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In 1980 Robert Plutchik constructed a wheel-like diagram of emotions visualizing eight basic emotions, plus eight derivative emotions each composed of two basic one.
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In today’s world, social media is considered as a necessary platform for the source of expressions, thoughts, point-of-views, and communication between people, that belongs to various walk of life. There are nearly 2.62 billion active users of different social networks, which is expected to be increased up to 3 billion users by 2021. The social net...
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... 1980 Robert Plutchik constructed a wheel-like diagram for emotions visualizing based on eight basic emotions. These eight emotion can be seen in Figure 4. ...
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Citations
... Kamal et al. [10] proposed a framework to analyze Twitter data for crowdsource sensing and decision-making with a lexicon-based emotion analysis approach. This framework can obtain real-time Twitter data and classify the data into eight different emotion categories, including anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. ...
Wide adoption of social media has caused an explosion of information stored online, with the majority of that information containing subjective, opinionated, and emotional content produced daily by users. The field of emotion analysis has helped effectively process such human emotional expressions expressed in daily social media posts. Unfortunately, one of the greatest limitations of popular word-based emotion analysis systems has been the limited emotion vocabulary. This paper presents an attempt to extensively expand one such word-based emotion analysis system by integrating multiple emotion dictionaries and implementing an automatic extraction mechanism for emotive expressions. We first leverage diverse emotive expression dictionaries to expand the emotion lexicon of the system. To do that, we solve numerous problems with the integration of various dictionaries collected using different standards. We demonstrate the performance improvement of the system with improved accuracy and granularity of emotion classification. Furthermore, our automatic extraction mechanism facilitates the identification of novel emotive expressions in an emotion dataset, thereby enriching the depth and breadth of emotion analysis capabilities. In particular, the automatic extraction method shows promising results for applicability in further expansion of the dictionary base in the future, thus advancing the field of emotion analysis and offering new avenues for research in sentiment analysis, affective computing, and human–computer interaction.
... Earlier, this kind of analysis required a set of features which are manually designed. Nowadays, the focus has shifted to other approaches such as graphs of dependency relationships [152], and lexicon-based supervised learning approach [55]. It is observed that identifying entities, their components, and knowing their polarity is not sufficient to know the sentiment of a sentence. ...
With the evolution of several online platforms for information sharing such as social media, blogs, product review sites, and discussion forums, people have become more proactive in sharing their expectations, views, feelings, and experiences. This large amount of emotionally wrapped data motivates many researchers to perform data mining and present the crux of hidden emotions or mental states in a more presentable and comprehensible manner. It has several applications in different domains such as business, education, psychology, politics, and many more. This paper presents a detailed literature review projected to rigorously analyze the existing approaches to identify the mental or emotional state of a person from unstructured textual data. We include the most relevant papers which were published during 2001-2022. The selected papers are classified into three categories: granularity level, contextual level, and cognition level. Each category is carefully analyzed followed by a detailed and critical discussion. Finally, open challenges, opportunities, applications, and future directives are presented in-depth to facilitate the researchers working in the domain of emotion mining.
... To address the above limitations, few researchers are trying to use the lexicon-based approach for emotion prediction [14]. Here, the lexicon-based approach does not require any training data and these are computationally not expensive in comparison with ML and DL. ...
span lang="EN-US">Emotion analysis is a process of identifying the human emotions derived from the various data sources. Emotions can be expressed either in monolingual text or code-switch text. Emotion prediction can be performed through machine learning (ML), or deep learning (DL), or lexicon-based approach. ML and DL approaches are computationally expensive and require training data. Whereas, the lexicon-based approach does not require any training data and it takes very less time to predict the emotions in comparison with ML and DL. In this paper, we proposed a lexicon-based method called non-binding lower extremity exoskeleton (NBLex) to predict the emotions associated with Kannada-English code-switch text that no one has addressed till now. We applied the One-vs-Rest approach to generate the scores for lexicon and also to predict the emotions from the code-switch text. The accuracy of the proposed model NBLex (87.9%) is better than naïve bayes (NB) (85.8%) and bidirectional long short-term memory neural network (BiLSTM) (84.7%) and for true positive rate (TPR), the NBLex (50.6%) is better than NB (37.0%) and BiLSTM (42.2%). From our approach, it is observed that a simple additive model (lexicon approach) can also be an alternative model to predict the emotions in code-switch text.</span
... Dokumen atau teks yang akan digunakan dalam text mining pada umumnya memiliki dimensi yang tinggi, terdapat noise, dan struktur teks yang tidak baik terutama tweet. Hal ini disebabkan karena tweet ditulis oleh publik sehingga ada kemungkinan bahwa sebagian besar kata-kata ditulis dalam bentuk kasual, singkatan, bentuk pendek, karakter khusus, dan kesalahan ejaan (Kamal et al., 2019). Dengan demikian, noise dalam data akan bertambah yang akan menyebabkan hasil analisis menjadi buruk. ...
