Pattern-Based Emotion Classification on Social Media

2015 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Tromp ◽  
Mykola Pechenizkiy
Keyword(s):  
Social Media ◽  
Emotion Classification

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2018 ◽  
Vol 13 (3) ◽  
pp. 99-111 ◽  
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

Sentiment Analysis on Social Media for Emotion Classification

2020 ◽  
Author(s):  
Dilesh Tanna ◽  
Manasi Dudhane ◽  
Amrut Sardar ◽  
Kiran Deshpande ◽  
Neha Deshmukh
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Emotion Classification

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2020 ◽  
pp. 1377-1390
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

scholarly journals DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2019 ◽  
Vol 33 ◽  
pp. 6818-6825 ◽  
Author(s):  
Navonil Majumder ◽  
Soujanya Poria ◽  
Devamanyu Hazarika ◽  
Rada Mihalcea ◽  
Alexander Gelbukh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Recurrent Neural Networks ◽  
Opinion Mining ◽  
State Of The Art ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Argumentation Mining ◽  
Consumer Feedback ◽  
The Individual

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

scholarly journals Deep Learning for emotion analysis in Arabic tweets

2021 ◽  
Author(s):  
ENAS ABDEL HAKIM KHALIL ◽  
Enas .M.F. El Houby ◽  
Hoda .k. Mohamed
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Short Term Memory ◽  
Model Performance ◽  
Support Vector ◽  
Emotion Classification ◽  
Network Layers ◽  
Learning Techniques ◽  
Comparable Performance ◽  
Fully Connected

Abstract Expressing our emotions using text and emojis expressions became widespread through social media such as Facebook, Instagram, Twitter, Weibo, and LinkedIn. Nowadays, both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. We proposed a model for multilabel emotion classification, using a bidirectional Long Short-term Memory BiLSTM deep network. It is evaluated on the Arabic tweets' dataset provided by SemEval 2018 for the E-c task. Several preprocessing steps, including ARLSTEM with some modifications, replacing emojis with corresponding text meaning from a manually built lexicon, and feature vector representation using Aravec word embedding is applied. The novelty in our research that it examines the effect of hyperparameter tuning on model performance, and it uses BiLSTM in all of its deep neural network layers. The proposed model achieves a comparable performance with state-of-the-art models using different machine learning and deep learning techniques. The system achieves about 9% enhancement in validation accuracy compared with the last best model in the same task using Support Vector classifier SVC; it outperforms the other deep neural networks (UNCCTeam) based on fully connected layers in micro F1 metric of about 4.4%.

scholarly journals EmoMix+: An Approach of Depression Detection Based on Emotion Lexicon for Mobile Application

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Ran Li ◽  
Yuanfei Zhang ◽  
Lihua Yin ◽  
Zhe Sun ◽  
Zheng Lin ◽  
...  
Keyword(s):  
Social Media ◽  
Mobile Application ◽  
Negative Emotions ◽  
Experimental Results ◽  
Emotion Classification ◽  
Emotion Analysis ◽  
Fine Grained ◽  
Depression Detection ◽  
Emotion Lexicon

Emotion lexicon is an important auxiliary resource for text emotion analysis. Previous works mainly focused on positive and negative classification and less on fine-grained emotion classification. Researchers use lexicon-based methods to find that patients with depression express more negative emotions on social media. Emotional characteristics are an effective feature in detecting depression, but the traditional emotion lexicon has limitations in detecting depression and ignores many depression words. Therefore, we build an emotion lexicon for depression to further study the differences between healthy users and patients with depression. The experimental results show that the depression lexicon constructed in this paper is effective and has a better effect of classifying users with depression.

scholarly journals Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises

2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Joel Brynielsson ◽  
Fredrik Johansson ◽  
Carl Jonsson ◽  
Anders Westling
Keyword(s):  
Social Media ◽  
Emotion Classification

scholarly journals Impact of the COVID-19 pandemic on the expression of emotions in social media

2020 ◽  
Vol 15 ◽  
pp. 23-35
Author(s):  
Debabrata Ghosh ◽  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Emotion Classification ◽  
Machine Learning Classification ◽  
Expression Of Emotions ◽  
The Mind

In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. Keywords: classification, COVID-19, emotion, emotion analysis, Naïve Bayes, Pandemic, Random Forest, SVM.

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