Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

Sentiment Analysis on Twitter: Role of Healthcare Professionals in the Global Conversation during the AstraZeneca Vaccine Suspension

The vaccines against COVID-19 arrived in Spain at the end of 2020 along with vaccination campaigns which were not free of controversy. The debate was fueled by the adverse effects following the administration of the AstraZeneca-Oxford (AZ) vaccine in some European countries, eventually leading to its temporary suspension as a precautionary measure. In the present study, we analyze the healthcare professionals' conversations, sentiment, polarity, and intensity on social media during two periods in 2021: the one closest to the suspension of the AZ vaccine and the same time frame 30 days later. We also analyzed whether there were differences between Spain and the rest of the world. Results: The negative sentiment ratio was higher (U = 87; p = 0.048) in Spain in March (Med = 0.396), as well as the daily intensity (U = 86; p = 0.044; Med = 0.440). The opposite happened with polarity (U = 86; p = 0.044), which was higher in the rest of the world (Med = -0.264). Conclusions: There was a general increase in messages and interactions between March and April. In Spain, there was a higher incidence of negative messages and intensity compared to the rest of the world during the March period that disappeared in April. Finally, it was found that the dissemination of messages linked to negative emotions towards vaccines against COVID-19 from healthcare professionals contributed to a negative approach to primary prevention campaigns in the middle of the pandemic.

Mpox panic, infodemic, and stigmatization of the 2SLGBTQIAP+ community: geospatial analysis, topic modeling, and sentiment analysis of a large, multilingual social media database (Preprint)

BACKGROUND

The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community.

OBJECTIVE

The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)-related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination.

METHODS

In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets' main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets' and Facebook posts' languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries.

RESULTS

The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04).

CONCLUSIONS

The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies.

Organizational Adoption of Sentiment Analytics in Social Media Networks

Enterprise adoption and application of sentiment analytics (SA) has recently attracted significant interest from both academia and industry, as it offers exciting opportunities to generate competitive intelligence on consumer attitudes and opinions. Yet, there is limited understanding of the factors underlying successful and widespread adoption of SA in enterprises. This study presents a systematic literature review (SLR) to analyze and summarize previous research on corporate adoption of SA in social media. The SLR examines the results of 83 studies and focuses on tasks, techniques, application domains, and factors that influence enterprise adoption of SA. The findings provide insights into (i) key factors influencing SA adoption, (ii) research trends and paradigms across disciplines, and (iii) potential areas for future research on enterprise adoption of SA. These findings recommend actionable future research agendas for scholars and inform practitioners' understanding of the decision-making processes involved in enterprise adoption of SA in social media.

Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews

The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). This estimation was based on review ratings and on sentiment polarity in the comments, with respect to specific aspects, such as “lecturer”, “presentation”, etc. Between course sessions, users stop reviewing, and also, the course contents may evolve. Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera.org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses.The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes. Among the supervised methods, the J48 decision trees algorithm yielded the best performance, compared to the naive Bayes model and the SVM model.

Facebook Data as Part of Cultural Heritage Investments Toolbox: Pilot Analysis of Users Interests and Preferences Concerning Adaptive Reuse

The growing popularity of social media data brings questions about its accuracy and usefulness for a wide array of cultural heritage projects, often lacking data sources crucial for better planning and implementation. In this paper, we are studying the opportunities offered by the freely available Facebook Ads Manager data on target group sizes in different locations. We conducted a small experiment and a pilot survey to verify if Facebook data concerning its users’ interests is accurate and could be used to facilitate the implementation of projects in the field of adaptive reuse. Despite all discovered limitations of this approach, we point out how Facebook data, along with other social media outlets, can be used to extract some strategic information and add to the socio-cultural assessment toolbox. As such, this type of data could be of use to local leaders planning activities and investments around cultural heritage sites. We also posit that such data can facilitate benefit transfer between cities through better understanding of local preferences and values-orientations.

Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce

The advent and incorporation of technology in businesses have reformed operations across industries. Notably, major technical shifts in e-commerce aim to influence customer behavior in favor of some products and brands. Artificial intelligence (AI) comes on board as an essential innovative tool for personalization and customizing products to meet specific demands. This research finds that, despite the contribution of AI systems in e-commerce, its ethical soundness is a contentious issue, especially regarding the concept of explainability. The study adopted the use of word cloud analysis, voyance analysis, and concordance analysis to gain a detailed understanding of the idea of explainability as has been utilized by researchers in the context of AI. Motivated by a corpus analysis, this research lays the groundwork for a uniform front, thus contributing to a scientific breakthrough that seeks to formulate Explainable Artificial Intelligence (XAI) models. XAI is a machine learning field that inspects and tries to understand the models and steps involved in how the black box decisions of AI systems are made; it provides insights into the decision points, variables, and data used to make a recommendation. This study suggested that, to deploy explainable XAI systems, ML models should be improved, making them interpretable and comprehensible.

Sustainability in Marketing through Customer Relationship Management in a Telecommunication Company

The idea of sustainable development links business, environmental and social objectives into one integrated effort aiming to meet a common goal. Due to the influence of current developments in customer behaviour companies revise their business strategies towards more sustainably oriented ways of production, business practices, resource efficiency, waste disposal, building partnerships, communication effectiveness etc. Market with telecommunication services is affected by technology development. Growing demands of customers result in re-evaluation of marketing routine. The main purpose of the research is to identify the change in customers’ approaches and satisfaction with offered products, enterprise processes, and business strategy as a result of implementing Customer Relationship Management in 2014 and subsequently in 2018 with the dependence on socio-demographic characteristics. Customers’ opinions were investigated using a questionnaire. Respondents were selected by direct sampling with contingent valuation. Sample set consisted of 3,282 respondents. Following the results, the fact that there are changes in approaches of customers in investigated areas in terms of gender and age as a result of implementing the Customer Relationship Management can be stated. The customers’ satisfaction with provided services improved. In order to serve the new and constantly evolving needs of the growing number of customers who care about social and environmental issues, the companies are required to permanently monitor their performance on the market and incorporate customer feedback as in input for evaluation of their internal processes. In terms of marketing, positive changes in approach, satisfaction with the quality of provided services and reduced service time were observed. In terms of business strategy, the changes in connection strategy, the effect of customer-driven strategy and services provided by call centres abroad are perceived in positive way. Keywords: sustainability, economical sustainability, marketing, Customer Relationship Management, telecommunication company.