•Text classification is a domain with high dimensional feature space.•Extracting the keywords as the features can be extremely useful in text classification.•An empirical analysis of five statistical ...keyword extraction methods.•A comprehensive analysis of classifier and keyword extraction ensembles.•For ACM collection, a classification accuracy of 93.80% with Bagging ensemble of Random Forest.
Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency-inverse sentence frequency based keyword extraction, co-occurrence statistical information based keyword extraction, eccentricity-based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization). In the study, a comprehensive study of comparing base learning algorithms (Naïve Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F-measure and area under curve values. To validate the empirical analysis, two-way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most-frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80%) is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword-based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification.
•The study describes the essential phases of text classification.•The overview explains the concepts related to text classification.•The quantitative analysis outlines different aspects of text ...classification.•The quantitative analysis uncovers the research trends in text classification.
The aim of this study is to provide an overview the state-of-the-art elements of text classification. For this purpose, we first select and investigate the primary and recent studies and objectives in this field. Next, we examine the state-of-the-art elements of text classification. In the following steps, we qualitatively and quantitatively analyse the related works. Herein, we describe six baseline elements of text classification including data collection, data analysis for labelling, feature construction and weighing, feature selection and projection, training of a classification model, and solution evaluation. This study will help readers acquire the necessary information about these elements and their associated techniques. Thus, we believe that this study will assist other researchers and professionals to propose new studies in the field of text classification.
Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods ...to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms.
In this paper, we present a zero-shot classification approach to document classification in any language into topics which can be described by English keywords. This is done by embedding both labels ...and documents into a shared semantic space that allows one to compute meaningful semantic similarity between a document and a potential label. The embedding space can be created by either mapping into a Wikipedia-based semantic representation or learning cross-lingual embeddings. But if the Wikipedia in the target language is small or there is not enough training corpus to train a good embedding space for low-resource languages, then performance can suffer. Thus, for low-resource languages, we further use a word-level dictionary to convert documents into a high-resource language, and then perform classification based on the high-resource language. This approach can be applied to thousands of languages, which can be contrasted with machine translation, which is a supervision-heavy approach feasible for about 100 languages. We also develop a ranking algorithm that makes use of language similarity metrics to automatically select a good pivot or bridging high-resource language, and show that this significantly improves classification of low-resource language documents, performing comparably to the best bridge possible.
•For the convolutional layer, the convolution window size and the stride size affect the classification performance.•BiLSTM and attention mechanism have greater effects than the convolutional layer ...on the classification accuracy.•Compared to pre-trained embedding word vector, random embedding word vector requires to train more parameters and it causes relatively lower classification accuracy in limited iterations.•Our proposed approach performs better than some state-of-the-art DNNs.
Neural network models have been widely used in the field of natural language processing (NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of arbitrary length, are common methods for sequence modeling tasks. Long short-term memory (LSTM) is one kind of RNNs and has achieved remarkable performance in text classification. However, due to the high dimensionality and sparsity of text data, and to the complex semantics of the natural language, text classification presents difficult challenges. In order to solve the above problems, a novel and unified architecture which contains a bidirectional LSTM (BiLSTM), attention mechanism and the convolutional layer is proposed in this paper. The proposed architecture is called attention-based bidirectional long short-term memory with convolution layer (AC-BiLSTM). In AC-BiLSTM, the convolutional layer extracts the higher-level phrase representations from the word embedding vectors and BiLSTM is used to access both the preceding and succeeding context representations. Attention mechanism is employed to give different focus to the information outputted from the hidden layers of BiLSTM. Finally, the softmax classifier is used to classify the processed context information. AC-BiLSTM is able to capture both the local feature of phrases as well as global sentence semantics. Experimental verifications are conducted on six sentiment classification datasets and a question classification dataset, including detailed analysis for AC-BiLSTM. The results clearly show that AC-BiLSTM outperforms other state-of-the-art text classification methods in terms of the classification accuracy.
Comparing automated text classification methods Hartmann, Jochen; Huppertz, Juliana; Schamp, Christina ...
International journal of research in marketing,
03/2019, Volume:
36, Issue:
1
Journal Article
Peer reviewed
Open access
Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication ...shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.
•To review free-text clinical text classification approaches from six aspects.•In selected studies, mostly content-based and concept-based features were used.•The datasets used in selected studies ...were categorized into four distinct types.•Selected studies used either supervised machine learning or rule-based approaches.•Ten open research challenges are presented in clinical text classification domain.
The pervasive use of electronic health databases has increased the accessibility of free-text clinical reports for supplementary use. Several text classification approaches, such as supervised machine learning (SML) or rule-based approaches, have been utilized to obtain beneficial information from free-text clinical reports. In recent years, many researchers have worked in the clinical text classification field and published their results in academic journals. However, to the best of our knowledge, no comprehensive systematic literature review (SLR) has recapitulated the existing primary studies on clinical text classification in the last five years. Thus, the current study aims to present SLR of academic articles on clinical text classification published from January 2013 to January 2018. Accordingly, we intend to maximize the procedural decision analysis in six aspects, namely, types of clinical reports, data sets and their characteristics, pre-processing and sampling techniques, feature engineering, machine learning algorithms, and performance metrics. To achieve our objective, 72 primary studies from 8 bibliographic databases were systematically selected and rigorously reviewed from the perspective of the six aspects. This review identified nine types of clinical reports, four types of data sets (i.e., homogeneous–homogenous, homogenous–heterogeneous, heterogeneous–homogenous, and heterogeneous–heterogeneous), two sampling techniques (i.e., over-sampling and under-sampling), and nine pre-processing techniques. Moreover, this review determined bag of words, bag of phrases, and bag of concepts features when represented by either term frequency or term frequency with inverse document frequency, thereby showing improved classification results. SML-based or rule-based approaches were generally employed to classify the clinical reports. To measure the performance of these classification approaches, we used precision, recall, F-measure, accuracy, AUC, and specificity in binary class problems. In multi-class problems, we primarily used micro or macro-averaging precision, recall, or F-measure. Lastly, open research issues and challenges are presented for future scholars who are interested in clinical text classification. This SLR will definitely be a beneficial resource for researchers engaged in clinical text classification.
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