Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Preliminarna kvalitativna analiza i posljedice percepcije drvnih proizvoda u društvenim medijima

The article presents the results of the qualitative research of social media, managed by the Institute of the Civil Society, University of Ss. Cyril and Methodius in Trnava, in cooperation with the Slovak University of Technology in Bratislava. The research aimed to analyse different areas of the current management challenges and their perception of the selected social networks. The study concentrates on the presentation of the chosen manufacturers of the automotive industry and furniture industry on social media. The content analysis was based on the VADER (Valence Aware Dictionary and Entiment Reasoner) lexicon that was explicitly tuned to sentiments expressed in social media and QDA software.članku su prikazani rezultati kvalitativnog istraživanja društvenih medija koje je proveo Zavod za civilno društvo Sveučilišta Svetog Ćirila i Metoda u Trnavi u suradnji sa znanstvenicima Slovačkoga tehnološkog sveučilišta u Bratislavi. Cilj istraživanja bio je analizirati različita područja aktualnih menadžerskih izazova i njihovu percepciju na odabranim društvenim mrežama. Studija je koncentrirana na prezentaciju proizvoda izabranih iz automobilske industrije i industrije namještaja u društvenim medijima. Analiza sadržaja temeljila se na leksikonu VADER (Valence Aware Dictionary and Entiment Reasoner), koji je eksplicitno utemeljen na dojmovima izraženim u društvenim medijima i na QDA softveru

Data science methods for the analysis of controversial social dedia discussions

Social media communities like Reddit and Twitter allow users to express their views on topics of their interest, and to engage with other users who may share or oppose these views. This can lead to productive discussions towards a consensus, or to contended debates, where disagreements frequently arise. Prior work on such settings has primarily focused on identifying notable instances of antisocial behavior such as hate-speech and “trolling”, which represent possible threats to the health of a community. These, however, are exceptionally severe phenomena, and do not encompass controversies stemming from user debates, differences of opinions, and off-topic content, all of which can naturally come up in a discussion without going so far as to compromise its development. This dissertation proposes a framework for the systematic analysis of social media discussions that take place in the presence of controversial themes, disagreements, and mixed opinions from participating users. For this, we develop a feature-based model to describe key elements of a discussion, such as its salient topics, the level of activity from users, the sentiments it expresses, and the user feedback it receives. Initially, we build our feature model to characterize adversarial discussions surrounding political campaigns on Twitter, with a focus on the factual and sentimental nature of their topics and the role played by different users involved. We then extend our approach to Reddit discussions, leveraging community feedback signals to define a new notion of controversy and to highlight conversational archetypes that arise from frequent and interesting interaction patterns. We use our feature model to build logistic regression classifiers that can predict future instances of controversy in Reddit communities centered on politics, world news, sports, and personal relationships. Finally, our model also provides the basis for a comparison of different communities in the health domain, where topics and activity vary considerably despite their shared overall focus. In each of these cases, our framework provides insight into how user behavior can shape a community’s individual definition of controversy and its overall identity.Social-Media Communities wie Reddit und Twitter ermöglichen es Nutzern, ihre Ansichten zu eigenen Themen zu äußern und mit anderen Nutzern in Kontakt zu treten, die diese Ansichten teilen oder ablehnen. Dies kann zu produktiven Diskussionen mit einer Konsensbildung führen oder zu strittigen Auseinandersetzungen über auftretende Meinungsverschiedenheiten. Frühere Arbeiten zu diesem Komplex konzentrierten sich in erster Linie darauf, besondere Fälle von asozialem Verhalten wie Hassrede und "Trolling" zu identifizieren, da diese eine Gefahr für die Gesprächskultur und den Wert einer Community darstellen. Die sind jedoch außergewöhnlich schwerwiegende Phänomene, die keinesfalls bei jeder Kontroverse auftreten die sich aus einfachen Diskussionen, Meinungsverschiedenheiten und themenfremden Inhalten ergeben. All diese Reibungspunkte können auch ganz natürlich in einer Diskussion auftauchen, ohne dass diese gleich den ganzen Gesprächsverlauf gefährden. Diese Dissertation stellt ein Framework für die systematische Analyse von Social-Media Diskussionen vor, die vornehmlich von kontroversen Themen, strittigen Standpunkten und Meinungsverschiedenheiten der teilnehmenden Nutzer geprägt sind. Dazu entwickeln wir ein Feature-Modell, um Schlüsselelemente einer Diskussion zu beschreiben. Dazu zählen der Aktivitätsgrad der Benutzer, die Wichtigkeit der einzelnen Aspekte, die Stimmung, die sie ausdrückt, und das Benutzerfeedback. Zunächst bauen wir unser Feature-Modell so auf, um bei Diskussionen gegensätzlicher politischer Kampagnen auf Twitter die oben genannten Schlüsselelemente zu bestimmen. Der Schwerpunkt liegt dabei auf den sachlichen und emotionalen Aspekten der Themen im Bezug auf die Rollen verschiedener Nutzer. Anschließend erweitern wir unseren Ansatz auf Reddit-Diskussionen und nutzen das Community-Feedback, um einen neuen Begriff der Kontroverse zu definieren und Konversationsarchetypen hervorzuheben, die sich aus Interaktionsmustern ergeben. Wir nutzen unser Feature-Modell, um ein Logistischer Regression Verfahren zu entwickeln, das zukünftige Kontroversen in Reddit-Communities in den Themenbereichen Politik, Weltnachrichten, Sport und persönliche Beziehungen vorhersagen kann. Schlussendlich bietet unser Modell auch die Grundlage für eine Vergleichbarkeit verschiedener Communities im Gesundheitsbereich, auch wenn dort die Themen und die Nutzeraktivität, trotz des gemeinsamen Gesamtfokus, erheblich variieren. In jedem der genannten Themenbereiche gibt unser Framework Erkenntnisgewinne, wie das Verhalten der Nutzer die spezifisch Definition von Kontroversen der Community prägt

