The effects of twitter sentiment on renewable energy stock's returns : a Portuguese study about EDP renováveis stocks

Investors’ rationality in the decision-making process has been topic of discussion in the last decades due to conflicts between schools of thought. Several anomalies in the Efficient Market Hypothesis (EMH) led to a new line of thought in the matter of rationality called behavior finance. Sentiment analysis is one branch of this new school of thought who studies investors’ emotions influence on economic variables. There is no consensus between academics if these emotions can make the investment decision biased or not. The aim of this paper is to observe if the prevailing sentiment in tweets can predict the stock returns for a renewable energy company of the Portuguese market. This study looks at the second biggest company by capitalizations of the Portuguese market, EDP Renováveis (EDPR), in the period from the June 1st 2021, to June 1st 2022, and finds no significant evidence of a relationship between Twitter mood and EDP Renováveis stock returns. The reasons for this result might be explained by EDPR belonging to a very small and concentrated market, corroborating the existing theory, as well as the stakeholder composition of the company only having a very small percentage of individual investors, being this kind of investors the most influenced by biases and heuristics present in the tweets. These findings have implications for the development of the sentiment analysis theory, giving more details of the influence of sentiment in smaller and concentrated market, in the renewable energy branch, and in the period of the beginning of the war between Ukraine and Russia and the worldwide economic recovery from the Covid-19 pandemic.A racionalidade dos investidores no processo de decisão de investimento tem sido tópico de discussão nas últimas décadas devido ao conflito entre duas linhas de pensamento diferentes. Várias anomalias que não iam de encontro com a hipótese do mercado eficiente deram origem a uma nova escola de pensamento em relação à racionalidade dos investidores chamada de finanças comportamentais. Análise de sentimentos é um dos ramos desta nova linha de pensamento que estuda a influência das emoções dos investidores em diferentes variáveis económicas. Não existe consenso entre académicos se estas emoções conseguem enviesar as decisões de investimento ou não. O objetivo desta tese é observar se o sentimento presente em tweets consegue fazer prever os retornos das ações de uma empresa de energias renováveis do mercado português. Este estudo analisa a segunda maior empresa portuguesa por capitalizações, a EDP Renováveis (EDPR), no período temporal entre o dia 1 de junho de 2021 e o dia 1 de julho de 2022, e não encontrou evidência com significância de uma relação entre o estado de espírito do Twitter e os retornos das ações da EDP Renováveis. As razões que justificam estes resultados podem ser o facto da EDPR pertencer a um mercado muito pequeno e concentrado como o português, indo de encontro com a evidência empírica, assim como a composição dos proprietários das ações da empresa ter uma percentagem muito reduzida de investidores individuais, que são o tipo de investidor mais facilmente influenciado por heurísticas presentes nos tweets. Este resultado tem implicações para o desenvolvimento da teoria de análise do sentimento, dando mais detalhes da influência deste em mercados mais pequenos e concentrados, no ramo das energias Renováveis, no período de tempo do início da guerra entre a Ucrânia e a Rússia e a recuperação financeira mundial pós-Covid-19

Spartan Daily, September 30, 2014

Volume 143, Issue 14https://scholarworks.sjsu.edu/spartandaily/1513/thumbnail.jp

Digital neighborhoods

With the advent of ‘big data’ there is an increased interest in using social media to describe city dynamics. This paper employs geo-located social media data to identify ‘digital neighborhoods’ – those areas in the city where social media is used more often. Starting with geo-located Twitter and Foursquare data for the New York City region in 2014, we applied spatial clustering techniques to detect significant groupings or ‘neighborhoods’ where social media use is high or low. The results show that beyond the business districts, digital neighborhoods occur in communities undergoing shifting socio-demographics. Neighborhoods that are not digitally oriented tend to have higher proportion of minorities and lower incomes, highlighting a social–economic divide in how social media is used in the city. Understanding the differences in these neighborhoods can help city planners interested in generating economic development proposals, civic engagement strategies, and urban design ideas that target these areas

