Commenting on Political Topics Through Twitter: Is European Politics European?

The aim of this study was to explore social media, and specifically Twitter's potential to generate a European demos. Our use of data derived from social media complements the traditional use of mass media and survey data within existing studies. We selected two Twitter hashtags of European relevance, #schengen and #ttip, to test several theories on a European demos (non-demos, European democracy, or pan-European demos) and to determine which of these theories was most applicable in the case of Twitter topics of European relevance. To answer the research question, we performed sentiment analysis. Sentiment analysis performed on data gathered on social media platforms, such as Twitter, constitutes an alternative methodological approach to more formal surveys (e.g., Eurobarometer) and mass media content analysis. Three dimensions were coded: (1) sentiments toward the issue public, (2) sentiments toward the European Union (EU), and (3) the type of framing. Among all of the available algorithms for conducting sentiment analysis, integrated sentiment analysis (iSA), developed by the Blog of Voices at the University of Milan, was selected for the data analysis. This is a novel supervised algorithm that was specifically designed for analyses of social networks and the Web 2.0 sphere (Twitter, blogs, etc.), taking the abundance of noise within digital environments into consideration. An examination and discussion of the results shows that for these two hashtags, the results were more aligned with the demoicracy and "European lite identity" models than with the model of a pan-European demos

Sentimen Analisis Twitter Ibu Kota Negara Nusantara Menggunakan Long Short-Term Memory dan Lexicon Based

In the 2020 APJII Survey, Indonesians who use Twitter for social media are 10% of the entirety of social media users in Indonesia (APJII 2020), the issue that is being discussed a lot both on social media and offline discussions, is the National Capital City (IKN) of the Archipelago, which is the new capital city of the Republic of Indonesia. The relocation of the capital city raises pros and cons. With these pros and cons, an analysis of public sentiment regarding the IKN issue becomes a necessity. In this research, the model that will be used to analyze sentiment analysis uses the Long Short Term Memory (LSTM) algorithm and lexicon based on two scenarios, which is the scenario that uses 100 data of tweets and 5112 data of tweets. The results for the 100 tweets dataset scenario obtained 64% accuracy, 40% precision, 64% recall, and 79% F1-Score. Meanwhile, the results for the 5112 tweets data scenario obtained 79% accuracy, 82% precision, 79% recall, 79% F1-Score. The sentiment results obtained from the 5112 tweets data are 44.8% positive sentiment, 36.2% negative sentiment and 19.0% neutral sentiment. Based on this research, the number of datasets will affect the performance of deep learning models built using lexicon based and LSTM algorithms

Sensing Human Sentiment via Social Media Images: Methodologies and Applications

abstract: Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity. One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding. In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Abstract. Insights gained from social media platforms are pivotal for businesses to understand their products’ present position. While it is possible to use consulting services focusing on surveys about a product or brand, such methods may yield limited insights. By contrast, on social media, people frequently express their individual and unique feelings about products openly and informally. With this in mind, we aim to provide rigorous methodologies to enable businesses to gain significant insights on their brands and products in terms of representations on social media. This study employs conjoint analysis to lay the analytical groundwork for developing positive and negative sentiment frameworks to evaluate the brands of three prominent emerging automotive companies in Indonesia, anonymized as “HMI,” “YMI,” and “SMI.” We conducted a survey with a sample size of n=67 to analyze the phrasings of importance for our wording dictionary construction. A series of data processing operations were carried out, including the collection, capture, formatting, cleansing, and transformation of data. Our study’s findings indicate a distinct ranking of the most positively and negatively perceived companies among social media users. As a direct management-related implication, our proposed data analysis methods could assist the industry in applying the same rigor to evaluating companies’ products and brands directly from social media users’ perspective. Keywords:  Brand image, social media, data analytics, sentiment analysis, conjoint analysi

Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions

The interest in social media sentiment analysis and opinion mining for public security events has increased over the years. The availability of social media platforms for communication provides a valuable source of information for sentiment analysis and opinion mining research. The content shared across the media gives potential input to the physical environment and social phenomena related to public security threats. The input has been used to: monitor public security threats or emergency events, analyzing sentiment and opinionated data for threat management and the detection of public security threat events using geographic location-based sentiment analysis. However, a systematic survey that describes the trends and latest developments in this domain is unavailable. This paper presents a survey of social media sentiment analysis and opinion mining for public security. This paper aims to: understand the progress of the current state-of-the-art, identify the research gaps, and propose potential future directions. In total, 200 articles published from 2016 to 2023 were considered in this survey. The taxonomy shows the key attributes and limitations of the work presented in the surveyed articles. Subsequently, the potential future direction of work on sentiment analysis in the public security domain is suggested for interested researchers

