EMOTION RECOGNITION: A LITERATURE SURVEY (original) (raw)
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IAEME PUBLICATION, 2018
Interaction of human with computer is very interesting and most famous area of research these days because the word is getting modern and digitize. This needs the digital systems to imitate the human behaviour correctly. Emotion is a particular part of human behaviour which plays an important role while interacting with computer, the computer interfaces need to detect the emotion of the users in order to build a truly intelligent behaviour. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. With the rapid growth of web application, most of documents are available on web in the form of text. So, detecting affects from text is a vital issue. Hence, attitude detection from text is important in many areas such as decision making, human computer interaction etc. Work done in this field is very less as compare to other fields. Therefore, it broadens our scope in the field of attitude detection. In this paper, we propose a hybrid model that incorporates natural language processing technique, including keyword-based and machine learning-based emotion classification from textual data at sentence level. Using proposed algorithm, one can calculate the affect vector of sentence by affect vector of word. Then based on affect vector categorize the sentence into appropriate affect class.
Text-based emotion detection: Advances, challenges, and opportunities
Engineering Reports, 2020
Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labelled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED.
Cornell University - arXiv, 2022
AI has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-today activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.
Affective analysis of text in tweets
2018
Affective computing is the study and development of devices that can recognize emotions through various modes such as video, audio and text automatically. In this thesis, I focus on the problem of affective computing in short texts, in particular, tweets. With the evolution of social media in the recent years, there has been a rapid growth of interactions that take occur online, which are expressive in terms of emotion. Internet users today have several diverse methods of being expressive through text, such as by using abbreviations, emoticons and hashtags. I use traditional lexical features and word embeddings to extract semantic and lexical information from the input text. I develop models ranging from linear and tree-based models to deep neural networks to perform emotion detection on Tweets. I create an ensemble of these methods to make my final predictions. I evaluate the ensemble on the SemEval 2018 dataset containing intensity and class annotations for emotions in tweets. I finally perform an error analysis of these algorithms and highlight potential areas of improvement.
Emotion detection and sentiment analysis of text
ELSEVIER SSRN SERIES , 2021
The identification of emotions and thoughts in text was an interesting topic of machine learning in natural languages. With some phrases about all details, emotions and feelings show themselves. Many citizens use foreign languages worldwide and many documents are written in English. Some people do not publish the text in (point) form precisely. In comparison to other technicians, we research emotions in monitor ing using text with or without punctuations, so we see how an emotional management device can be designed with certain beneficial approaches. By developing in a particular way, we benefit from tracking and the possibility of identifying the feelings as outcomes more accurately. In this paper we used different methods for identifying the emotions. Naïve bayes classifier, linear SVM, Logistic regression and random forest are used but best accuracy is achieve d by random forest. The challenge that we have solved in this paper is that in this master learning algorithm we recognize feelings or feelings not shared directly on posts, blogs and social networking pages.
From Words to Emoticons: Deep Emotion Recognition in Text and Its Wider Implications
This paper summarizes several lexical methods for more comprehensive affect recognition in text using an example of typed utterances. We introduce a set of algorithms that are capable of recognizing emotions of user's statements in order to achieve more effective and smoother human-machine conversation. Aspects often neglected by existing systems working with Japanese language, e.g. compound sentences, double negation sentences, modifiers as adverbs and emoticons were combined and their higher effectiveness in recognizing affect in more complicated sentences was confirmed through evaluation experiments. The results are introduced together with separate analysis of emoticons' influence on emotional load. We also discuss importance of predicting human emotions not only in the field of human-computer interaction but also its meaning for developing ethical chatbots.
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.
Informatics, 2021
Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opinion is indisputably positive or negative, render such solutions obsolete and emphasize the need for a more in-depth analysis of the available text. Emotion analysis is a solution to this problem, but the multi-dimensional elements of the expressed emotions in text along with the complexity of the features that allow their identification pose a significant challenge. Machine learning solutions fail to achieve a high accuracy, mainly due to the limited availability of annotated training datasets, and the bias introduced to the annotations by the personal interpretations of emotions from individuals. A hybrid rule-based algorithm that allows the acquisition of a dataset that is annotated with regard to the Plutchik’s eight basic emotions...
IRJET- Emotion Detection from Tweets and Emoticons Using Machine Learning
IRJET, 2021
Many micro blogging sites have millions of people sharing their thoughts daily. We propose and investigate the sentiment from a popular real-time micro blogging service, Twitter, where real time reactions are posted by the user and we find their opinions for almost about "everything". Social networking sites like twitter, Facebook, Instagram, Orkut etc. are the great source of communication for internet users. So this becomes an important source for understanding the opinions, views or emotions of people. We extract data, i.e. tweets from Twitter in real time and apply machine learning techniques to convert them into a useful form and then use it for building sentiment classifier. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity i.e. positive, negative. With the increase use of Internet and big explosion of text data, it has been a very significant research subject to extract valuable information from Text Ocean. To realize multi-classification for text sentiment and emoticons sentiments, this paper promotes a RNN language model based on Long Short Term Memory (LSTM). LSTM is far better than the traditional RNN. And as a language model, LSTM is applied to achieve multiclassification for text and emoticon emotional attributes.
A review on sentiment analysis and emotion detection from text
Social Network Analysis and Mining
Social networking platforms have become an essential means for communicating feelings to the entire world due to rapid expansion in the Internet era. Several people use textual content, pictures, audio, and video to express their feelings or viewpoints. Text communication via Web-based networking media, on the other hand, is somewhat overwhelming. Every second, a massive amount of unstructured data is generated on the Internet due to social media platforms. The data must be processed as rapidly as generated to comprehend human psychology, and it can be accomplished using sentiment analysis, which recognizes polarity in texts. It assesses whether the author has a negative, positive, or neutral attitude toward an item, administration, individual, or location. In some applications, sentiment analysis is insufficient and hence requires emotion detection, which determines an individual's emotional/mental state precisely. This review paper provides understanding into levels of sentiment analysis, various emotion models, and the process of sentiment analysis and emotion detection from text. Finally, this paper discusses the challenges faced during sentiment and emotion analysis.