Multi-class text categorization based on LDA and SVM

Li, Kunlun; Xie, Jing; Sun, Xusheng; Ma, Yinghui; Bai, Hui

doi:10.1016/j.proeng.2011.08.366

Cited by 30 publications

(4 citation statements)

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“…In order to compare our method with other methods in verifying the efficiency of extracting coordinate relationship, we adopt the following two kinds of methods, Naïve Bayesian (NB) [20][21][22][23] and Support Vector Machine (SVM) [24][25][26][27]. The detailed descriptions for these two methods are as follows.…”

Section: B Comparison Methodsmentioning

confidence: 99%

Coordinate relationship extraction on sentence level in Chinese corpus

Sun

Liu

Zhou

2013

2013 Ninth International Conference on Natural Computation (ICNC)

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An important problem in text mining is the automatic extraction of relations. The study of extracting semantic relation between event semantic chunks from sentences is rarely involved in the current research. This paper presents a metric theory on identifying semantic relation based on matching degree of the deictic word and the similarity of event semantic chunk, and discusses the calculation of the similarities of concept and event semantic tree. Finally, a novel method based on ontology is proposed to extract coordinate relation from sentences. This method explores the effect of extracting coordinate relation for each kind of deictic word of coordinate relation and none of deictic word. This paper provides a contrast with the experimental comparison between Ontology-based, Naïve Bayesian (NB) and Support Vector Machine (SVM). The overall performance of Ontology-based method is tested by selecting randomly 100 sentences of coordinate relation and 600 sentences of non-coordinate relation from data corpus. This Experimental datashows that the method is effective.

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Section: B Comparison Methodsmentioning

confidence: 99%

Coordinate relationship extraction on sentence level in Chinese corpus

Sun

Liu

Zhou

2013

2013 Ninth International Conference on Natural Computation (ICNC)

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“…Cao et al [17] introduced the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to measure the relevance between topics and find the optimal LDA model by iteration. Li et al [18] introduced category information into the existing LDA model feature selection algorithm and constructed a Support Vector Machine(SVM) multiclass classifier on the implied topic-text matrix to address the problem that traditional feature selection methods generally ignore the semantic relationships between words. Zhao et al [19] proposed determining the optimal number of topics in the LDA model by the rate of change of the perplexity.…”

Section: B Optimal Topic Number Selection In Ldamentioning

confidence: 99%

An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification

Zheng,

Jiang,

et al. 2023

IEEE Access

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Nowadays, news text information is exploding, and people need more and more heterogeneous news content. Therefore, news text topic identification is needed to help viewers quickly and accurately screen and filter news related to their interests to save time and energy. The Latent Dirichlet Allocation(LDA) model is the most commonly used method for text topic identification. The optimal number of topics must be specified in advance when using the LDA model to extract topics in previous studies. However, the selection of the too-large or the too-small number of topics significantly impacts the final results of LDA topic models, which directly determines the quality of topic extraction. Moreover, the news text datasets from social media are very time-sensitive, and the combination of temporal and semantic modelling has not been considered in past studies of news topic identification. This paper proposes an adaptive optimal topic number determination method for the fusion of semantic and temporal information in news datasets to address the existing problems. Semantic and temporal are first extracted in this method as two different views. Then, density peak clustering of multi-view information fusion is performed based on the two obtained feature vectors. The clustering results are used as the final optimal number of topics. To demonstrate the effectiveness of the proposed method, this paper compares the performance of four traditional methods for determining the optimal number of topics with the performance of this paper's method on the public datasets. The results show that the optimal number of topics considering semantic and temporal factors is significantly better than the other four traditional methods regarding F-value, PMI scores, MI scores. It performs well in other indicators as well. The above experimental results show that the method proposed in this paper combines the temporal and semantic of news data to determine the optimal number of topics of news text, which can improve the accuracy of selecting the optimal number of topics in the LDA model and the effectiveness of the topic identification of news text to some extent. It can help viewers better understand and utilize the massive news text information. In addition, the method also broadens the idea of identifying and mining unique datasets from multiple perspectives.INDEX TERMS LDA model, multi-view information fusion, optimal number of topics, news topics, social media data mining

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“…Domain Identification: The selected approaches are clustering-based unsupervised categorisers and do not support domain identification. Use of feature selector to select appropriate domain features [49] or classifiers, such as SVM [16], [50] and K-Nearest Neighbor with topic modelling, can identify service domains. A domain-based IoT services classifier could be a potential future research direction.…”

Section: Qualitative Evaluation and Open Research Challengesmentioning

confidence: 99%

Enabling Efficient and Scalable Service Search in IoT With Topic Modeling: An Evaluation

Razzaque

2021

IEEE Access

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Service search in IoT's large-scale, heterogeneous and multi-domain services space is a challenging task. It can take the time that may not be acceptable for many IoT applications and requires resources that may not be available in many IoT devices. A categorisation of these services into their application domains can reduce the search space and offer an efficient and scalable service search. Recently, in many fields, such as short text messages and categorisation of IoT service specifications, generative probabilistic models, like Topic modelling, are being used. Generally, IoT service descriptions are short and sparse. Existing work on IoT services categorisation is based on Latent Dirichlet Allocation (LDA), but it does not perform well in short and sparse texts. Also, IoT services categorisation has few specific issues, which are not well addressed by existing short texts-specific topic modelling approaches. In this paper, we identify these issues and quantitatively and qualitatively evaluate how well a set of selected short textsspecific topic modelling approaches perform as IoT service categorisers against these issues. The results show that these approaches do not perform well in a corpus of noisy APIs descriptions and heterogeneous service descriptions. Also, they do not support domain identification of services, which is essential in domain-based service search. We conclude that integrating an appropriate and comprehensive knowledge base (i.e., domain ontology) could minimise noise and address IoT's APIs and service descriptions' heterogeneity. More importantly, it can identify the domains of those APIs and services.

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Multi-class text categorization based on LDA and SVM

Cited by 30 publications

References 1 publication

Coordinate relationship extraction on sentence level in Chinese corpus

Coordinate relationship extraction on sentence level in Chinese corpus

An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification

Enabling Efficient and Scalable Service Search in IoT With Topic Modeling: An Evaluation

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