Emotion Mining Using Semantic Similarity

2020 ◽  
pp. 1115-1138 ◽  
Author(s):  
Rafiya Jan ◽  
Afaq Alam Khan
Keyword(s):  
Semantic Similarity ◽  
Semantic Relatedness ◽  
Semantic Features ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Textual Data ◽  
Distributional Semantic Models ◽  
Affective Information ◽  
Emotion Mining

Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This approach uses semantic similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.

Emotion Mining Using Semantic Similarity

2018 ◽  
Vol 9 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Rafiya Jan ◽  
Afaq Alam Khan
Keyword(s):  
Semantic Similarity ◽  
Semantic Relatedness ◽  
Semantic Features ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Textual Data ◽  
Different Types ◽  
Distributional Semantic Models ◽  
Affective Information

Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This approach uses semantic similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

Structure and Semantics of Tactile Verbs in a Comparative Aspect (On the Material of the English, German and Russian Languages)

2021 ◽  
pp. 58-63
Author(s):  
Yu. A. Sakhno
Keyword(s):  
Comparative Study ◽  
Semantic Features ◽  
Semantic Classification ◽  
Comparative Aspect ◽  
Similarities And Differences ◽  
The Comparative Study ◽  
Linguistic Units

This article deals with the study of the structural and semantic features of tactile verbs (hereinafter TVs) in English, German and Russian. Particular attention is paid to the comparative study of TVs, which allows us to identify structural and semantic similarities and differences of linguistic units studied. The structural and semantic classification of TVs in the compared languages is also provided.

A Hybrid Model for Emotion Detection from Text

2017 ◽  
Vol 7 (1) ◽  
pp. 32-48 ◽  
Author(s):  
Samar Fathy ◽  
Nahla El-Haggar ◽  
Mohamed H. Haggag
Keyword(s):  
Semantic Similarity ◽  
Hybrid Model ◽  
Semantic Analysis ◽  
Main Idea ◽  
Object Extraction ◽  
Emotion Detection ◽  
Suggested Approach ◽  
Matching Process ◽  
Extraction Algorithm ◽  
Input Sentence

Emotions can be judged by a combination of cues such as speech facial expressions and actions. Emotions are also articulated by text. This paper shows a new hybrid model for detecting emotion from text which depends on ontology with keywords semantic similarity. The text labelled with one of the six basic Ekman emotion categories. The main idea is to extract ontology from input sentences and match it with the ontology base which created from simple ontologies and the emotion of each ontology. The ontology extracted from the input sentence by using a triplet (subject, predicate, and object) extraction algorithm, then the ontology matching process is applied with the ontology base. After that the emotion of the input sentence is the emotion of the ontology which it matches with the highest score of matching. If the extracted ontology doesn't match with any ontology from the ontology base, then the keyword semantic similarity approach used. The suggested approach depends on the meaning of each sentence, the syntax and semantic analysis of the context.

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals A THEMATIC CLASSIFICATION OF RELIGIOUS-CHRISTIAN VOCABULARY OF RUSSIAN-UDMURT DICTIONARIES BY G. Ye. VERESHCHAGIN

2021 ◽  
Vol 15 (2) ◽  
pp. 214-221
Author(s):  
Leonid Michaylovich Ivshin
Keyword(s):  
Russian Language ◽  
Semantic Features ◽  
Outstanding Scientist ◽  
Linguistic Activity ◽  
The Russian Language ◽  
National Corpus

The article examines the vocabulary of religious-Christian content in the handwritten Russian-Udmurt dictionaries by the first Udmurt writer and outstanding scientist, educator and missionary, clergyman G. Ye. Vereshchagin. There is no exact information about the time when the manuscripts were written. One of them was presumably created at the end of the 19 - beginning of the 20 centuries, before the adoption of the Russian spelling reform in 1918, since the letter ъ is inconsistently encountered at the very beginning of the dictionary in lexemes ending in a hard consonant. Another manuscript can be dated to the period after the adoption of the Russian spelling reform, when the Cyrillic letters ѣ, ө and ъ were excluded from the Russian alphabet. The author of the manuscripts selected appropriate primordial Udmurt equivalents to words of religious content or used borrowings (mainly from the Russian language), and was guided by the following considerations: 1) he used Udmurt words that arose in the depths of paganism, which by the time the manuscripts were compiled had acquired a completely Christian meaning (Kyldysin tӧre 'Archangel'); 2) adapted concepts that had a slightly different, everyday meaning (viz sonany, gavyldyns, aldans ‘to tempt’); 3) terms without direct correspondences in the Udmurt language are often translated by a combination of words or interpretation (umoytem Inmar vyle veras ‘blasphemer’); 4) borrowed from Russian or other languages (Archirey, Arquerey ‘Bishop’). The study of the lexical and semantic features of written attestations in the context of developing the national corpus of the Udmurt language and filling it with not only absolutely new, but also to some extent forgotten and revisited elements is a very important linguistic activity. The linguistic actualization of religious vocabulary contributes to the recovery of speech assets and registers in a significant number of dictionary nominalizations by designating concepts and phenomena of the spiritual and religious sphere of the Udmurt language.

scholarly journals News Classification Using Machine Learning

2021 ◽  
Vol 9 (5) ◽  
pp. 23-27
Author(s):  
SHWETA MAHAJAN
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Performance Improvement ◽  
Vital Role ◽  
Learning Approach ◽  
Entertainment Education ◽  
Meaningful Information ◽  
Textual Data ◽  
Machine Learning Approach

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

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