Identifying Purpose Behind Electoral Tweets

Tweets pertaining to a single event, such as a national election, can number in the hundreds of millions. Automatically analyzing them is beneficial in many downstream natural language applications such as question answering and summarization. In this paper, we propose a new task: identifying the purpose behind electoral tweets--why do people post election-oriented tweets? We show that identifying purpose is correlated with the related phenomenon of sentiment and emotion detection, but yet significantly different. Detecting purpose has a number of applications including detecting the mood of the electorate, estimating the popularity of policies, identifying key issues of contention, and predicting the course of events. We create a large dataset of electoral tweets and annotate a few thousand tweets for purpose. We develop a system that automatically classifies electoral tweets as per their purpose, obtaining an accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class task (both accuracies well above the most-frequent-class baseline). Finally, we show that resources developed for emotion detection are also helpful for detecting purpose

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data

The meteoric rise in the popularity of public engagement platforms such as social media, customer review websites, and public input solicitation efforts strives for establishing an inclusive environment for the public to share their thoughts, ideas, opinions, and experiences. Many decisions made at a personal, local, or national scale are often fueled by data generated by the public. As such, inclusive collection, analysis, sensemaking, and utilization of pubic-generated data are crucial to support the exercise of successful decision-making processes. However, people often struggle to engage, participate, and share their opinions due to inaccessibility, the rigidity of traditional public engagement methods, and the lack of options to provide opinions while avoiding potential confrontations. Concurrently, data analysts and decision-makers grapple with the challenges of analyzing, sensemaking, and making informed decisions based on public-generated data, which includes high dimensionality, ambiguity present in human language, and a lack of tools and techniques catered to their needs. Novel technological interventions are therefore necessary to enable the public to share their input without barriers and allow decision-makers to capture, forage, peruse, and sublimate public-generated data into concrete and actionable insights. The goal of this dissertation is to demonstrate how human-centered approaches involve the stakeholders in the design, development, and evaluation of tools and techniques that can lead to inclusive, effective, and efficient approaches to public-generated data collection and analysis to support informed decision-making. To that end, in this dissertation, I first addressed the challenges of empowering the public to share their opinions by exploring two major opinion-sharing avenues --- social media and public consultation. To learn more about people\u27s social media experiences and challenges, I built two technology probes and conducted a qualitative exploratory study with 16 participants. This study is followed up by exploring the challenges of inclusive participation during public consultations such as town halls. Based on a formative study with 66 participants and 20 organizers, I designed and developed CommunityClick to enable reticent share their opinions silently and anonymously during town halls. Equipped with the knowledge and experiences from these works, I designed, developed, and evaluated technologies and methods to facilitate and accelerate informed data-driven decision-making based on increased public-generated data. Based on interviews with 14 analysts and decision-makers in the civic domain, I built a visual analytics system CommunityClick that can facilitate public input analysis by surfacing hidden insights, people\u27s reflections, and priorities. Leveraging the lessons learned during this work, I created a visual text analytics system that supports serendipitous discovery and balanced analysis of textual data to help make informed decisions. In this work, I contribute an understanding of how people collect and analyze public-generated data to fuel their decisions when they have increased exposure to alternative avenues for opinion-sharing. Through a series of human-centered studies, I highlight the challenges that inhibit inclusivity in opinion sharing and shortcomings of existing methods that prevent decision-makers to account for comprehensive public input that includes marginalized or unpopular opinions. To address these challenges, I designed, developed, and evaluated a collection of interactive systems including CommunityClick, CommunityPulse, and Serendyze. Through a rigorous set of evaluation strategies which include creativity sessions, controlled lab studies, in-the-wild deployment, and field experiments, I involved stakeholders to assess the effectiveness and utility of the built systems. Through the empirical evidence from these studies, I demonstrate how alternative designs for social media could enhance people\u27s social media experiences and enable them to make new connections with others to share opinions. In addition, I show how CommunityClick can be utilized to enable reticent attendees during public consultation to share their opinions while avoiding unwanted confrontation and allowing organizers to capture and account for silent feedback. I highlight how CommunityPulse allowed analysts and decision-makers to examine public input from multiple angles for an accelerated analysis and more informed decision-making. Furthermore, I demonstrate how supporting serendipitous discovery and balanced analysis using Serendyze can lead to more informed data-driven decision-making. I conclude the dissertation with a discussion on future avenues to expand this research including the facilitation of multi-user collaborative analysis, integration of multi-modal signals in the analysis of public-generated data, and potential adoption strategies for decision-support systems designed for inclusive collection and analysis of public-generated data

Service quality monitoring in confined spaces through mining Twitter data

Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel fine-tuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches