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Plutchik's wheel of emotions.

Plutchik's wheel of emotions.

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Internet is the day by day increasing global system. In recent years number of efforts were devoted for mining opinions and sentiments automatically from natural language in social media messages, commercial product reviews, news and movie reviews. This task includes understanding explicit and implicit information conveyed by the language deeply. M...

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... In addition, the underlying emotions from the text were also identified. These emotions were adapted from the following emotions studies: Ekman [20], Plutchik [21], Socher et al. [27], Chafale et al. [28]; the methodology used was adaptive fuzzy similarity-based classification for classifying text into sentiment. Some of the emotions studied were anxiety, sadness, anger, satisfaction, excitement, and happiness. ...
... Plutchik (1994) presented the emotion wheel with four opposite pairs; sadness-joy, surprise-anticipation, disgust-trust, and fear-anger. Plutchik's wheel of emotion has been broadly applied in varied online textual content mining practices, including consumer review websites (e.g., Atabay & Cizel, 2020;Chafale & Pimpalkar, 2014), social communities (e.g., Bertola & Patti, 2013), blogs (e.g., Abbasi & Beltiukov, 2019), and others. The present research adopted Plutchik's emotion wheel due to four considerations. ...
... Diversas abordagens lexicais estão disponíveis na literatura (Gonçalves et al., 2013), no entanto, para nossa análise adotamos o NRC Emotion Lexicon -Emolex (Mohammad & Turney, 2013), um método léxico que classifica textos em 8 categorias afetivas. Essas emoções foram definidas no modelo conhecido como Roda das Emoções de Plutchik (Chafale & Pimpalkar, 2014) e foram inspiradas na teoria da psicologia evolutiva das emoções, que descrevem emoções como alegria, tristeza, raiva, medo, confiança, desgosto, antecipação e surpresa. No R foi utilizado o pacote Syuzhet. ...
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... For example, amazement is juxtaposed to vigilance. Further, the intensity of different emotions can be represented through the intensity of different colours (Chafale & Pimpalkar, 2014). For each of the eight emotions, there are three degrees. ...
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... EEG segment is partitioned into its main bands through bandpass filtering with a 10th order Butterworth filter. We include alpha (8-13 Hz), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and gamma (30-45 Hz) rhythms in our analysis, as well as raw signals, since those have been acknowledged as the most emotion-sensitive [178] and have shown the largest multiscale variability. We select 12 left (Fp1, AF3, F7, F3, FC5, FC1, T7, C3, CP5, CP1, P3, P7) and 12 right (Fp2, AF4, F4, F8, FC2, FC6, C4, T8, CP2, CP6, P4, P8) channels that have shown competitive performance, particularly when their asymmetry is examined, and we assess the proposed features on each set separately. ...
... [22] Plutchik's Emotion Wheel.Figure 1.2: [173] The Valence-Arousal Space. ...
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The analysis of human emotions is a widely researched topic in the scientific fields of Psychology and Neuroscience, trying to investigate the nature and elicitation mechanisms of our feelings. From a computational perspective, however, it remains rather underexplored. While Artificial Intelligence has made overwhelming progress in modeling rational intelligence, there are yet no highly reliable systems to analyze affect, as considerable barriers exist in this process: Emotion expression can be highly subjective and its interpretation varies depending on the context, whereas it poses an inter-subject variability. Yet, most Signal Processing and Machine Learning studies concentrate on behavioral processing of emotions, through modalities like speech, text and facial expressions. To address the challenges of Affective Analysis, in this thesis we choose to process brain signals, and specifically the Electroencephalogram (EEG), as a means to derive emotional information. Recorded physiological and neural signals are capable of being more objective and reliable affective indicators, whereas they can also contribute to develop human-aid systems for applications like the treatment or rehabilitation from brain diseases. Importantly, we consider music as the means to induce emotions for the EEG recordings, since music is known to have a deep emotional impact on humans. Our approach can be divided into two main parts: In the first one, we analyze the complex structure of the EEG and examine novel feature extraction schemes that are based on two multifractal algorithms, namely Multiscale Fractal Dimension and Multifractal Detrended Fluctuation Analysis. In this way we attempt to quantify the variability of the observed signals' complexity across multiple timescales. Our proposed EEG features surpass widely used baselines on Emotion Recognition, whereas they show competitive results in challenging subject-independent experiments and recognition of arousal, indicating that it is highly correlated with the EEG's fragmented structure. In the second part, we utilize a two-branch neural network as a bimodal EEG-music framework, which learns common latent representations between the EEG signals and their music stimuli in order to examine their correspondence. Through this model, we perform supervised emotion recognition experiments and retrieval of music rankings to EEG input queries. By applying this system to independent subject data, we also extract interesting patterns regarding the latent similarity of brain and music signals, the temporal variation of the music-induced emotions and the activated brain regions in each case. As a whole, this study deals with core problems regarding the interpretation of complex EEG signals and illustrates multiple ways that music stimulates the brain activity.
... The next stage calculates the frequency of words that often appears in both categories to produce positive or negative and visualize them using word cloud as shown in Figure 2 and The results of the word cloud were analyzed for emotions using Plutchik's Wheel of Emotions model. This model divides emotions into eight categories: joy, trust, fear, surprise, sadness, disgust, anger, and anticipation [29]. Figure 4 shows the dominant emotions in aggregate in March 2020 are anticipation, sadness, and anger. ...
