Aspect-based Sentiment Analysis using Dependency Parsing

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

#yeeyeenation

Using methods from country music studies, performance studies, hashtag ethnography, and Black Feminist Thought (BFT), this article employs sonic, discursive, and social media analysis to examine performances of White masculinity known as “country boys.” In the opening sections, I describe examples of country boys that emerge from Texas A&M University (College Station), bringing together confederate statues and the men who identify with and defend such statues. I then turn my focus to critical analysis of one country boy in particular: county music singer, brand progenitor, and Texas icon, Granger Smith a.k.a. Earl Dibbles Jr. Highlighting the importance of country boys to the cultural identity of Texas A&M University, I argue that White publics aggregate and accrue racialized and gendered meaning in social media spaces through signs associated with Smith like the hashtag #yeeyeenation. Such signs are predicated on and normalize a rhetoric—in this case, that something or someone “is not racist”—even in the face of evidence to the contrary. Extending the insights of scholarship on the former Confederacy to contemporary country music cultures and to the present political moment, this article interrogates how White identities and related genealogies in the U.S. context are not simply established to sanitize and excuse expressions of racist, gendered, and exclusionary thought, but are sustained by aestheticized deceptions. I refer to these deceptions as mythopoetics. In this article I demonstrate how Smith’s success, particularly since he is best known for his “redneck” alter-ego, Earl Dibbles Jr., is a testament to the power and reach of mythopoetics in a hegemonic White and heteropatriarchal society. I argue that mythopoetics are not only essential to majoritarian cultural formations today, but also normalize White supremacy to such a point that its violence can circulate without consequence and in plain sight.

HPV Vaccination: Polish-Language Facebook Discourse Analysis

Social media platforms are widely used for spreading vaccine-related information. The objectives of this paper are to characterize Polish-language human papillomavirus (HPV) vaccination discourse on Facebook and to trace the possible influence of the COVID-19 pandemic on changes in the HPV vaccination debate. A quantitative and qualitative analysis was carried out based on data collected with a tool for internet monitoring and social media analysis. We found that the discourse about HPV vaccination bearing negative sentiment is centralized. There are leaders whose posts generate the bulk of anti-vaccine traffic and who possess relatively greater capability to influence recipients’ opinions. At the beginning of the COVID-19 pandemic vaccination debate intensified, but there is no unequivocal evidence to suggest that interest in the HPV vaccination topic changed.

Sadness, Negativity, and Uncertainty in Education During COVID-19 on Social Media

The global COVID-19 pandemic increased social media usage to obtain information and to share concerns, feelings, and emotions, turning it into a prolific field of research through which it is possible to understand how audiences are coping with the multitude of recent challenges. This paper presents results from a social media analysis of 61532 education-related news headlines posted by the major daily news provider in Portugal, Sic Notícias, on Facebook, from January to December 2020. We focus on how the news impacted on audiences’ emotional response and discourse, and we analyze the key issues of the most commented news content. The results show a prevailing sadness among audiences and a very negative discourse all throughout 2020, with a high degree uncertainty being expressed. The main concerns revolved around parents supporting children in their first remote learning endeavors, financial sustainability, the lack of devices, the disinfection of schools, and the students’ mobility, particularly in the non-higher education context.

The Spatial Dimension in Social Media Analysis

In the data revolution era, the availability of “voluntary” and “derived from social media” geographic information allowed the spatial dimension to gain attention in digital and web studies. The purpose of this work is to recognize the impact of this research stream on some methodological and theoretical issues. The first regards “critical algorithm studies” in order to understand what algorithms are used. The second concerns how these works conceive the space. The last two issues concern the disciplinary areas in which these researches take place and which are the ecological units taken into account. The authors answer these questions by analyzing, through a content analysis, the researches extracted with the PRISMA methodology that have used Twitter as a data source. The application of this procedure allows the authors to classify the analysis material, moving simultaneously on the four defined dimensions.

