Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes

2014 ◽  
pp. 140-149 ◽  
Author(s):  
Yuliya Rubtsova
Keyword(s):  
Sentiment Classification ◽  
Term Extraction ◽  
Text Collections ◽  
Automatic Term Extraction

scholarly journals Reducing the Deterioration of Sentiment Analysis Results Due to the Time Impact

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 184 ◽  
Author(s):  
Yuliya Rubtsova
Keyword(s):  
Computational Complexity ◽  
Text Classification ◽  
Feature Space ◽  
Sentiment Classification ◽  
Text Collections ◽  
Word Representation ◽  
F Measure ◽  
Over Time

The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of the F-measure over a year and a half. This paper presents three different approaches to improving text classification by sentiment in continuously-updated text collections in Russian: using a weighing scheme with linear computational complexity, adding lexicons of emotional vocabulary to the feature space and distributed word representation. All methods are compared, and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described. It is shown that suggested approaches could reduce the deterioration of sentiment classification results for collections staggered over time.

scholarly journals Method for Automatic Term Extraction from Scientific Articles Based on Weak Supervision

2021 ◽  
Vol 19 (2) ◽  
pp. 5-16
Author(s):  
E. P. Bruches ◽  
T. V. Batura
Keyword(s):  
Supervised Learning ◽  
Additional Data ◽  
Weakly Supervised Learning ◽  
Weak Supervision ◽  
Term Extraction ◽  
Text Collections ◽  
Automatic Term Extraction ◽  
Scientific Terms ◽  
Weakly Supervised

We propose a method for scientific terms extraction from the texts in Russian based on weakly supervised learning. This approach doesn't require a large amount of hand-labeled data. To implement this method we collected a list of terms in a semi-automatic way and then annotated texts of scientific articles with these terms. These texts we used to train a model. Then we used predictions of this model on another part of the text collection to extend the train set. The second model was trained on both text collections: annotated with a dictionary and by a second model. Obtained results showed that giving additional data, annotated even in an automatic way, improves the quality of scientific terms extraction.

SENTIMENT CLASSIFICATION OF THE LOCAL VISITORS' SOCIAL MEDIA REVIEWS

2018 ◽  
Vol 4 (26) ◽  
pp. 5534-5538
Author(s):  
Semra AKTAŞ POLAT
Keyword(s):  
Social Media ◽  
Sentiment Classification

Term extraction and management based on event templates

Terminology ◽  
2014 ◽  
Vol 20 (2) ◽  
pp. 151-170 ◽  
Author(s):  
Katia Peruzzo
Keyword(s):  
European Union ◽  
Knowledge Base ◽  
Legal System ◽  
Classification Criteria ◽  
The European Union ◽  
Additional Information ◽  
Term Extraction ◽  
Automatic Term Extraction ◽  
Multi Level ◽  
Victims Of Crime

The paper examines the possible usage of event templates derived from Frame-Based Terminology (Faber et al. 2005, 2006, 2007) as an aid to the extraction and management of legal terminology embedded in the multi-level legal system of the European Union. The method proposed here, which combines semi-automatic term extraction and a simplified event template containing six categories, is applied to an English corpus of EU texts focusing on victims of crime and their rights. Such a combination allows for the extraction of category-relevant terminological units and additional information, which can then be used for populating a terminological knowledge base organised on the basis of the same event template, but which also employs additional classification criteria to account for the multidimensionality encountered in the corpus.

Aspect-Level Sentiment Classification of Chinese Patient Comments Based on Pre-trained Sentiment Embedding

2021 ◽  
Author(s):  
Yongxue Shan ◽  
Zhaoqian Zhong ◽  
Chao Che ◽  
Bo Jin ◽  
Xiaopeng Wei
Keyword(s):  
Chinese Patient ◽  
Sentiment Classification
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