scholarly journals Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 535 ◽  
Author(s):  
Alejandro Ramón-Hernández ◽  
Alfredo Simón-Cuevas ◽  
María Matilde García Lorenzo ◽  
Leticia Arco ◽  
Jesús Serrano-Guerrero
Keyword(s):  
Topic Modeling ◽  
Social Impact ◽  
Opinion Mining ◽  
Relevant Information ◽  
User Generated Content ◽  
Context Aware ◽  
Government Programs ◽  
Digital Platforms ◽  
Opinion Summarization ◽  
Digital Governance

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

A Novel Approach for Crawling the Opinions from World Wide Web

2016 ◽  
Vol 6 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Surbhi Bhatia ◽  
Manisha Sharma ◽  
Komal Kumar Bhatia
Keyword(s):  
World Wide ◽  
Opinion Mining ◽  
Real Data ◽  
User Generated Content ◽  
Decision Making Process ◽  
Web Pages ◽  
Data Sets ◽  
Web Technologies ◽  
Design And Implementation ◽  
Novel Approach

Due to the sudden and explosive increase in web technologies, huge quantity of user generated content is available online. The experiences of people and their opinions play an important role in the decision making process. Although facts provide the ease of searching information on a topic but retrieving opinions is still a crucial task. Many studies on opinion mining have to be undertaken efficiently in order to extract constructive opinionated information from these reviews. The present work focuses on the design and implementation of an Opinion Crawler which downloads the opinions from various sites thereby, ignoring rest of the web. Besides, it also detects web pages which frequently undergo updation by calculating the timestamp for its revisit in order to extract relevant opinions. The performance of the Opinion Crawler is justified by taking real data sets that prove to be much more accurate in terms of precision and recall quality attributes.

scholarly journals International TV series distribution on Chinese digital platforms: Marketing strategies and audience engagement

2018 ◽  
Vol 3 (3) ◽  
pp. 213-230 ◽  
Author(s):  
Filippo Gilardi ◽  
Celia Lam ◽  
K Cohen Tan ◽  
Andrew White ◽  
Shuxin Cheng ◽  
...  
Keyword(s):  
Case Studies ◽  
Cultural Context ◽  
Marketing Strategies ◽  
User Generated Content ◽  
Online Media ◽  
Media Content ◽  
Online Video ◽  
Audience Engagement ◽  
Digital Platforms ◽  
The Relationship

The relationship between online media platforms in China and fan groups is a dynamic one when it comes to the distribution of international TV series and other media content, as media platforms incorporate user-generated content to encourage or foster audience engagement. Through a series of case studies, this article investigates how international TV series are acquired, distributed, marketed and curated on Chinese online video platforms. This helps to identify specific strategies and themes used by these platforms to promote international content and engage users. These marketing techniques, however, are not always as successful as expected, suggesting the need for a closer examination of the types of engagement sought by media platforms, and the ways in which Chinese audiences have responded within their cultural context.

scholarly journals Digital Platforms for the Common Good: Social Innovation for Active Citizenship and ESG

2022 ◽  
Vol 14 (2) ◽  
pp. 639
Author(s):  
Evangelos Katsamakas ◽  
Kostapanos Miliaresis ◽  
Oleg V. Pavlov
Keyword(s):  
Business Model ◽  
Common Good ◽  
Social Impact ◽  
Network Effects ◽  
Social Innovation ◽  
Feedback Loops ◽  
Social Entrepreneurs ◽  
Digital Platforms ◽  
The Common Good ◽  
The Common

The platform business model has attracted significant attention in business research and practice. However, much of the existing literature studies commercial platforms that seek to maximize profit. In contrast, we focus on a platform for volunteers that aims to maximize social impact. This business model is called a platform for the common good. The article proposes a Causal Loop Diagram (CLD) model that explains how a platform for the common good creates value. Our model maps the key strategic feedback loops that constitute the core structure of the platform and explains its growth and performance through time. We show that multiple types of network effects create interlocking, reinforcing feedback loops. Overall, the article contributes towards a dynamic theory of the platforms for the common good. Moreover, the article provides insights for social entrepreneurs who seek to build, understand, and optimize platforms that maximize social value and managers of companies that seek to participate in such platforms. Social entrepreneurs should seek to leverage the critical feedback loops of their platform.

