A linked data approach to sentiment and emotion analysis of twitter in the financial domain

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information

Analiza raspoloženja tvitova predsjednika Trumpa: od pobjede na izborima do borbe protiv COVID-19

Twitter, as one of the popular social networks today and big data generator, can affect and change the public discourse, so political candidates are using it extensively as the vehicle for attracting and keeping their followers. Since Donald Trump\u27s 2016 presidency election, his Twitter account with millions of followers has become an important subject for various statistical analyses, mostly because of his controversy. Therefore, this paper uses sentiment analysis of a large set of his tweets to explore his influence, as well the set of affective and cognitive aspects of his messages. The results of this analysis indicate what kind of findings in political domain can be recognized from tweets, and how they can be interpreted.Twitter, kao jedna od popularnih društvenih mreža današnjice i generator velikih podataka, može utjecati i mijenjati javni diskurs, pa ga politički kandidati intenzivno koriste kao sredstvo za privlačenje i održavanje pinga svojih pratitelja. Od predsjedničkih izbora Donalda Trumpa 2016., njegov Twitter račun s milijunima pratitelja postao je važan predmet raznih statističkih analiza, ponajviše zbog njegove kontroverze. Stoga ovaj rad koristi analizu sentimenta velikog skupa njegovih tweetova kako bi istražio njegov utjecaj, kao i skup afektivnih i kognitivnih aspekata njegovih poruka. Rezultati ove analize ukazuju na to kakva se saznanja u političkoj domeni mogu prepoznati iz tweetova i kako ih se može interpretirati

Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review

Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. Over the past decade, SA has gained significant popularity in the field of Natural Language Processing (NLP). With the widespread use of social media and online platforms, SA has become crucial for companies to gather customer feedback and shape their marketing strategies. Additionally, researchers rely on SA to analyze public sentiment on various topics. In this particular research study, a comprehensive survey was conducted to explore the latest trends and techniques in SA. The survey encompassed a wide range of methods, including lexicon-based, graph-based, network-based, machine learning, deep learning, ensemble-based, rule-based, and hybrid techniques. The paper also addresses the challenges and opportunities in SA, such as dealing with sarcasm and irony, analyzing multi-lingual data, and addressing ethical concerns. To provide a practical case study, Twitter was chosen as one of the largest online social media platforms. Furthermore, the researchers shed light on the diverse application areas of SA, including social media, healthcare, marketing, finance, and politics. The paper also presents a comparative and comprehensive analysis of existing trends and techniques, datasets, and evaluation metrics. The ultimate goal is to offer researchers and practitioners a systematic review of SA techniques, identify existing gaps, and suggest possible improvements. This study aims to enhance the efficiency and accuracy of SA processes, leading to smoother and error-free outcomes

Leveraging Twitter data to understand the dynamics of social media interactions on cryptocurrencies

Rapid technological change in the last decades has led to the emergence of new platforms and fields such as cryptocurrencies and social media data. Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide. The social media platform has millions of users where tweets are continuously shared every second. Therefore, tweets are useful when a large amount of data is generated to conduct a social media analysis. In addition, Twitter is broadly utilized by investors and financial analysts to gather valuable information. Several studies have shown that the content posted on Twitter can predict the movement of cryptocurrency prices. However, limited research has been conducted on the dynamics of Twitter interactions on cryptocurrencies among users. By leveraging 1724328 tweets, this research aims to understand the dynamics of social media users’ interactions on cryptocurrencies. Essentially by shedding light on larger cryptocurrencies contrary to smaller. The findings reveal that Twitter users are more positive than negative about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The thesis contributes to demonstrating the effectiveness of the Social set analysis framework to analyze and visualize a massive amount of social media data and user-generated data created on social media platforms such as Twitter

360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Analyzing the drivers of customer satisfaction via social media

Social media became a great influence force during the last decade. Active social media user population increased with the new generations. Thus, data started to accumulate in tremendous amounts. Data accumulated through social media offers an opportunity to reach valuable insights and support business decisions. The aim of this project is to understand the drivers of customer satisfaction by public sentiments on Twitter towards a financial institution. Data was extracted from the most popular microblogging platform Twitter and sentiment analysis was performed. The unstructured data was classified by their sentiments with a lexicon-based model and a machine learning based model. The outcome of this study showed machine learning based model successfully overcame the language specific problems and was able to make better predictions where lexicon-based model struggled. Further analysis was performed on the extreme daily average sentiment scores to match these days with prominent events. The results showed that the public sentiment on Twitter is driven by three main themes; complaints related to services, advertisement campaigns, and influencers’ impact.Sosyal medyanın etki alanı geçtiğimiz yıllarla birlikte giderek artmıştır. Yeni jenerasyonlarla birlikte aktif olarak sosyal medya kullanan nüfus artış göstermiştir. Bu sebeple büyük veri birikimi artmıştır. Sosyal medya üzerinden oluşan büyük veri şirketlerin iş yapış şekillerine yönelik değerli kavrayış ve karar alma mekanizmalarına destek fırsatları sunmaktadır. Bu çalışmanın amacı bir finansal kurumun müşterilerinin memnuniyet seviyelerini sosyal medyada oluşan algıyı kullanarak anlamaya çalışmaktır. Çalışma kapsamında kullanılan veri popüler mikro-blog sitesi Twitter üzerinden derlenmiştir. Yapılandırılmamış bu veri sözlük tabanlı ve makine öğrenmesi tabanlı iki model kullanılarak analiz edilmiştir. Çalışma sonucu makine öğrenmesi tabanlı modelin sözlük tabanlı modelin karşılaştığı Türkçe kaynaklı sorunlardan daha az etkilendiği ve daha başarılı tahminler üretebildiğini göstermiştir. Analizin sonraki aşamasında ortalama sonucu aşırı uçlarda çıkan günler aynı günlerde ortaya çıkan olaylar ile eşleştirilmiştir. Ortaya çıkan sonuçlara göre müşteri memnuniyeti sosyal medyada ortaya çıkan üç temel faktörden etkilenmektedir. Bunlar, şikâyet yönetimi, kampanya yönetimi ve sosyal medya fenomenlerinin etkisi olarak tanımlanmaktadır