Javascript must be enabled to continue!
Research on the internal influence factors of the text multi-classification problem
View through CrossRef
This paper mainly deals with the classification of text type data. The statistics show that more than 8000 articles have been reached in all kinds of documents retrieved by the optical network. However, there are few papers on the factors that affect the classification of text. The text classification method used is important, but the internal factors sometimes play a great role, and even affect the success or failure of the whole text classification. In order to make up for this deficiency, this paper selects the Rocchio algorithm as the classification method, mainly from the category clustering density, class complexity, category definition, stop words and document’s length five internal factors, we tested their influences on text classification by the experiment. Experiment shows that the clustering density is higher and the complexity of the lower class, class definition is higher, the higher the accuracy of text classification, text classification effect is better, and better effect to text stop words, the length of the text does not directly affect the effect of text classification, but according to the text classification algorithm is more suitable to choose the length of the document.
Title: Research on the internal influence factors of the text multi-classification problem
Description:
This paper mainly deals with the classification of text type data.
The statistics show that more than 8000 articles have been reached in all kinds of documents retrieved by the optical network.
However, there are few papers on the factors that affect the classification of text.
The text classification method used is important, but the internal factors sometimes play a great role, and even affect the success or failure of the whole text classification.
In order to make up for this deficiency, this paper selects the Rocchio algorithm as the classification method, mainly from the category clustering density, class complexity, category definition, stop words and document’s length five internal factors, we tested their influences on text classification by the experiment.
Experiment shows that the clustering density is higher and the complexity of the lower class, class definition is higher, the higher the accuracy of text classification, text classification effect is better, and better effect to text stop words, the length of the text does not directly affect the effect of text classification, but according to the text classification algorithm is more suitable to choose the length of the document.
Related Results
E-Press and Oppress
E-Press and Oppress
From elephants to ABBA fans, silicon to hormone, the following discussion uses a new research method to look at printed text, motion pictures and a te...
On Flores Island, do "ape-men" still exist? https://www.sapiens.org/biology/flores-island-ape-men/
On Flores Island, do "ape-men" still exist? https://www.sapiens.org/biology/flores-island-ape-men/
<span style="font-size:11pt"><span style="background:#f9f9f4"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><b><spa...
Afaan Oromo Multi-Label News Text Classification Using Deep Learning Approach
Afaan Oromo Multi-Label News Text Classification Using Deep Learning Approach
Abstract
Classification is a technique for categorizing textual data into a form of predefined categories. Due to its major consequences in regard to critical tasks such as...
Λc Physics at BESIII
Λc Physics at BESIII
In 2014 BESIII collected a data sample of 567 [Formula: see text] at [Formula: see text] = 4.6 GeV, which is just above the [Formula: see text] pair production threshold. By analyz...
An Analysis Method for Interpretability of CNN Text Classification Model
An Analysis Method for Interpretability of CNN Text Classification Model
With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning...
Strong vb-dominating and vb-independent sets of a graph
Strong vb-dominating and vb-independent sets of a graph
Let [Formula: see text] be a graph. A vertex [Formula: see text] strongly (weakly) b-dominates block [Formula: see text] if [Formula: see text] ([Formula: see text]) for every vert...
Multi-label Emotion Classification on Social Media Comments using Deep learning
Multi-label Emotion Classification on Social Media Comments using Deep learning
Abstract
Social media is an online platform that people use to develop social networks or relationships with others. Every day, millions of people use different social medi...
On weak Ikeda–Nakayama rings
On weak Ikeda–Nakayama rings
A ring [Formula: see text] is called a left Ikeda–Nakayama ring (briefly, left IN-ring) if [Formula: see text] for all left ideals [Formula: see text] and all left ideals [Formula:...

