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‐labeled 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.
The article surveys the concept of emotion detection from texts and highlights the major contributions, approaches, datasets, and weaknesses of recent text‐based emotion detection schemes. Also, emotion‐labeled data sources are presented to provide neophytes with text databases that are eligible for emotion detection. The paper further explores possible opportunities for improving the detection of emotions from texts.
Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a ...comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, ...including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training (GPT) and its variants, Transformer-XL, Cross-lingual Language Models (XLM), and the Bidirectional Encoder Representations from Transformers (BERT). Considering BERT’s strength and popularity in text-based emotion detection, the paper discusses recent works in which researchers proposed various BERT-based models. The survey presents its contributions, results, limitations, and datasets used. We have also provided future research directions to encourage research in text-based emotion detection using these models.
•We detect and analyze sentiment and emotion expressed by people from text in their twitter posts.•We collected tweets and replies on few specific topics and created a dataset with text, user, ...emotion, sentiment information, etc.•We used the dataset to detect sentiment and emotion from tweets and their replies and measured the influence scores of users based on various user-based and tweet-based parameters.•We used the detected sentiment and emotions to generate generalized and personalized recommendations for users based on their twitter activity.
Online social networks have emerged as new platform that provide an arena for people to share their views and perspectives on different issues and subjects with their friends, family, relatives, etc. We can share our thoughts, mental state, moments, stand on specific social, national, international issues through text, photos, audio and video messages and posts. Indeed, despite the availability of other forms of communication, text is still one of the most common ways of communication in a social network. The target of the work described in this paper is to detect and analyze sentiment and emotion expressed by people from text in their twitter posts and use them for generating recommendations. We collected tweets and replies on few specific topics and created a dataset with text, user, emotion, sentiment information, etc. We used the dataset to detect sentiment and emotion from tweets and their replies and measured the influence scores of users based on various user-based and tweet-based parameters. Finally, we used the latter information to generate generalized and personalized recommendations for users based on their twitter activity. The method we used in this paper includes some interesting novelties such as, (i) including replies to tweets in the dataset and measurements, (ii) introducing agreement score, sentiment score and emotion score of replies in influence score calculation, (iii) generating general and personalized recommendation containing list of users who agreed on the same topic and expressed similar emotions and sentiments towards that particular topic.
Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, ...but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.
Human emotions can be expressed in many ways through facial expressions, speech, actions, and in textual form. Emotions play an important role in determining the human’s state and influence their ...lives in decision-making, behaviour and interaction. With the evolution of many advances in technology, researchers are able to detect the emotion of humans through facial expressions, speech, and text. Much advanced and efficient research work has been done in the field of emotion recognition through facial expression and audio, but still, there are not great works on emotion detection from text. Detecting emotions from text has wide applications in stock markets, business, decision-making, analyzing the view of people on any topic through social media conversations, tweets, blogs, and articles. Our work is focused on the detection of emotions from the text. We propose different approaches for detecting emotions through text. We implemented different approaches like the lexicon-based approach, the supervised machine learning approach with the Naïve-bayes algorithm, and the unsupervised machine learning approach with semantic similarities. After the consideration of the cons of all the approaches and the accuracies derived from our work, it is evident that the unsupervised machine learning approach with semantic similarities is the best approach with an accuracy of 78.5%.
Emotion detection assumes a pivotal role in the evaluation of adverse psychological attributes, such as stress, anxiety, and depression. This study undertakes an exploration into the prospective ...capacities of machine learning to prognosticate individual emotional states, with an innovative integration of electroencephalogram (EEG) signals as a novel informational foundation. By conducting a comprehensive comparative analysis of an array of machine learning methodologies upon the Kaggle Emotion Detection dataset, the research meticulously fine-tunes classifier parameters across various models, including, but not limited, to random forest, decision trees, logistic regression, support vector machines, nearest centroid, and naive Bayes classifiers. Post hyperparameter optimization, the logistic regression algorithm attains a peak accuracy rate of 97%, a proximate performance mirrored by the random forest model. Through an extensive regimen of EEG-based experimentation, the study underscores the profound potential of machine learning paradigms to significantly elevate the precision of emotion detection, thereby catalyzing advancements within the discipline. An ancillary implication resides in early discernment capabilities, rendering this investigation pertinent within the domain of mental health assessments.
•We apply fuzzy rough nearest neighbour methods to classify text based on emotions.•Feature engineering and ensembles can gain accuracy similar to deep learning methods.•Our approach provides a more ...interpretable alternative than black box methods.
Due to the ever-expanding volumes of information available on social media, the need for reliable and efficient automated text understanding mechanisms becomes evident. Unfortunately, most current approaches rely on black-box solutions rooted in deep learning technologies. In order to provide a more transparent and interpretable framework for extracting intrinsic text characteristics like emotions, hate speech and irony, we propose to integrate fuzzy rough set techniques and text embeddings. We apply our methods to different classification problems originating from Semantic Evaluation (SemEval) competitions, and demonstrate that their accuracy is on par with leading deep learning solutions.
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