Senpy: A framework for semantic sentiment and emotion analysis services

Sánchez-Rada, J. Fernando; Araque, Óscar; Iglesias, Carlos Á.

doi:10.1016/j.knosys.2019.105193

Cited by 7 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GIS spatial statistics method is to obtain the image information of large-scale areas by using remote sensing technology, splice and convert these image phenomena into data information, and then discrete extraction, so as to realize the data information statistics of GIS spatial location, carry out high-value statistics of effective data, and realize the summary and classification of specific data information [20]. Remote sensing discrete expansion algorithm is an intelligent calculation method based on GIS spatial statistical method strategy to discretize different spatial information and realize the highvalue analysis and quantitative representation of different spatial statistical data by setting the gradient rules of optimal value and optimal target solution [21].…”

Section: Application Of Remote Sensing Discrete Expansion Algorithm Inmentioning

confidence: 99%

Landscape Preference of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Technology and Emotion Analysis

Yang

2022

Security and Communication Networks

View full text Add to dashboard Cite

How to combine GIS spatial statistics technology to realize the intelligent and efficient classification of tourists’ landscape preferences is a hot spot in the direction of rural cultural tourism. Based on this, this paper studies the emotion analysis strategy based on GIS spatial statistical method in the field of landscape preference of rural cultural tourism tourists. Firstly, an intelligent and efficient classification model based on remote sensing discrete expansion algorithm is proposed. Combined with the observation strategy of common emotional rules in the landscape preference analysis of rural cultural tourism tourists, the landscape preference of rural cultural tourism tourists is processed with high quantitative emotional information, so as to realize the intelligent separation of preference, and then the error of discrete remote sensing data expansion model is corrected. Secondly, the judgment function of emotional rules is constructed, and the commonly used data layer-driven decentralized method is used in preference classification to eliminate the secondary factors in the landscape preference analysis of rural cultural tourism tourists. At the same time, the remote sensing discrete expansion algorithm is used to optimize and improve the emotional rules of high-volume emotional information based on GIS spatial statistical method. Finally, through the design of confirmatory experiments, the results show that the landscape preference analysis model based on GIS spatial statistics method and remote sensing discrete expansion algorithm is more efficient than the conventional preference classification method.

show abstract

Section: Application Of Remote Sensing Discrete Expansion Algorithm Inmentioning

confidence: 99%

Landscape Preference of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Technology and Emotion Analysis

Yang

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Analyzing the extracted textual data to enrich it, gain further insights, and discover patterns is crucial for the presented intelligent system. To achieve such analysis, the presented system uses Senpy [53], a framework to develop, integrate, and evaluate web services for text analysis. The text analyses performed in this module extracted emotional, psychological, and moral information from the text (for a detailed explanation of these linguistic analyses, please consult Section 4.3).…”

Section: Software Architecture and Componentsmentioning

confidence: 99%

“…Each vocabulary will be explained in further detail in Sections 3.1-3.3, respectively. Moreover, their usage combined with The Senpy Ontology (SENPY) vocabulary [53], a vocabulary to annotate a NIF Context, is exemplified in Section 3.4.…”

mentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

Self Cite

View full text Add to dashboard Cite

Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

show abstract

“…[1] propose a deep learning network for sentiment analysis. There are also some research and application of sentiment analysis, such as Sánchez et al [4] introduce an open-source service framework for sentiment analysis, Huddar et al [5] propose a multi-level feature optimization model for sentiment analysis, Chen et al [7] propose a personalized recommendation model based on sentiment analysis, Machová et al [8] study sentiment analysis in conversation content.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of News Reports in the Period of New Coronavirus

Li¹,

Pan²

2021

dtcse

View full text Add to dashboard Cite

The spread of new coronavirus has a significant impact on the world. To protect people from viruses, a large number of personnel and substances in research, production, medical treatment, and other aspects are used to deal with the virus. News reporting is an important way for people to obtain information about the coronavirus. Objective and immediate news reports can provide people with accurate information and correct response methods, thus playing their due role in defeating the coronavirus. In order to compare the emotional characteristics of news reports, the methods of natural language processing are used to conduct sentiment analysis on news reports, including the subjectivity and the polarity of news reports. We select the news websites of BBC and CNN, that have an important influence in the world, for comparative analysis. The results show that BBC news reports are more positive than CNN, and in terms of subjective, BBC News is more factual than CNN.

show abstract

Senpy: A framework for semantic sentiment and emotion analysis services

Cited by 7 publications

References 6 publications

Landscape Preference of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Technology and Emotion Analysis

Landscape Preference of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Technology and Emotion Analysis

Making Sense of Language Signals for Monitoring Radicalization

Sentiment Analysis of News Reports in the Period of New Coronavirus

Contact Info

Product

Resources

About