When Emotions Rule Knowledge

2021 ◽  
Vol 17 (3) ◽  
pp. 1-16
Author(s):  
Nora Fteimi ◽  
Olivia Hornung ◽  
Stefan Smolnik
Keyword(s):  
Knowledge Management ◽  
Sentiment Analysis ◽  
Human Behavior ◽  
Negative Emotions ◽  
Comprehensive Understanding ◽  
High Term ◽  
Holistic View ◽  
Rule Knowledge ◽  
Sentiment Dictionary ◽  
Positive And Negative Emotions

Although emotions play an important role in human behavior and knowledge studies, knowledge management (KM) research considers them from specific angles and, to date, has lacked a comprehensive understanding of the emotions dominating KM. To offer a holistic view, this study investigates the presence of emotions in KM publications by applying a sentiment analysis. The authors present a sentiment dictionary tailored to KM, apply it to KM publications to determine where and how emotions occur, and categorize them on an emotion scale. The considerable amount of positive and negative emotions expressed in KM studies prove their relevance to and dominance in KM. There is high term diversity but also a need to consolidate terms and emotion categories in KM. This study's results provide new insights into the relevance of emotions in KM research, while practitioners can use this method to detect emotion-laden language and successfully implement KM initiatives.

Election Forecasts With Twitter

2010 ◽  
Vol 29 (4) ◽  
pp. 402-418 ◽  
Author(s):  
Andranik Tumasjan ◽  
Timm O. Sprenger ◽  
Philipp G. Sandner ◽  
Isabell M. Welpe
Keyword(s):  
Sentiment Analysis ◽  
Political Party ◽  
Negative Emotions ◽  
The Political ◽  
Federal Election ◽  
Election Result ◽  
Political Landscape ◽  
Political Preferences ◽  
Election Results ◽  
Positive And Negative Emotions

This study investigates whether microblogging messages on Twitter validly mirror the political landscape off-line and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets' sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters' political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

scholarly journals Positive Emotions Boost Enthusiastic Responsiveness to Capitalization Attempts. Dissecting Self-Report, Physiology, and Behavior

2021 ◽  
Author(s):  
Lukasz D. Kaczmarek ◽  
Todd B. Kashdan ◽  
Maciej Behnke ◽  
Martyna Dziekan ◽  
Ewelina Matuła ◽  
...  
Keyword(s):  
Positive Emotions ◽  
Negative Emotions ◽  
Emotional Valence ◽  
Self Report ◽  
Physiological Reactivity ◽  
Direct Effects ◽  
High Quality ◽  
Video Clips ◽  
And Behavior ◽  
Positive And Negative Emotions

AbstractWhen individuals communicate enthusiasm for good events in their partners' lives, they contribute to a high-quality relationship; a phenomenon termed interpersonal capitalization. However, little is known when individuals are more ready to react enthusiastically to the partner's success. To address this gap, we examined whether positive and negative emotions boost or inhibit enthusiastic responses to partner's capitalization attempts (RCA). Participants (N = 224 individuals) responded to their partner's success. Before each capitalization attempt (operationalized as responses following the news that their partner won money in a game), we used video clips to elicit positive (primarily amusement) or negative (primarily anger) or neutral emotions in the responder. We recorded emotional valence, smiling intensity, verbal RCA, and physiological reactivity. We found indirect (but not direct) effects such that eliciting positive emotions boosted and negative emotions inhibited enthusiastic RCA (smiling intensity and enthusiastic verbal RCA). These effects were relatively small and mediated by emotional valence and smiling intensity but not physiological reactivity. The results offer novel evidence that positive emotions fuel the capitalization process.

scholarly journals MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 115 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Ehoche Faith
Keyword(s):  
Sentiment Analysis ◽  
Interaction Design ◽  
Mobile Application ◽  
Situational Awareness ◽  
Mobile Internet ◽  
The Public ◽  
Public Sentiment ◽  
High Altitude Area ◽  
Sentiment Dictionary ◽  
Analysis System

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

The interplay of positive and negative emotions to quit unhealthy consumption behaviors: Insights for social marketers

2020 ◽  
Vol 28 (4) ◽  
pp. 349-360
Author(s):  
Syed Muhammad Fazal-e-Hasan ◽  
Hormoz Ahmadi ◽  
Gary Mortimer ◽  
Harjit Sekhon ◽  
Husni Kharouf ◽  
...  
Keyword(s):  
Negative Emotions ◽  
Positive And Negative Emotions
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