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Relevant bibliographies by topics / Sentimenti positivi

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Journal articles

Academic literature on the topic 'Sentimenti positivi'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Sentimenti positivi"

1

Mancini, Giacomo, Nicole Righi, Elena Trombini, and Roberta Biolcati. "Intelligenza emotiva di tratto e burnout professionale negli insegnanti di scuola primaria. Una revisione della letteratura." RICERCHE DI PSICOLOGIA, no. 1 (May 2022): 1–22. http://dx.doi.org/10.3280/rip2022oa13705.

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La presente rassegna si propone di esaminare le pubblicazioni scientifiche internazionali che hanno indagato il rapporto tra l'Intelligenza Emotiva (intesa secondo il modello dei tratti e valutata attraverso questionari self-report), e il burnout professionale (caratterizzato da esaurimento emotivo, sentimenti di depersonalizzazione e ridotta autoefficacia) negli insegnanti di scuola primaria. Le recenti ricerche in questo campo, che non sono ancora state sufficientemente sistematizzate, sottolineano infatti l'importanza delle competenze emotive per facilitare e migliorare sia la prestazione l

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2

Haidar, Abdullah, and Putri Oktavia Rusadi. "A Sentiment Analysis: History of Islamic Economic Thought." Journal of Islamic Economics (JoIE) 2, no. 2 (2022): 150–63. http://dx.doi.org/10.21154/joie.v2i2.5082.

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This study reviews the history of Islamic economic thought research in Islamic economics and finance. It uses descriptive statistical analysis based on selected 125 article publications. The entire sample publications have been published from 1984 to 2022. This study analyzes the number of publications based on journal and year, the top authors, the top-cited paper, and the sentiment analysis. The results show that the research of the history of Islamic economic thought throughout the world has a high-positive sentiment of 1%, a positive sentiment of 27%, a negative sentiment of 33%, a high-ne

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3

Rossi, Roberta, Elisabetta Todaro, Giovanna Torre, and Chiara Simonelli. "Omosessualitŕ e desiderio di genitorialitŕ: indagine esplorativa su un gruppo di omosessuali italiani." RIVISTA DI SESSUOLOGIA CLINICA, no. 1 (July 2010): 23–40. http://dx.doi.org/10.3280/rsc2010-001002.

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Il desiderio di avere un figlio rappresenta un tipo di progettualitŕ multidimensionale, complessiva ed allargata per l'identitŕ individuale e di coppia. L'obiettivo della presente ricerca consiste nell'esplorare la presenza del desiderio di genitorialitŕ in un gruppo di omosessuali italiani, approfondendo le motivazioni ed il grado di riflessivitŕ e d'intensitŕ del desiderio di avere un figlio. La ricerca ha coinvolto 226 soggetti (143 M; 83 F) di etŕ compresa tra i 17 ed i 67 anni (media 31 anni; DS 9.36). Le aree indagate nel presente lavoro sono: dati sociodemografici, l'orientamento sessua

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4

Apif Supriadi and Fatmasari. "Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web." Jurnal Sistem Informasi 10, no. 1 (2021): 46–54. http://dx.doi.org/10.51998/jsi.v10i1.356.

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Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify

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5

Afzaal, Muhammad, Muhammad Usman, and Alvis Fong. "Predictive aspect-based sentiment classification of online tourist reviews." Journal of Information Science 45, no. 3 (2018): 341–63. http://dx.doi.org/10.1177/0165551518789872.

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With the increase of online tourists reviews, discovering sentimental idea regarding a tourist place through the posted reviews is becoming a challenging task. The presence of various aspects discussed in user reviews makes it even harder to accurately extract and classify the sentiments. Aspect-based sentiment analysis aims to extract and classify user’s positive or negative orientation towards each aspect. Although several aspect-based sentiment classification methods have been proposed in the past, limited work has been targeted towards the automatic extraction of implicit, infrequent and c

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6

Huang, Changqin, Zhongmei Han, Ming Li, Xizhe Wang, and Wenzhu Zhao. "Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis." Australasian Journal of Educational Technology 37, no. 2 (2021): 81–95. http://dx.doi.org/10.14742/ajet.6749.

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Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistem

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7

Zhou, Xinyi, Shengmin Jin, and Reza Zafarani. "Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?" Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 798–807. http://dx.doi.org/10.1609/icwsm.v14i1.7344.

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Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users are indeed (not just illusively) more positive than the users themselves. This is mostly due to positive users having more friends. We identify five sentiment paradoxes at different network levels ranging from triads to large-scale communities. Empir

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8

Kumar, Abhishek, Vishal Dutt, Vicente García-Díaz, and Sushil Kumar Narang. "Twitter sentimental analysis from time series facts: the implementation of enhanced support vector machine." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2845–56. http://dx.doi.org/10.11591/eei.v10i5.3078.

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Sentiment analysis through textual data mining is an indispensable system used to extract the contextual social information from the texts submitted by the intended users. Now days, world wide web is playing a vital source of textual content being shared in different communities by the people sharing their own sentiments through the websites or web blogs. Sentiment analysis has become a vital field of study since based on the extracted expressions, individuals or the businesses can access or update their reviews and take significant decisions. Sentimental mining is typically used to classify t

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9

Gao, Xiang, Weige Huang, and Hua Wang. "Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility." Virtual Economics 4, no. 1 (2021): 7–18. http://dx.doi.org/10.34021/ve.2021.04.01(1).

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This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-s

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10

Mushtaq, Muhammad Faheem, Mian Muhammad Sadiq Fareed, Mubarak Almutairi, Saleem Ullah, Gulnaz Ahmed, and Kashif Munir. "Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques." Vaccines 10, no. 5 (2022): 661. http://dx.doi.org/10.3390/vaccines10050661.

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COVID-19 is a widely spread disease, and in order to overcome its spread, vaccination is necessary. Different vaccines are available in the market and people have different sentiments about different vaccines. This study aims to identify variations and explore temporal trends in the sentiments of tweets related to different COVID-19 vaccines (Covaxin, Moderna, Pfizer, and Sinopharm). We used the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool to analyze the public sentiments related to each vaccine separately and identify whether the sentiments are positive (compound ≥ 0.05), nega

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