Coronavirus Disease 2019 (COVID-19) merupakan penyakit menular yang disebabkan oleh Severe Acute Respiratory Illness Coronavirus. Penyebaran virus ini tergolong sangat cepat dan sulit dikendalikan sehingga pada Maret 2020, organisasi kesehatan dunia mendeklarasikan COVID-19 sebagai pandemi. Pandemi COVID-19 memberi dampak negatif hampir di segala aspek kehidupan. Mulai dari sosial, ekonomi, hingga psikologis publik. Selama pandemi, aktivitas di luar rumah terbatas. Korban PHK bertambah. Hal-hal tersebut diduga melatarbelakangi publik mengalami gangguan psikis. Oleh karenanya, perlu pemantauan keadaan psikologis agar kesehatan mental publik dapat tetap terjaga. Tujuan penelitian ini adalah mengkaji sentimen dan psikis publik yang dilihat melalui keadaan emosi publik selama pandemi COVID-19 periode Maret-Juli 2020. Data penelitian adalah data Twitter yang dianalisis menggunakan analisis sentimen dan emosi dengan pendekatan berbasis leksikon. Hasil penelitian menunjukan sentimen negatif lebih banyak diekspresikan dan ketakutan adalah emosi yang paling dirasakan publik. Hasil tersebut dapat dijadikan masukan khususnya bagi pemerintah agar lebih memerhatikan keadaan emosi publik.
... The research was conducted on 18,659 advertisement tweets by three major cell phone companies selling their phones in India, the results of which revealed that Xiaomi appears, in every aspect, to be the winner of all the three brand names -Xiaomi, Honor, and OnePlus, irrespective of the scope of SM. In the same year, a study by Fernández-Gavilanes, Juncal-Martínez, García-Méndez, Costa-Montenegro, & González-Castaño proposed the combination of the position prediction approach with an unmonitored SA strategy to assess, automatically, the presence in various countries of social network users during an event of global impact [15]. A study was done to examine users' or potential consumer preferences to evaluate users' emotional responses to different types of posts (images or video) distributed through SM channels. ...
... Low precision and inaccuracy due to the noise in data (Kamal et al., 2019) Complex and technical approaches to opinion mining in different business sectors for decision-making and planning help analyze and visualize data. (Kim & Jeong, 2015) individuals and Indian brands (WebguruIndia, 2019). ...
Sentiment Analysis is the process by which opinions are defined and categorized as positive, neutral, or negative in a given piece of text. It is a vital part of every strategy for tracking social media. It helps to understand what someone is thinking behind a social media post. Knowing the emotion will provide valuable background for the businesses to move further and react. Over the last decade, numerous studies have been carried out on how businesses can use Sentiment Analysis to understand and quantify the feelings of their consumers about their brand, products, and services. An in-depth analysis of the existing empirical literature on SA regarding social media campaigns is provided using a systematic review of the literature. This paper presents the findings according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA). The aim is to create a resource of the methods and techniques used in the studies to assist both researchers and academia. We subsequently identified the corpus trends, challenges, and implications. We suggested avenues for further research in the application of SA in social media campaign design and analysis.
... Kamal et al. [18] identified the emotions of the data collected from Twitter, Rotten Tomatoes, IMDB. They classify emotions based on the crowdsource sensing framework of the lexicon approach and represent the words in the form of clouds classified as positive and negative. ...
With the extensive usage of Internet technology, Sentiment Analysis (SA) has become one of the research areas
to analyze the sentiments. SA extracts emotions in the text, which helps various organizations to make effective
decisions. The explosive volumes of data create more opportunities and challenges in the analysis. The existing
survey focuses on Natural Language Processing techniques for SA. However, in the last few years, many
Machine Learning techniques have been developed. In this survey paper, a comprehensive review of the
Machine Learning techniques for SA is presented. This survey mainly focuses on the essential works carried out
in the last few years. SA is an evolving field, and the other contribution of this paper is presenting newer
applications and open problems in sentimental analysis.
... Kamal et al. [18] identified the emotions of the data collected from Twitter, Rotten Tomatoes, IMDB. They classify emotions based on the crowdsource sensing framework of the lexicon approach and represent the words in the form of clouds classified as positive and negative. ...
