A Review on Opinion Mining: Approaches, Practices and Application

Opinion Mining also known as Sentiment Analysis (SA) has recently become the focus of many researchers, because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms

Discourse polarization index: Analysis of top-down and ground-up political discourses in Portugal

An increasing number of events across the world have been a warning for democracy stability in established democratic countries. Events such as Hungary’s prime minister Viktor Orbán publicity doubting that liberal democracies could remain globally competitive, and the increasing voting share of anti-establishment parties in European member states are consequences of the political polarization phenomenon which endangers our democracy. To understand if we are becoming more polarized, literature has been focusing on measuring political polarization through surveys and voting data, without consistent evidence for any trend. Although the theoretical definition of political polarization has found stability in the literature, the different forms of measuring it have not. The measurement of political polarization needs to be more robust and extended to mass society besides elite society, enabling a comparison between the two, and within the real life and the digital. This dissertation answers this need, measuring political polarization, using text-as-data and computational social science methods, in an effective way independent of manual tasks, language, survey or pooling, polarization’s actors, and environments. It uses an empirical framework applied to parliamentary discourses and Twitter data to create a Discourse Polarization Index (DPI) which enables the assessment of the evolution of political polarization in discourse, considering its state and process. Portugal is used as use case, showing an increase in political polarization from 2015 to 2021, for the elite and the mass society, with similar behaviour between the two groups. A semantic validation is done, and research future steps are given.Diversos acontecimentos mundiais põem em causa a estabilidade democrática nos países democráticos. Destacando-se o comentário do primeiro-ministro húngaro, Viktor Orban, que declarou que as democracias atuais podem não ser competitivas globalmente, justificando a inclinação por uma autocracia, assim como o número crescente de partidos antissistema na Europa ocidental. Ambos os eventos são consequência da polarização política, um fenómeno que tem vindo a pôr em risco as democracias ocidentais. Para perceber a tendência, a literatura tem-se focado na medição quantitativa da polarização política através de questionários e sondagens, sem nenhuma tendência identificada. A quantificação da polarização política precisa de ser mais robusta e estudar também a polarização da massa publica, para além da elite, sendo possível assim a comparação da polarização entre os dois grupos, mas também entre os ambientes em que interagem, na vida real ou no digital. Esta dissertação responde a essa necessidade, medindo a polarização política, usando texto e métodos de ciências sociais computacionais, independente da língua, dos questionários, das sondagens e de tarefas manuais. A dissertação usa um modelo matemático empírico aplicado ao discurso parlamentar e a dados retirados do Twitter para criar o Índice de Polarização no Discurso. Este índice permite avaliar a evolução da polarização no discurso, considerando as suas características de estado e processo. Portugal é usado como caso de estudo, mostrando um aumento da polarização política entre 2015 e 2021, para a elite e massa pública, com comportamentos semelhantes. É efetuada uma validação semântica e são dadas recomendações para próximos passos de investigação

Using Machine Learning to Measure Political Polarization on Social Media

Polarization in the political sphere, seen through combative communication and stalemate, may impose negative social impacts on the population. Attempting to measure political polarization in the masses through self-reported surveys and interviews can present response biases of social desirability. The classification of thought freely written online allows political polarization to be measured in an impartial manner. Reddit is one application that enables users to share opinions and create discussions anonymously; this text can be used to measure the political climate at any given time. Disagreement has grown over the perceived level of polarization in our society. The purpose of my research is to measure the degree of political polarization over time by collecting and classifying threads of dialogue within political communities on Reddit. I utilized Reddit APIs to gather threads of text from multiple subreddit communities online. I recruited a team of evaluators to hand-annotate the threads for polarization. Then, I created a machine learning classifier to predict whether a thread posted on Reddit is polarized or not. I trained a multilayer perceptron on the hand-tagged data. Depending on parameter choices, the classifier performed with an accuracy of between 75% and 80% on an independent test set. There does not appear to be a strong pattern indicating a rise in polarization overall on Reddit during the period 2008 to the present. However, when looking at some of the subreddits individually, there is evidence of an increase in polarization

A quantitative research on ideological and political polarization on social media

