Multi-label text classification via joint learning from label embedding and label correlation

Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.

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Multi-label text classification based on the label correlation mixture model

Intelligent Data Analysis ◽

10.3233/ida-163055 ◽

2017 ◽

Vol 21 (6) ◽

pp. 1371-1392 ◽

Cited By ~ 2

Author(s):

Zhiyang He ◽

Ji Wu ◽

Ping Lv

Keyword(s):

Mixture Model ◽

Text Classification ◽

Label Correlation

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Leveraging Accident Investigation Reports as Leading Indicators of Construction Safety Using Text Classification

Construction Research Congress 2020 ◽

10.1061/9780784482872.053 ◽

2020 ◽

Author(s):

Shraddha Shrestha ◽

Syed Ahnaf Morshed ◽

Nipesh Pradhananga ◽

Xuan Lv

Keyword(s):

Text Classification ◽

Construction Safety ◽

Leading Indicators ◽

Accident Investigation

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A Brief Survey on Text Classification Using Various Machine Learning Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i1.521 ◽

2018 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Padmavathi .S ◽

M. Chidambaram

Keyword(s):

Machine Learning ◽

Text Classification ◽

Fixed Number ◽

Machine Learning Techniques ◽

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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Improved text classification algorithm for spam filtering based on CABSOFV

Future Computer and Information Technology ◽

10.2495/icfcit131301 ◽

2013 ◽

Cited By ~ 1

Author(s):

G. Y. Wei ◽

L. Zou ◽

J. Pan

Keyword(s):

Text Classification ◽

Classification Algorithm ◽

Spam Filtering

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Survey of Feature Selection and Text Classification Methods for Genetic Mutation Classification

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i4.933937 ◽

2019 ◽

Vol 7 (4) ◽

pp. 933-937

Author(s):

Varun Saproo ◽

Rujuta Upadhyay ◽

Manisha Valera

Keyword(s):

Feature Selection ◽

Text Classification ◽

Genetic Mutation ◽

Classification Methods

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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