Emotion Detection on Twitter Textual Data

2020 ◽  
pp. 66-76
Author(s):  
Fan Jiang ◽  
Colton Aarts
Keyword(s):  
Emotion Detection ◽  
Textual Data

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.

Sentiment Classification-How to Quantify Public Emotions Using Twitter

2018 ◽  
Vol 10 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Prerna Mahajan ◽  
Anamika Rana
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Research Area ◽  
Sentiment Classification ◽  
Government Policies ◽  
Emotion Detection ◽  
Textual Data ◽  
Good Classification ◽  
Important Research Area ◽  
Sentiment Score

This article describes how with the tremendous popularity in the usage of social media has led to the explosive growth in unstructured data available on various social networking sites. Sentiment analysis of textual data collected from such platforms has become an important research area. In this article, the sentiment classification approach which employs an emotion detection technique is presented. To identify the emotions this paper uses the NRC lexicon based approach for identifying polarity of emotions. A score is computed to quantify emotions obtained from NRC lexicon approach. The method proposed has been tested on twitter datasets of government policies and reforms, more about current NDA government initiatives in India. The polarity components apply and classify the tweets into eight predefined emotions. This article performs both quantitative and sentiment analysis processes with the objective of analyzing the opinion conveyed to each social content, assign a category (+ve, -ve & neutral) or numbered sentiment score. The assigned scores have been classified using six different machine classification algorithms. Good classification results are achieved with the data.

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 Detection of Textual Data: An Interdisciplinary Survey

2021 ◽  
Author(s):  
Samira Zad ◽  
Maryam Heidari ◽  
James H Jr Jones ◽  
Ozlem Uzuner
Keyword(s):  
Emotion Detection ◽  
Textual Data

Patients’ Perceptions of Caring and Uncaring Nursing Encounters in Inpatient Rehabilitation Settings

2019 ◽  
Vol 21 (2) ◽  
Author(s):  
Joan C Cheruiyot ◽  
Petra Brysiewicz
Keyword(s):  
South Africa ◽  
Content Analysis ◽  
Inpatient Rehabilitation ◽  
Structured Interview ◽  
The Self ◽  
Personal Relationships ◽  
Individual Interviews ◽  
Textual Data ◽  
Interview Guide ◽  
Being There

This study explores and describes caring and uncaring nursing encounters from the perspective of the patients admitted to inpatient rehabilitation settings in South Africa. The researchers used an exploratory descriptive design. A semi-structured interview guide was used to collect data through individual interviews with 17 rehabilitation patients. Content analysis allowed for the analysis of textual data. Five categories of nursing encounters emerged from the analysis: noticing and acting, and being there for you emerged as categories of caring nursing encounters, and being ignored, being a burden, and deliberate punishment emerged as categories of uncaring nursing encounters. Caring nursing encounters make patients feel important and that they are not alone in the rehabilitation journey, while uncaring nursing encounters makes the patients feel unimportant and troublesome to the nurses. Caring nursing encounters give nurses an opportunity to notice and acknowledge the existence of vulnerability in the patients and encourage them to be present at that moment, leading to empowerment. Uncaring nursing encounters result in patients feeling devalued and depersonalised, leading to discouragement. It is recommended that nurses strive to develop personal relationships that promote successful nursing encounters. Further, nurses must strive to minimise the patients’ feelings of guilt and suffering, and to make use of tools, for example the self-perceived scale, to measure this. Nurses must also perform role plays on how to handle difficult patients such as confused, demanding and rude patients in the rehabilitation settings.

Sign in / Sign up

Export Citation Format

Share Document