Social Media Analysis to Understand the Expected Benefits by Plant-Based Meat Alternatives Consumers

The plant-based alternative meat products market has attracted attention in recent years, as the demand for these products has grown worldwide. To meet the needs of this promising market, marketers must pay attention to the expected benefits of consumers and the insights that can be gleaned from comments posted on social media. This article proposed an investigation of the potential of the content analysis of comments posted on the Instagram social network of food companies that manufacture plant-based alternative meat products to understand the expected benefits by end consumers from the perspective of the classic marketing mix variables. The content posted voluntarily by consumers was organized into 13 categories of expected benefits analyzed within a proposal of evidence from the perspective of the marketing mix. The results showed that, among the insights obtained, 63% were related to the place variable, 21% to the product variable, 11% to the price variable, and 5% to the promotion variable. The insights reinforce the notion that marketing mix variables are crucial factors for companies to make products available in the right place, in the right quantity, and at a fair price, in addition to engaging with consumers through social media.

Creating Brand Image Profile by Social Media Analysis

The purpose of creating a brand image profile is to measure the brand perception of consumers considering brand attributes. Thus, marketing decisions can be made based on the brand's strengths and weaknesses by determining them. The brand image profile is traditionally created using the attitude scales and surveys. However, alternative methods are needed since the questionnaires' responses are careless, the number of participants is relatively low and the cost per participant is high. In this study, as an alternative method, creating a brand image profile by analyzing social media data with artificial intelligence was made for the iPhone product. Firstly, the focus group study determined the attributes related to the last version of the iPhone. Then, between December 17th, 2019 and March 23rd, 2020, 87.227 tweets that include these attributes in English were collected from the Twitter social media platform through the RapidMiner data mining tool. Sentiment analysis was performed on collected tweets by the MeaningCloud text mining tool. In this analysis, positive and negative emotions were tried to be detected through artificial intelligence algorithms. Net Brand Reputation Score (NBR) was calculated using the positive and negative tweets amount for each attribute separately. Brand image profile was created by skew analysis using NBR values. As a result, it is thought that social media analysis can be a complementary method that can be used with traditional methods in creating a brand image profile. So, it is seen as an inevitable method to use in further studies to make sentiment analysis by processing raw data received from the Social Media platforms through artificial intelligence algorithms to transform the product label or the perspectives of an event into meaningful information.

Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to Covid-19: topic modelling and sentiment analysis (Preprint)

BACKGROUND Social media contains an overabundance of health information relating to people living with different type of diseases. Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts and reported trends have revealed a considerable increase in prevalence and incidence. Research had shown that the ASD community provides significant support to its members through Twitter, providing information about their values and perceptions through their use of words and emotional stance. OBJECTIVE Our purpose was to analyze the messages posted on Twitter platform regarding ASD and analyze the topics covered within the tweets, in order to understand the attitude of the various people interested in the topic. In particular, we focused on the discussion of ASD and Covid-19. METHODS The data collection process was based on the search for tweets through hashtags and keywords. After bots screening, the NMF (Non-Negative Matrix Factorization) method was used for topic modeling because it produces more coherent topics compared to other solutions. Sentiment scores were calculated using AFiNN for each tweet to represent its negative to positive emotion. RESULTS From the 2.458.929 tweets produced in 2020, 691.582 users were extracted (188 bots which generated 59.104 tweets), while from the 2.393.236 total tweets from 2019, the number of identified users was 684.032 (230 bots which generated 50.057 tweets). The number of tweets and the topics covered are very similar between 2019 and 2020. The total number of Covid-asd tweets is only a small part of the total dataset. Often, the negative sentiment identified in the sentiment analysis referred to anger towards Covid-19 and its management, while the positive sentiment reflected the necessity to provide constant support to people with ASD. CONCLUSIONS Social media contributes to a great discussion on topics related to autism, especially with regards to focus on family, community, and therapies. The Covid-19 pandemic increased the use of social media, especially during the lockdown period. It is important to help develop and distribute appropriate, evidence-based ASD-related information.