With the extensive usage of Internet technology, Sentiment Analysis (SA) has become one of the research areas to analyze the sentiments. SA extracts emotions in the text, which helps various organizations to make effective decisions. The explosive volumes of data create more opportunities and challenges in the analysis. The existing survey focuses on Natural Language Processing techniques for SA. However, in the last few years, many Machine Learning techniques have been developed. In this survey paper, a comprehensive review of the Machine Learning techniques for SA is presented. This survey mainly focuses on the essential works carried out in the last few years. SA is an evolving field, and the other contribution of this paper is presenting newer applications and open problems in sentimental analysis.
... One is for training the sentiments, other for evaluating regional values of India. Using data extracted from twitter, A crowd source sensory methodology was introduced by Rashid Kamal et al. [4]. He developed a word model that can identify emotions which eventually was found to be more effective than location-based research. ...
Abstract—The ongoing pandemic has caused several impacts
on human life. Social media has become more popular during
the pandemic, wherein people share all their thoughts and
emotions. Due to the social distancing norms and other preventive
measures, people are connecting with each other through this
platform. This gave us an occasion to study the overall mental
reaction of the public to this disease. To measure the region-wise
sentiment value, we used the tweets associated with COVID-19 in
this paper. This is then used for the model’s training. Then, we
generate the average emotion conveyed by each region. This can
be beneficial in validating the psychological impact that COVID-
19 had on the people, helping governments to take effective
actions. The output as well as the COVID-19 guidelines and
data are then represented in a website.
... As feeling or emotions impact everything in daily life, investigators handle this as a challenge to recognize and experiment emotion. Implicit Feedback generated using Artificial Intelligent (AI) System from individuals helps in adjusting the customer's experience (e.g., communication with virtual operators) (Rahid, Ali Sha, Carsten, Mohsin, & Wahid, 2019;Sebe, Cohen, & Huang, 2005). Detection of intimidation on social media (Dinakar, Jones, Havasi, Lieberman, & Picard, 2012). ...
The study examines the effects of psychosocial intervention on emotional disorders and social issues among people infected with HIV/AIDS. Ten consented people infected with HIV, attending the antiretroviral therapy center of Jharkhand (India), and having emotional disorders and social problems, were selected conveniently. Only six participants completed all sessions for the psychosocial intervention spanning 6 months. Mental health and social issues are widespread among people infected with HIV, and this psychosocial problem hampers their treatment. Case 1 was suffering from hypochondriasis, and cognitive behavior therapy was implemented. Case 2 was having an anxiety disorder, and systematic desensitization with cognitive behavior therapy was administered on him. Cases 3 and 4 had marital conflicts, and marital therapy was conducted on the couple. Case 5 had moderate depression, and cognitive behavior therapy was applied. Case 6 had alcohol dependency, and motivational enhancement therapy was administered. Standard scales were used to monitor the progress of the reported symptoms. The baseline, midline, and endline scale scores showed recovery from psychosocial problems and improvement in CD-4 counts and medication adherence. Conclusively, individually tailored psychosocial intervention, along with anti-retroviral therapy, can improve the conditions of persons who have HIV/AIDS. The selection of six participants in the study was not randomized, controlled, and blinded.
... As feeling or emotions impact everything in daily life, investigators handle this as a challenge to recognize and experiment emotion. Implicit Feedback generated using Artificial Intelligent (AI) System from individuals helps in adjusting the customer's experience (e.g., communication with virtual operators) (Rahid, Ali Sha, Carsten, Mohsin, & Wahid, 2019;Sebe, Cohen, & Huang, 2005). Detection of intimidation on social media (Dinakar, Jones, Havasi, Lieberman, & Picard, 2012). ...
In today’s world, online social media has become one of the top platforms to convey thoughts, expression, views, and transferring of messages among people via microblogging. Recent studies have found that there are around 331 billion of active users, and they are relied upon to exceed 400 billion users by 2021. Social media users utilize this platform between 2 and 3 h a day. Twitter tweets may contain emotions. Most of the researchers using social media especially Twitter users use three different polarities for sentiment analysis, namely positive, negative, and neutral. Such polarities have never detected the emotion for any Twitter data. This chapter focuses on identifying emotional level of Twitter tweets by text classification. The proposed methodology uses Multinomial Naïve Bayes model for classification. It is computed by training and testing the model. The probability of each term in the document is calculated for every class. The class with higher frequency is considered and assigned a value. All these are done by extracting features like unigram and unigram with POS tagging for four-way classification and five-way classification method. Four-way classification using unigram feature produced a better result of 82.25% accuracy compared to five-way classification and existing systems.
