The Comparison of the Text Classification Methods to be Used for the Analysis of Motion Data in DLP Architect

With the rise and rapid development of short video sharing websites, the number of short videos on the Internet has been growing explosively. The organization and classification of short videos have become the basis for the effective use of short videos, which is also a problem faced by major short video platforms. Aiming at the characteristics of complex short video content categories and rich extended text information, this paper uses methods in the text classification field to solve the short video classification problem. Compared with the traditional way of classifying and understanding short video key frames, this method has the characteristics of lower computational cost, more accurate classification results, and easier application. This paper proposes a text classification model based on the attention mechanism of multitext embedding short video extension. The experiment first uses the training language model Albert to extract sentence-level vectors and then uses the attention mechanism to study the text information in various short video extensions in a short video classification weight factor. And this research applied Google’s unsupervised data augmentation (UDA) method based on unsupervised data, creatively combining it with the Chinese knowledge graph, and realized TF-IDF word replacement. During the training process, we introduced a large amount of unlabeled data, which significantly improved the accuracy of model classification. The final series of related experiments is aimed at comparing with the existing short video title classification methods, classification methods based on video key frames, and hybrid methods, and proving that the method proposed in this article is more accurate and robust on the test set.

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Incorporating Context-Relevant Knowledge into Convolutional Neural Networks for Short Text Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330110067 ◽

2019 ◽

Vol 33 ◽

pp. 10067-10068 ◽

Cited By ~ 2

Author(s):

Jingyun Xu ◽

Yi Cai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Text Classification ◽

Classification Methods ◽

Short Text ◽

Proposed Model ◽

High Level ◽

Context Features

Some text classification methods don’t work well on short texts due to the data sparsity. What’s more, they don’t fully exploit context-relevant knowledge. In order to tackle these problems, we propose a neural network to incorporate context-relevant knowledge into a convolutional neural network for short text classification. Our model consists of two modules. The first module utilizes two layers to extract concept and context features respectively and then employs an attention layer to extract those context-relevant concepts. The second module utilizes a convolutional neural network to extract high-level features from the word and the contextrelevant concept features. The experimental results on three datasets show that our proposed model outperforms the stateof-the-art models.

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A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification

Jurnal Informatika Jurnal Pengembangan IT ◽

10.30591/jpit.v3i2.828 ◽

2018 ◽

Vol 3 (2) ◽

pp. 157-160 ◽

Cited By ~ 2

Author(s):

Yohan Muliono ◽

Fidelson Tanzil

Keyword(s):

Support Vector Machine ◽

Text Classification ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Classification Methods

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Subjective Text Mining for Arabic Social Media

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch075 ◽

2020 ◽

pp. 1483-1495

Author(s):

Nourah F. Bin Hathlian ◽

Alaaeldin M. Hafez

Keyword(s):

Social Media ◽

Text Mining ◽

Text Classification ◽

Research Area ◽

Classification Methods ◽

Learning Mechanisms ◽

Subjective Analysis ◽

Related Information ◽

Data Source ◽

Rich Content

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

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