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.
Publish Exploring Algorithmic Approaches for Emotion Classification in Text
The classification of emotions in text is a rapidly growing field with significant applications, including sentiment analysis and social media monitoring. However, achieving high accuracy in emotion classification remains a challenge. This research aims to explore algorithmic approaches for emotion classification using the ISEAR dataset, which includes various emotions such as joy, fear, anger, sadness, disgust, shame, and guilt. Researchers employ machine learning techniques such as Random Forest, Logistic Regression, Naive Bayes, and Support Vector Machines (SVM), and use data augmentation techniques to improve model performance. Augmentation methods, including synonym replacement and random embedding, are used to increase the diversity of the training data. The dataset is divided into training and testing sets with a ratio of 80:20. The results show that Random Forest excels with the highest score on all performance metrics: Accuracy, Precision, Recall, and F1-Score of 92% each. SVM also shows good performance, especially in Recall, with a score of 79%. Logistic Regression has stable performance across all metrics with scores of 76%. The Naive Bayes model demonstrates the poorest performance, achieving an accuracy rate of 69%, precision rate of 75%, recall rate of 69%, and F1-Score of 69%. In conclusion, data augmentation techniques significantly enhance model performance, with the Random Forest model demonstrating the highest efficiency in emotion classification in this study.
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%.
Sentence-Level Emotion Detection Framework Using Rule-Based Classification
Cognitive Computation, 2017
Emotion detection and analysis aims at developing applications that can detect and analyse emotions expressed by the users in a given text. Such applications have received considerable attention from experts in computer science, psychology, communications and health care. Emotion-based sentiment analysis can be performed using supervised and unsupervised techniques. The existing studies using supervised and unsupervised emotion-based sentiment analysis are based on Ekman's basic emotion model; have limited coverage of emotion-words, polarity shifters and negations; and lack emoticons and slang. The problems associated with existing approaches can be overcome by the development of an effective, sentence-level emotion-detection sentiment analysis system under a rule-based classification scheme with extended lexicon support and an enhanced model of emotion signals: emotion words, polarity shifters, negations, emoticons and slang. In this work, we propose a rule-based framework for emotion-based sentiment classification at the sentence level obtained from user reviews. The main contribution of this work is to integrate cognitive-based emotion theory (e.g. Ekman's model) with sentiment analysis-based computational techniques (e.g. detection of emotion words, emoticons and slang) to detect and classify emotions from natural language text. The main focus is to improve the performance of state-of-the-art methods by including additional emotionrelated signals, such as emotion words, emoticons, slang, polarity shifters and negations, to efficiently detect and classify emotions in user reviews. The improved results in terms of accuracy, precision, recall and F-measure demonstrate the superiority of the proposed method's classification results compared with baseline methods. The framework is generalized and capable of classifying emotions in any domain.
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.
Latest trends in emotion recognition methods: case study on emotiw challenge
International Journal of Advanced Computer Research
Emotion recognition is becoming increasingly very active field in research. In recent past, this research field has emerged as a milestone in software engineering, website customization, education, and gaming. Moreover, Emotion recognition models are used by more and more intelligent system to improve the multimodal interaction. Therefore, this paper demonstrates the recent literature on the emotion recognition methods presented at Emotion Recognition in the Wild (EmotiW) challenge. EmotiW is a grand challenge organized every year in ACM international conference on multimodal interaction. There has been number of methods presented every year at EmotiW for emotion analysis which are incorporated in this paper on the basis of emotion categorization in different areas. This work depicts a broad methodical analysis of EmotiW challenge for sentiments analysis which can help researchers, IT professionals and academia to find worthy technique for emotion grouping in several areas. It would also provide aid to select the most suitable technique for emotion recognition on the basis of their applications.
Classifying Emotion in News Sentences: When Machine Classification Meets Human Classification
International Journal on Computer …, 2010
Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a method for classifying news sentences into multiple emotion categories. The corpus consists of 1000 news sentences and the emotion tag considered was anger, disgust, fear, happiness, sadness and surprise. We performed different experiments to compare the machine classification with human classification of emotion. In both the cases, it has been observed that combining anger and disgust class results in better classification and removing surprise, which is a highly ambiguous class in human classification, improves the performance. Words present in the sentences and the polarity of the subject, object and verb were used as features. The classifier performs better with the word and polarity feature combination compared to feature set consisting only of words. The best performance has been achieved with the corpus where anger and disgust classes are combined and surprise class is removed. In this experiment, the average precision was computed to be 79.5% and the average class wise micro F1 is found to be 59.52%.
