, and 83.3% accuracy for the 70:30 ratio. This research shows that two different text mining algorithms can be implemented simultaneously in a effective and efficient way, while still providing an accurate and satisfying performance results in one integrated information system.

The Effect of Stemming on Arabic Text Classification

2011 ◽  
Vol 1 (3) ◽  
pp. 54-70 ◽  
Author(s):  
Abdullah Wahbeh ◽  
Mohammed Al-Kabi ◽  
Qasem Al-Radaideh ◽  
Emad Al-Shawakfa ◽  
Izzat Alsmadi
Keyword(s):  
Text Classification ◽  
Digital Libraries ◽  
Arabic Language ◽  
Support Vector ◽  
Svm Classifier ◽  
Arabic Text ◽  
Text Documents ◽  
Information Retrieval Systems ◽  
Arabic Text Classification ◽  
The Web

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%.

An Effective K-Means Clustering Based SVM Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1344-1348
Author(s):  
Yu Kai Yao ◽  
Yang Liu ◽  
Zhao Li ◽  
Xiao Yun Chen
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Small Subset ◽  
Training Set ◽  
Data Mining Algorithms ◽  
Svm Algorithm ◽  
Svm Model ◽  
Separating Hyperplane ◽  
Regression Problems ◽  
Mining Algorithms

Support Vector Machine (SVM) is one of the most popular and effective data mining algorithms which can be used to resolve classification or regression problems, and has attracted much attention these years. SVM could find the optimal separating hyperplane between classes, which afford outstanding generalization ability with it. Usually all the labeled records are used as training set. However, the optimal separating hyperplane only depends on a few crucial samples (Support Vectors, SVs), we neednt train SVM model on the whole training set. In this paper a novel SVM model based on K-means clustering is presented, in which only a small subset of the original training set is selected to constitute the final training set, and the SVM classifier is built through training on these selected samples. This greatly decrease the scale of the training set, and effectively saves the training and predicting cost of SVM, meanwhile guarantees its generalization performance.

scholarly journals Comparison of Random Forest and Support Vector Machine for Indonesian Tweet Complaint Classification

2019 ◽  
pp. 202-207 ◽  
Author(s):  
Desi Ramayanti
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Text Classification ◽  
Research Area ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Documents ◽  
Case Organization

In digital business, the managerial commonly need to process text so that it can be used to support decision-making. The number of text documents contained ideas and opinions is progressing and challenging to understand one by one. Whereas if the data are processed and correctly rendered using machine learning, it can present a general overview of a particular case, organization, or object quickly. Numerous researches have been accomplished in this research area, nevertheless, most of the studies concentrated on English text classification. Every language has various techniques or methods to classify text depending on the characteristics of its grammar. The result of classification among languages may be different even though it used the same algorithm. Given the greatness of text classification, text classification algorithms that can be implemented is the support vector machine (SVM) and Random Forest (RF). Based on the background above, this research is aimed to find out the performance of support vector machine algorithm and random forest in classification of Indonesian text. 1. Result of SVM classifier with cross validation k-10 is derived the best accuracy with value 0.9648, however, it spends computational time as long as 40.118 second. Then, result of RF classifier with values, i.e. 'bootstrap': False, 'min_samples_leaf': 1, 'n_estimators': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None is achieved accuracy is 0.9561 and computational time 109.399 second.

The Effect of Stemming on Arabic Text Classification

2013 ◽  
pp. 207-225 ◽  
Author(s):  
Abdullah Wahbeh ◽  
Mohammed Al-Kabi ◽  
Qasem Al-Radaideh ◽  
Emad Al-Shawakfa ◽  
Izzat Alsmadi
Keyword(s):  
Text Classification ◽  
Digital Libraries ◽  
Arabic Language ◽  
Support Vector ◽  
Svm Classifier ◽  
Arabic Text ◽  
Text Documents ◽  
Information Retrieval Systems ◽  
Arabic Text Classification ◽  
The Web

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%.

scholarly journals Multiclass patent document classification

2017 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Chaitanya Anne ◽  
Avdesh Mishra ◽  
Md Tamjidul Hoque ◽  
Shengru Tu
Keyword(s):  
Text Classification ◽  
Information Gain ◽  
Document Classification ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Practical Reasons ◽  
Patent Document ◽  
Vast Number

Text classification is used in information extraction and retrieval from a given text, and text classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This article addresses patent document classification problem into fifteen different categories or classes, where some classes overlap with each other for practical reasons. For the development of the classification model using machine learning techniques, useful features have been extracted from the given documents. The features are used to classify patent document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k-Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based classification algorithms: Random Forest and J48. The major research steps in this paper consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding pseudo-synthetic data wherever appropriate, which resulted in a superior SVM classifier based model.

scholarly journals Support Vector Machines and Kernel Functions for Text Processing

2013 ◽  
Vol 20 (3) ◽  
pp. 130 ◽  
Author(s):  
Celso Antonio Alves Kaestner
Keyword(s):  
Text Classification ◽  
Learning Algorithm ◽  
Text Processing ◽  
Dimensional Space ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Automatic Text Classification ◽  
Automatic Text

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The “kernel trick”, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions.

Automatic User Domain Classification Based on Support Vector Machine (SVM)

2019 ◽  
Vol 16 (8) ◽  
pp. 3327-3331
Author(s):  
Mercy Paul Selvan ◽  
A. Viji Amutha Mary ◽  
S. Jancy
Keyword(s):  
Support Vector Machine ◽  
Specific Area ◽  
Support Vector ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Publish Or Perish ◽  
Search Results ◽  
Classification Technique ◽  
Two Phases ◽  
Training Standard

Finding domain of a research paper and a researcher is a crucial task and would be highly appreciable in order to provide personalized search results to the user. An automatic user domain classification technique based on SVM has been proposed in this paper in order to determine the domain of a user based on her publications. In this technique, for a given user, his specific area of domain is determined by classifying the keywords from his publication works. It consists of two phases: keyword extraction and domain classification. In keyword extraction phase, the list of publications corresponding to a user mail id is retrieved by using publish or perish tool. From each of the published papers, the keywords are extracted. In domain classification, SVM classifier is applied to determine the domain of the user. This is performed by training standard keywords from each domain into the SVM classifier. If a user belongs to more than one domain, then the primary domain with more publications will be considered.

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