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On 2 March 2020, the Indonesian government, through President Joko ‘Jokowi’ Widodo, announced the first two cases of COVID-19 in Indonesia. This is the first case of COVID-19 officially confirmed in that country. Several cases have continued to increase since then. President Jokowi began issuing policies on the spread of this virus. This is different from other countries, such as Malaysia and Singapore, which responded from the previous month when the Indonesian government still stated that coronavirus does not exist in Indonesia. Our case study is to find a public opinion through social network analysis of Indonesian public policy during the beginning of the Indonesian COVID-19 pandemic in March 2020. This research implements text mining and document-based sentiments on Twitter data that is reprocessed through machine learning techniques using the Naïve Bayes method. We found negative opinions in the period more dominant by 46%, while that was 35% positive sentiment and 20% neutral. This research shows that anticipation, sadness, and anger are very dominant in the emotional analysis.
... However, personal and professional traits of teachers are also known to be effective criteria. Being reflective, showing empathy, respecting students, being a good communicator, his own passion for learning, as well as his instructional delivery makes a teacher effective 33 . ...
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The continuous pursuit of quality education has always been a concern of higher institutions. This can be seen in the way university teachers deliver academic services to the students in terms of professionalism, commitment, knowledge of the subject matter, teaching for independent learning, and management of learning. Students as recipients of these services are significant sources of information about their course interaction that takes place in an educational system. Utilizing Latent Dirichlet Allocation (LDA) algorithm and sentiment analysis through NRC emotion lexicons based on Plutchik Model, this study aimed to decipher students’ sentiments of the academic services and reveal commonalities contained in their qualitative responses. Results revealed five latent themes in the students’ responses as: The Disparity of Teaching Assignment to Professors Field of Expertise, Professors’ Expression of Willingness to Help Students in School-Related Matters, Desirable Traits Portrayed by a Professional Teacher, Professor’s Commitment and Dedication to Classroom Instruction, and Enhancement of Teaching Practices to Improve Quality of Academic Services. The results also suggest that majority of the students have a positive sentiments (64.42%), some of were negative (34.62%), and very few were neutral (0.95%). This study aimed to give inputs to any academic interventions undertaken by institution.
... The development of the internet has generated a large number of texts with subjective emotions on the internet, and researchers have gradually evolved from simple analysis of emotional words to more complex emotional sentences or chapters in recent years (Shirsat et al., 2017). Zhong et al. (Yang et al., 2018;Wang et al., 2017;Zhong et al., 2014;Chafale and Pimpalkar, 2014) proposed a text classification algorithm based on matrix projection and normalised vector to realise the emotional analysis of commodity evaluation. A multi note-based algorithm has been proposed to address the problems of long training time of network models combined with attention mechanism and sequential input network, such as long short-term memory (LSTM), in specific target emotional analysis and the inability to input text in parallel. ...
... The display of sentiment sentences has evident emotional elements (emotional words). At present, explicit sentiment analysis achieves good progress and fruitful results regardless whether it is based on traditional dictionary, machine learning (Zhong et al., 2014;Chafale and Pimpalkar, 2014;Yu and Hatzivassiloglou, 2003;Gadhe et al., 2015), or deep neural network. Such analysis also performs well in emotional tendency identification. ...
... Implicit sentiment orientation analysis is more difficult than sentiment orientation analysis. In 1975, Wikis (Zheng and Wu, 2018;Chafale and Pimpalkar, 2014) argued that metaphor can be expressed as a violation of combinatory norms. On the basis of this idea, Balahur et al. (2012) automatically inferred the emotional tendencies of sentences without evident emotional clues from the context of sentences by using the knowledge base of common sense. ...
... The development of the internet has generated a large number of texts with subjective emotions on the internet, and researchers have gradually evolved from simple analysis of emotional words to more complex emotional sentences or chapters in recent years (Shirsat et al., 2017). Zhong et al. (Yang et al., 2018;Wang et al., 2017;Zhong et al., 2014;Chafale and Pimpalkar, 2014) proposed a text classification algorithm based on matrix projection and normalised vector to realise the emotional analysis of commodity evaluation. A multi note-based algorithm has been proposed to address the problems of long training time of network models combined with attention mechanism and sequential input network, such as long short-term memory (LSTM), in specific target emotional analysis and the inability to input text in parallel. ...
... The display of sentiment sentences has evident emotional elements (emotional words). At present, explicit sentiment analysis achieves good progress and fruitful results regardless whether it is based on traditional dictionary, machine learning (Zhong et al., 2014;Chafale and Pimpalkar, 2014;Yu and Hatzivassiloglou, 2003;Gadhe et al., 2015), or deep neural network. Such analysis also performs well in emotional tendency identification. ...
... Implicit sentiment orientation analysis is more difficult than sentiment orientation analysis. In 1975, Wikis (Zheng and Wu, 2018;Chafale and Pimpalkar, 2014) argued that metaphor can be expressed as a violation of combinatory norms. On the basis of this idea, Balahur et al. (2012) automatically inferred the emotional tendencies of sentences without evident emotional clues from the context of sentences by using the knowledge base of common sense. ...