Learning landslide hazard in a virtual environment at University level combining real case study, collaborative work and innovative tools

2021 ◽  
Author(s):  
Marta Guinau ◽  
Gloria Furdada
Keyword(s):  
Digital Media ◽  
Collaborative Work ◽  
Relevant Information ◽  
Landslide Hazard ◽  
Mitigation Strategies ◽  
Significant Information ◽  
Digital Platforms ◽  
Face To Face ◽  
Triggering Factors ◽  
Different Types

<p>The pandemic situation we are experiencing has forced us to transform face-to-face teaching into virtual teaching. Digital platforms hinder the interaction, discussion and feedback that naturally occur in a face-to-face class, but at the same time, they provide an opportunity to put the focus on the student’s learning rather than on content delivering. Learning include both, inductive and deductive processes; induction can be effectively acquired by using case studies; then, deduction can be achieved through comparison, analysis, generalisation and synthesis.  Digital platforms appear as an optimal resource to facilitate the individual and collaborative tasks and learning processes. In this work we present our experience on the landslide hazard subject (Master’s level) focussed on the student’s learning through the use of digital media.</p><p>Internet information of undeniable quality that can be easily accessed is basic: The Landslide Blog by Dave Petley (https://blogs.agu.org/landslideblog/) in Blogosphere hosted by AGU (American Geophysical Union) provides valuable and updated information on landslide events occurring worldwide. The learning activities are structured around several cases selected by the lecturer from the blog to ensure the analysis of the most frequent landslide types. All activities are developed in 8 steps: 1) The teacher presents the learning action (objective, tasks, and assessment guide) using a Genially platform interactive image; 2) Each student selects one of the proposed cases and compile relevant information about it; 3) Each student analyses the landslide characteristics, identifies the landslide type  and classifies it according to Hungr et al., 2014 (available through the educational virtual platform), and recognises the control and triggering factors (one virtual session is programmed and a forum tool is provided to the students to discuss and to solve doubts); 4) Each student selects and organizes the significant information about each case by building an interactive image in Genially; 5) Each student presents each case using his/her interactive image in a virtual session, which is recorded and uploaded to the educational platform; 6) Students peer evaluate the content and design of the interactive images and oral presentations based on the provided assessment guide; 7) During a predetermined time, students collaboratively compile all the information in a Google sheet table to synthesize the geomorphological characteristics, materials involved, mobilization mechanisms and control and triggering factors of the different types of landslides; 8) the synthetic table is discussed and  completed during a virtual session.</p><p>All the knowledge and skills acquired by students with these activities are put into practice in a two-day field trip where students have to identify, characterize and classify different types of landslides as well as their control and triggering factors. The risk situation and the mitigation strategies are discussed in each case and compared to the ones studied through virtual learning. Furthermore, students get used and learn how to clearly present information through virtual tools, as Genially, useful for dissemination purposes.</p><p>Hungr et al. 2014. The Varnes classification of landslide types, an update. Landslides 11(2). DOI: 10.1007/s10346-013-0436-y</p>

Opinion Mining

2017 ◽  
pp. 193-206
Author(s):  
Rawan T. Khasawneh ◽  
Izzat Alsmadi
Keyword(s):  
Information Technologies ◽  
Opinion Mining ◽  
Smart Phones ◽  
User Generated Content ◽  
Communication Tools ◽  
Daily Life Activities ◽  
Political Debates ◽  
Internet Users ◽  
Internet Information ◽  
Source Of Information

In recent years social media sites become very popular communication tools among Internet users where a significant amount of information is exchanged via computers, smart phones, etc. Internet now is not only a source of information for users to search for; regular users are now a major source of Internet information; where now regular people post daily life activities, share online pictures, and express their opinions about products, news, political debates, etc. Such noticed growing of opinion-rich resources along with user-generated content makes it worthwhile to use information technologies to collect, analyze, and understand human factors and behaviors. This chapter covers three main sections where the first section introduces the field of opinion mining in general along with a detailed exploration of its definitions and goals. Then a discussion of opinion mining related challenges is presented in the second section. The last section explores opinion mining available approaches along with possible future directions.

User-Generated Content and Consumer Purchase Intentions in the Cosmetic Industry

2021 ◽  
pp. 1084-1106
Author(s):  
Jemi Patel
Keyword(s):  
Social Networking ◽  
Social Impact ◽  
Marketing Strategies ◽  
User Generated Content ◽  
Digital Marketing ◽  
Computer Mediated ◽  
Online Retailers ◽  
Impact Theory ◽  
Cosmetic Industry ◽  
Luxury Cosmetics

Online retailers within the luxury cosmetics industry have grown in popularity due to a wider and more diverse catalogue of products. Beauty e-commerce has also seen an uplift due to the increase in blogs/vlogs and online YouTube tutorials which motivate customers to click through to brands and retailer sites through links and affiliate marketing. Given the importance of computer-mediated marketing environments, particularly the burgeoning Internet tapestry along with its various social networking platforms, it is fundamental for management to foster and understand how these emerging technologies impact on their marketing strategies. Drawing on social impact theory (SIT), this paper contends that user-generated content can provide the basis for brand managers in the cosmetic industry to re-evaluate their digital marketing strategies. The paper concludes with discussions about the value of social impact theory in the development of digital marketing strategies.

A hybrid recommender system for the mining of consumer preferences from their reviews

2019 ◽  
Vol 46 (5) ◽  
pp. 664-682
Author(s):  
Li Chen Cheng ◽  
Ming-Chan Lin
Keyword(s):  
Opinion Mining ◽  
Information Overload ◽  
Consumer Preferences ◽  
Online Reviews ◽  
User Generated Content ◽  
Mining Technique ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Filtering Approach

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.

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