Techniques in Emotion Classi ication: An Overview (original) (raw)

On the Use of Emojis to Train Emotion Classifiers

2019

Nowadays, the automatic detection of emotions is employed by many applications in different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media and treat this problem as a classification problem, which requires preparing training data. The typical method for annotating the training data by human experts is considered time consuming, labor intensive and sometimes prone to error. Moreover, such an approach is not easily extensible to new domains/languages since such extensions require annotating new training data. In this study, we propose a distant supervised learning approach where the training sentences are automatically annotated based on the emojis they have. Such training data would be very cheap to produce compared with the manually created training data, thus, much larger training data can be easily obtained. On the other hand, this training data would naturally have lower quality as it may ...

Analysis on emotion classification methods

KDU IRC 2020, 2020

Emotional intelligence is the ability to understand changing states of emotion, it is an important aspect of human interaction. With upcoming developments emotion identification is an important aspect in HCI. Ideally if a computer can identify a human's emotions and respond to it accordingly human computer interactions would be much more natural and more convenient. But even from a human's perspective emotions are hard to identify and track, hence for a computer to identify accurate emotions can be challenging. Nonetheless there exists few methods to classify and label emotions into categories. Hence this research is an analysis of methods used to classify emotions. Discussing the strengths and weaknesses in communication cues such as facial expression classifiers, gesture movements, acoustic emotion classifiers and emotion mining in text. It argues that there exists an increment of accuracy when two or more systems are paired to extract the features in different situations. Hence results show that, while each model has its advantages and disadvantages, when integrated to classify, it gives better, more accurate prediction and improved results. Additionally, this paper mentions some of the practical issues that exist when it comes to emotion recognition and HCI. Furthermore, it is identified that emotion identification via text is a research area which holds great potential and among many approaches hand crafted models with the use of machine learning gives the best results. Finally, it proposes a solution, a mobile application for emotional support using emotion identification via text messages.

Predicting The Emotions Based on Emoji's and Speech Using Machine Learning Techniques

Speech consists of assorted information, like language, emotions, what type of message to be communicated with others etc. Emotions are the part of human life in every situation, sometimes one get angry, sad, happy based on the dialogues and behavior of the opposite person. In this work, we have a tendency to square measure aiming to predict the emotions supported the audio files. At first the dataset encompass audio files. Here the emotions typically represented as happy, sad, surprised, angry etc., and could be divided into 2 varieties like positive emotions and negative emotions. Here emoji's are used to predict the emotion of the person, so that it can be quickly identified, for every feeling there'll be a revered emoji format supported that we have a tendency to square measure able to get emoji's for the required emotions given within the datasets. Before applying ways or models on the dataset, feature extraction plays a big role during this speech feeling prediction. Afterward we have a tendency to square measure applying Machine Learning Techniques such as Decision Tree, MLP classifier, neural networks and Augmenting the information using noise injection with Laplace and logistic distribution and pitch shifting and trimming the data so as to induce sensible performance.

Harnessing Emotive Features for Emotion Recognition from Text

International Journal of Advanced Computer Science and Applications, 2021

With the prevalence of affective computing, emotion recognition becomes vital in any work related to natural language understanding. The inspiration for this work is provided by supplying machines with complete emotional intelligence and integrating them into routine life to satisfy complex human desires and needs. The text being a common communication medium on social media even now, it is important to analyze the emotions expressed in the text which is challenging due to the absence of audiovisual cues. Additionally, the conversational text conveys many emotions through communication contexts. Emoticon serves the purpose of selfannotation of writer's emotion in text. Therefore, a machine learning-based text emotion recognition model using emotive features proposed and evaluated it on the SemEval-2019 dataset. The proposed work involves exploitation of different emotionbased features with classical machine learning classifiers like SVM, Multilayer perceptron, REPTree, and decision tree classifiers. The proposed system performs competitively well in terms of f-score 65.31% and accuracy 87.55%.