İnternet kullanıcılara yer ve zamandan bağımsız olarak sınırsız veriye ve enformasyona kolayca erişebilecekleri bir ortam sağlamıştır. Ancak özellikle sosyal medya mecralarının yaygınlaşması ile algoritmik filtreleme faktörleri ve kullanıcıların kendine benzer özelliklerde olan kullanıcılarla etkileşimde bulunma durumu bir kullanıcının tükettiği içeriğin genişliğini daraltmıştır. Bu durum sosyal medyadaki kullanıcıların yalnızca kendi inanç ve görüşleriyle benzer özelliklerde olan içerikleri tükettiği kısır bir döngüye yol açmaktadır. Bu çalışma, sosyal medya mecralarını oluşturan algoritmaların filtreleme faktörlerinden bağımsız olarak kullanıcıların sosyal medya kullanma pratiklerinin ideolojik ve siyasi kutuplaşma üzerindeki etkilerini incelemektedir. Araştırmada basit tesadüfi örnekleme yöntemiyle 770 katılımcı belirlenmiş ve anket soruları katılımcılara çevrim içi araçlarla ulaştırılmıştır. Araştırma sonucu elde edilen bulgular katılımcıların büyük çoğunluğunun farklı ideolojik ve siyasi görüşe sahip diğer sıradan kullanıcıları takip etmediklerini ve bu kullanıcılarla etkileşimde bulunmadıklarını ortaya koymuştur. Benzer şekilde katılımcıların çoğunluğunun karşıt ideolojik görüşteki gazetecileri ve siyasi liderleri takip etmedikleri ve bu kişilerin sosyal medya paylaşımlarıyla etkileşimde bulunmadıkları bulgusuna ulaşılmıştır. Sonuç olarak filtre balonlarının etkisiyle kendi ideolojik ve siyasi düşüncesine benzer içerikler sunulan kullanıcılar, bilinçli olarak farklı görüşlerin önüne set çekmekte ve kendilerini gönüllü olarak yankı odalarına hapsetmektedir.The Internet has provided users with an environment where they can easily access unlimited data and information regardless of place and time. However, especially with the spread of social media channels, algorithmic filtering factors and the tendency of users to interact with other users with similar characteristics have narrowed the limit of the content consumed by a user. This leads to a vicious cycle in which users on social media only consume content that has similar characteristics to their own beliefs and opinions. This study examines the effects of users’ social media usage practices on ideological and political polarization in social media channels, regardless of the filtering factors of algorithms. In the study, 770 participants were determined by a simple random sampling method and the survey questions were delivered to the participants via online tools. According to the results of the research, it has been revealed that the majority of the participants do not follow users with different ideological and political views on social media and do not interact with these users. In addition, it was observed that the majority of the participants do not follow journalists and political leaders with opposing ideological and political views and do not interact with their posts. As a result, users, who are presented with content similar to their own ideological and political thoughts with the effect of filter bubbles, are also consciously blocking different opinions and voluntarily imprisoning themselves in echo chambers

Analyzing Polarization on Social Media: A Case Study of the 2022 Brazil Presidential Election

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceSocial Media has become a big part of our society and has now a significant role in the relationships between inter and intra-communities. Twitter is now an important communication platform for political campaigns: in the last years, politicians, campaigners, and general users have been extensively using Twitter to promote campaigns and engage in political discussions. Some studies argue that social media can create filter bubbles by limiting the flow of online information, and therefore creating communities where exposure to political diversity is rare. This selective exposure can build echo chambers where individuals only interact with those who have the same opinions as they have and by doing that, they build a polarized community. Identifying, understanding, and mitigating polarization is very important for the democratic process. People should be exposed to different ideas and opinions so they can choose their representatives without being influenced by some portion of the information. This project analyzed political polarization on social media using data from Twitter. Brazil’s presidential election in 2022 was used as a case study. Tweets from the two main candidates were extracted. A Topic Modeling algorithm was used to cluster tweets in topics. An Engagement Graph was built based on the interactions between users, candidates, and topics and was used to compute the Topic Centrality measures. A pre-trained Sentiment Analysis model was used to measure the sentiment polarity of each tweet. In the end, the project analyzed the extracted features and identified which topics were more central to each candidate and how users interact with them. The major conclusion of this work is that polarization in Brazil is more affective than ideological since the user’s sentiments towards topics are not as relevant as the sentiments towards the candidates

Miradas comunicacionales de la polarización política en entornos digitales, una revisión bibliográfica

Social polarization, understood as a phenomenon of division in societies around extreme positions of various kinds, has been deepening in the political sphere for some decades and is used as a strategy in electoral campaigns in order to generate divisions that benefit an ideological position or a particular candidate. La polarización social, entendida como un fenómeno de división en las sociedades en torno a posiciones extremas de diversa índole, se ha ido profundizando en el ámbito político desde hace algunas décadas y es utilizada como estrategia en las campañas electorales con el fin de generar fraccionamientos que beneficien a una postura ideológica o a un candidato en particular. A polarização social, entendida como um fenômeno de divisão das sociedades em torno de posições extremas de diversos tipos, vem se aprofundando na esfera política há algumas décadas e é utilizada como estratégia em campanhas eleitorais para gerar divisões que beneficiem uma posição ideológica ou um determinado candidato

Перспективы развития фундаментальных наук. Т. 7 : IT-технологии и электроника

Сборник содержит труды участников XVIII Международной конференции студентов, аспирантов и молодых учёных «Перспективы развития фундаментальных наук», представленные на секции «IT-технологии и электроника». Для студентов, аспирантов, молодых ученых и преподавателей, специализирующихся в области интеллектуальных систем управления, автоматизированных систем обработки информации и управления, информационной безопасности, наноэлектроники, получения и исследования наноматериалов, оптоэлектроники и нанофотоники, плазменной эмиссионной электроники, интеллектуальной силовой электроники, СВЧэлектроники, систем радиолокации, телевидения, радиосвязи, радиометрии и распространения волн радиочастотного и акустического диапазонов, а также импульсных и радиочастотных измерениях