Understanding Consumer Interaction on Instagram: The Role of Satisfaction, Hedonism, and Content Characteristics

The increasing relevance of Instagram and its growing adoption among top brands suggest an effort to better understand consumers'' behaviors within this context. The purpose of this study is to examine the role of perceived hedonism and satisfaction in determining consumers'' intentions to interact and their actual interaction behaviors (the number of likes, by tapping a heart icon, and comments) in a brand''s official Instagram account. Also, we investigate the effect of consumer perceptions about the characteristics of the content generated in the account (perceived originality, quantity, and quality) on their perceived hedonism and satisfaction. Data were collected in two stages from 808 members of a fashion brand''s official Instagram account. First, participants answered an online questionnaire to evaluate their perceptions, satisfaction, and interaction intentions. Second, 1 month later, we measure the number of likes and comments done by each participant in the brand''s official Instagram account during that month. Using partial least squares to analyze the data, perceived hedonism is found to affect both satisfaction and the intention to interact in Instagram, which in turn influences actual behavior. Besides, perceived originality is the most relevant content characteristic to develop perceived hedonism. These findings offer managers a general vision of consumers'' behaviors on Instagram, highlighting the importance of hedonism to create a satisfactory experience

A Call for Standardization and Validation of Text Style Transfer Evaluation

Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a meta-analysis on human and automated TST evaluation and experimentation that thoroughly examines existing literature in the field. The meta-analysis reveals a substantial standardization gap in human and automated evaluation. In addition, we also find a validation gap: only few automated metrics have been validated using human experiments. To this end, we thoroughly scrutinize both the standardization and validation gap and reveal the resulting pitfalls. This work also paves the way to close the standardization and validation gap in TST evaluation by calling out requirements to be met by future research.Comment: Accepted to Findings of ACL 202

Using Social Media Data to Analyse Issue Engagement During the 2017 German Federal Election

A fundamental tenet of democracy is that political parties present policy alternatives, such that the public can participate in the decision-making process. Parties, however, strategically control public discussion by emphasising topics that they believe will highlight their strengths in voters' minds. Political strategy has been studied for decades, mostly by manually annotating and analysing party statements, press coverage, or TV ads. Here we build on recent work in the areas of computational social science and eDemocracy, which studied these concepts computationally with social media. We operationalize issue engagement and related political science theories to measure and quantify politicians' communication behavior using more than 366k Tweets posted by over 1,000 prominent German politicians in the 2017 election year. To this end, we first identify issues in posted Tweets by utilising a hashtag-based approach well known in the literature. This method allows several prominent issues featuring in the political debate on Twitter that year to be identified. We show that different political parties engage to a larger or lesser extent with these issues. The findings reveal differing social media strategies by parties located at different sides of the political left-right scale, in terms of which issues they engage with, how confrontational they are and how their strategies evolve in the lead-up to the election. Whereas previous work has analysed the general public's use of Twitter or politicians' communication in terms of cross-party polarisation, this is the first study of political science theories, relating to issue engagement, using politicians' social media data

The good, the bad and the implicit: a comprehensive approach to annotating explicit and implicit sentiment

We present a fine-grained scheme for the annotation of polar sentiment in text, that accounts for explicit sentiment (so-called private states), as well as implicit expressions of sentiment (polar facts). Polar expressions are annotated below sentence level and classified according to their subjectivity status. Additionally, they are linked to one or more targets with a specific polar orientation and intensity. Other components of the annotation scheme include source attribution and the identification and classification of expressions that modify polarity. In previous research, little attention has been given to implicit sentiment, which represents a substantial amount of the polar expressions encountered in our data. An English and Dutch corpus of financial newswire, consisting of over 45,000 words each, was annotated using our scheme. A subset of this corpus was used to conduct an inter-annotator agreement study, which demonstrated that the proposed scheme can be used to reliably annotate explicit and implicit sentiment in real-world textual data, making the created corpora a useful resource for sentiment analysis

How can we save social media data?

The availability and scale of social media data offer researchers new opportunities to leverage those data for their work in broad areas such as public opinion, digital culture, labor trends, public health, and social movements. The success of efforts to save social media data for reuse by researchers will depend on aligning data management and archiving practices with evolving norms around capture, use, sharing, and security of datasets containing this new type of data. This paper presents an initial foray into understanding how established practices for managing and preserving data should adapt to new demands from social media data platforms, researchers who use and reuse social media data, and people who supply social media content and are subjects in social media data. We examine the data management practices of researchers who use social media data in research through a survey of researchers and an analysis d of published articles. We present results from 73 respondents and 40 papers and discuss the data management practices described, how they differ from management of more conventional data types, and the implications for creating and maintaining stable archives for these important research resources. We discuss the similarities and differences between social media data and other types of social science research data, including other types of “found” data, and discuss the implications for data archives wishing to include social media data in their collections.This material is based upon work supported by the National Science Foundation under Grant No. 1822228.https://deepblue.lib.umich.edu/bitstream/2027.42/149013/4/How can we save social media data.pdf1550Description of how can we save social media data.pdf : ManuscriptDescription of How can we save social media data.pdf : Main articl