WEFEST: Word Embedding Feature Extension for Short Text Classification

PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approachThe paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.FindingsThis paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/valueUsing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

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A New SVM Method for Short Text Classification Based on Semi-Supervised Learning

2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS) ◽

10.1109/aits.2015.34 ◽

2015 ◽

Cited By ~ 12

Author(s):

Chunyong Yin ◽

Jun Xiang ◽

Hui Zhang ◽

Jin Wang ◽

Zhichao Yin ◽

...

Keyword(s):

Supervised Learning ◽

Text Classification ◽

Short Text

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Incorporate Syntactic Information for Short Text Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.697 ◽

2011 ◽

Vol 268-270 ◽

pp. 697-700

Author(s):

Rui Xue Duan ◽

Xiao Jie Wang ◽

Wen Feng Li

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Environment ◽

Text Classification ◽

The Internet ◽

Selection Methods ◽

Text Documents ◽

Short Text ◽

Syntactic Information ◽

Dependency Relations

As the volume of online short text documents grow tremendously on the Internet, it is much more urgent to solve the task of organizing the short texts well. However, the traditional feature selection methods cannot suitable for the short text. In this paper, we proposed a method to incorporate syntactic information for the short text. It emphasizes the feature which has more dependency relations with other words. The classifier SVM and machine learning environment Weka are involved in our experiments. The experiment results show that incorporate syntactic information in the short text, we can get more powerful features than traditional feature selection methods, such as DF, CHI. The precision of short text classification improved from 86.2% to 90.8%.

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Improving Term Weighting Schemes for Short Text Classification in Vector Space Model

IEEE Access ◽

10.1109/access.2019.2953918 ◽

2019 ◽

Vol 7 ◽

pp. 166578-166592

Author(s):

Surender Singh Samant ◽

N. L. Bhanu Murthy ◽

Aruna Malapati

Keyword(s):

Vector Space ◽

Text Classification ◽

Vector Space Model ◽

Term Weighting ◽

Weighting Schemes ◽

Short Text ◽

Space Model

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