EMOTION RECOGNITION: A LITERATURE SURVEY

As one of the most prosperous applications of text analysis and understanding emotions(sentiments) and short messaging text apperception has recently received consequential attention especially during the past several years. This is corroborated by the emergence of Web 2.0, social networking services, micro blogging, blogs, chats, online reviews, forums, discussions and systematic evaluations of text analysis(emotion analysis) techniques. There are five major aspects for this trend: first is the wide range of commercial and social marketing applications, second is the understanding of one‘s feelings (sentiments), third human-computer interaction , fourth text to speech generation and fifth the availability of natural language and machine learning approaches and technologies after 20 years of research. This paper provides an up-to-date critical survey of emotions, emoticons and short messaging text research.

Emotion models for textual emotion classification

Journal of physics, 2016

This paper deals with textual emotion classification which gained attention in recent years. Emotion classification is used in user experience, product evaluation, national security, and tutoring applications. It attempts to detect the emotional content in the input text and based on different approaches establish what kind of emotional content is present, if any. Textual emotion classification is the most difficult to handle, since it relies mainly on linguistic resources and it introduces many challenges to assignment of text to emotion represented by a proper model. A crucial part of each emotion detector is emotion model. Focus of this paper is to introduce emotion models used for classification. Categorical and dimensional models of emotion are explained and some more advanced approaches are mentioned.

Emojis Pictogram Classification for Semantic Recognition of Emotional Context

2021

In online interactions, users frequently add emojis (e.g., smileys, hearts, angry faces) to text for expressing the emotions behind the communication context, aiming at a better interpretation to text especially of polysemous short expressions. Emotion recognition refers to the automated process of identifying and classifying human emotions. If textbased emoticons (i.e., emojis created by textual symbols and characters) can be directly understood by semantic-based context recognition tools used in the Web and Artificial Intelligence and robotics, image-based emojis need instead image recognition for a complete semantic context interpretation. This study aims to explore and compare systematically different classification models of emoticon pictograms collected from the Internet, with different labels according to the Ekman model of six basic emotions. A first comparison involves supervised machine learning classifiers trained on features extracted through neural networks. In the second phase, the comparison is extended to different deep learning models. Results indicate that deep learning models performed excellent, and traditional supervised algorithms also achieve very promising outcomes.

Artificial Intelligence-Based Emotion And Mood Analysis On Social Media Emojis

2022

The aim of this project was to show the importance of algorithms that can be used to analyze people's emotions and to show the potential of algorithms that can learn in such branches with different experiments. When we look at the first writings of humanity, it was common to describe an idea with visuals, and it was an interesting and easy method in terms of sentiment analysis to add such a basic thing to programs through emojis. In order to support this work, I researched visual-based languages, especially Sumerian. While talking about the potential of deep learning, I used the artificial intelligence-created drawings of the character named Krystal of the Nintendo company, which is fictional but very special for me, which I used for the GPT-3 algorithm experience that I previously made, to prove this thesis. The reason I chose this character was that I wanted to test the algorithms at an extreme level because it had artistically original features and had a different combination of both humanoid and non-humanoid features. It was also effective that I wanted to kind of thank him for making me interested in the game industry. Then, I performed an experiment where I transferred the face made based on the mummy of Pharaoh Rameses II, one of the ancient Egyptian dynasties, to another algorithm, which allowed his facial movements to be animated thousands of years later.I have used the experiments I have linked in other places, as well as in private lessons where I talked about the future of the gaming industry...

EMOTEX: Detecting Emotions in Twitter Messages

Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or public mood of a community. In this paper, we propose a new approach for automatically classifying text messages of individuals to infer their emotional states. To model emotional states, we utilize the well-established Circumplex model that characterizes affective experience along two dimensions: valence and arousal. We select Twitter messages as input data set, as they provide a very large, diverse and freely available ensemble of emotions. Using hash-tags as labels, our methodology trains supervised classifiers to detect multiple classes of emotion on potentially huge data sets with no manual effort. We investigate the utility of several features for emotion detection, including unigrams, emoticons, negations and punctuations. To tackle the problem of sparse and high dimensional feature vectors of messages, we utilize a lexicon of emotions. We have compared the accuracy of several machine learning algorithms, including SVM, KNN, Decision Tree, and Naive Bayes for classifying Twitter messages. Our technique has an accuracy of over 90%, while demonstrating robustness across learning algorithms.