Comparing the Utility of Different Classification Schemes for Emotive Language Analysis
Journal of Classification, 2019
In this paper we investigated the utility of different classification schemes for emotive language analysis with the aim of providing experimental justification for the choice of scheme for classifying emotions in free text. We compared six schemes: (1) Ekman's six basic emotions, (2) Plutchik's wheel of emotion, (3) Watson and Tellegen's Circumplex theory of affect, (4) the Emotion Annotation Representation Language (EARL), (5) WordNet-Affect, and (6) free text. To measure their utility, we investigated their ease of use by human annotators as well as the performance of supervised machine learning. We assembled a corpus of 500 emotionally charged text documents. The corpus was annotated manually using an online crowdsourcing platform with five independent annotators per document. Assuming that classification schemes with a better balance between completeness and complexity are easier to interpret and use, we expect such schemes to be associated with higher inter-annotator agreement. We used Krippendorff's alpha coefficient to measure inter-annotator agreement according to which the six classification schemes were ranked as follows: (1) six basic emotions (α = 0.483), (2) wheel of emotion (α = 0.410), (3) Circumplex (α = 0.312), EARL (α = 0.286), (5) free text (α = 0.205), and (6) WordNet-Affect (α = 0.202). However, correspondence analysis of annotations across the schemes highlighted that basic emotions are oversimplified representations of complex phenomena and as such likely to lead to invalid interpretations, which are not necessarily reflected by high inter-annotator
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets
arXiv (Cornell University), 2023
Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experimental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled.
Text Emotion Analysis Using Classification Techniques: A Review
In the context of analytics in the field of information technology a rapidly growing research area is that of Social Media Analytics. Social media data are vast, noisy and unstructured and dynamic in nature and thus new challenges arise. Twitter, facebook and blogs place one of the important sources of social media across which information is shared. Today all the things of social media is based upon emotions. There are many type of emotion like facial emotion and gestures, by written text, and by speech etc. There are many machine learning algorithms that exist for emotion detection like SVM, Neural, and Naïve Bayes. The aim of this paper is to study about these techniques and give a comparison of the results on basis of recognition of expressions of the six basic emotions. In order to get accurate results data that should be used for classification purposes should contain many emotional gestures.
Recently, given the rise of types of social media networks, the analysis of sentiment and opinions in textual data has gained significant importance. However, sentiment analysis in informal Arabic text presents challenges due to morphological complexities and dialectal variances. This research aims to develop an Emoji Sentiment Lexicon (Emo-SL) tailored to Arabic-language tweets and demonstrate performance improvements by combining emoji-based features with machine learning (ML) for sentiment classification. We constructed the Emo-SL using a corpus of 58K Arabic tweets containing emojis, calculating sentiment scores for 222 frequently occurring emojis based on their distribution across positive and negative categories. Emoji weighting is integrated with text-based feature extraction using lexicons to train classifiers on an Arabic tweet dataset. ML models, including Support Vector Machines (SVM), Naive Bayes, Random Forests, and K-Nearest Neighbors (KNN) are evaluated after optimal preprocessing and normalization. The results show that adding Emo-SL derived emoji features to ML classifiers can significantly improve accuracy by 26.7% over just textual features. The emoji-aware integrated approach achieves 89% F1 score, outperforming the rule-based VADER sentiment analyzer. Additionally, analysis of n-gram impacts further confirms the value of fusing emoji and text semantics for Arabic sentiment classification. The Emo-SL lexicon provides an effective framework for extracting nuanced emotional insights from noisy micro-text, which demonstrates the potential of contextualized emoji understanding to advance multilingual sentiment analysis performance. INDEX TERMS Emoji sentiment lexicon for Arabic (Emo-SL), Arabic-language tweets, machine learning (ML), social media analysis, VADER model, data modeling and analysis, X tweets.
Emotion Detection using Social Media Data
IJRASET, 2021
Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models.
Emotion Detection using Natural Language Process
International Journal of Scientific Methods in Intelligence Engineering Networks
The Internet and other types of modern communication technologies have made the process of exchanging information nowadays extraordinarily rapid and fluid. In this day and age, when both mobile phones and computers are prevalent, it is fairly typical for people to use several communication channels, such as mobile phones and computers, in order to communicate with one another. The creation of an Emotion Detection Model that takes into account emotion at the phrase level is one of the tasks that are included in the scope of this project. Concepts from both Natural Language Processing (NLP) and Machine Learning (ML) can be found in content-based classification issues that fall under NLP’s purview. Our methodology makes use of direct emotional keywords that are located throughout the text as a method for determining feelings. These keywords can be located by searching for them. Throughout the process of identification, words and phrases that convey emotional affect were also taken into...