Social media analytics for YouTube comments: potential and limitations

The need to elicit public opinion about predefined topics is widespread in the social sciences, government and business. Traditional survey-based methods are being partly replaced by social media data mining but their potential and limitations are poorly understood. This article investigates this issue by introducing and critically evaluating a systematic social media analytics strategy to gain insights about a topic from YouTube. The results of an investigation into sets of dance style videos show that it is possible to identify plausible patterns of subtopic difference, gender and sentiment. The analysis also points to the generic limitations of social media analytics that derive from their fundamentally exploratory multi-method nature

Opini Masyarakat Twitter terhadap Kandidat Bakal Calon Presiden Republik Indonesia Tahun 2024

Registration for the 2024 presidential candidates began at the end of 2023, but the euphoria of the supporters of the 2024 presidential candidates began to be felt from the beginning of 2022. Several survey institutions released public opinions regarding several prospective 2024 presidential candidates. One of the approaches taken in the survey was by conducting direct interviews with the public. However, political dynamics can change the results of political surveys at great expense. Public opinion about the 2024 presidential candidates cannot only be acquired through direct interviews. Public opinion acquisition can also be done through social media such as Twitter. This article aims to find out public opinion on the candidates for the 2024 presidential candidate on Twitter social media. This article uses a Twitter dataset and data analysis tools using orange data mining. The crawling dataset was carried out using the hashtags #capres2024 and #presiden2024 and the keywords anies baswedan, prabowo subianto and ganjar pranowo with 10,000 tweet data in content written in Indonesian. Text preprocessing includes transformation, tokenization, filtering and normalization applied to data before analysis is carried out with topic modeling and sentiment towards the presidential candidates. The results of the word cloud analysis show a very high level of popularity for candidate Ganjar Pranowo, but the results of the sentiment analysis show that Ganjar Pranowo has a negative sentiment

Effectiveness of Social Media Analytics on Detecting Service Quality Metrics in the U.S. Airline Industry

During the past few decades, social media has provided a number of online tools that allow people to discuss anything freely, with an increase in mobile connectivity. More and more consumers are sharing their opinions online with others. Electronic Word of Mouth (eWOM) is the virtual communication in use; it plays an important role in customers’ buying decisions. Customers can choose to complain or to compliment services or products on their social media platforms, rather than to complete the survey offered by the providers of those services. Compared with the traditional survey, or with the air travel customer report published by U.S. Department of Transportation (DOT) each month, social media offers features that can spread information quickly and broadly. This dissertation offers a novel methodology that, by utilizing emotional sentiment analysis, can help the airline industry to improve its service quality. Longitudinal data, retrieved from Twitter, are collected from twelve U.S.-based airline companies, in order to represent airline companies in different levels and categories. The data covers three consecutive months in Quarter 2 of 2017. Applied alongside the service quality metrics of the airline industry, the benchmark datasets for each metric are created. The purpose of this dissertation is to bridge the gap in traditional methodology for a service quality measurement in the airline industry and to demonstrate the way in which socialized textual data can measure the quality of the service offered by airline service providers. In addition, sentiment analysis is applied, in order to get the sentiment score of each tweet. Emotional lexicons are used to detect the emotion expressed by the tweet in two emotional dimensions: each tweet’s Valence and Arousal are calculated. Once the SERVQUAL model is applied and the keywords to find the corresponding social media data are created for each dimension, the results show that responsiveness, assurance, and reliability are positively correlated to the AQR score that measures the service quality of airline industry. This study also finds that a large amount of negative social media data will negatively affect the AQR score. Finally, this study finds that the interaction of the sentiment score and the arousal score of textual social media data play the important role in predicting the service quality of the airline industry. Finally, an opinion-oriented information system is proposed. In the last, this study provides theory verification of SERVQUAL

A Survey on Feature Extraction Techniques, Classification Methods and Applications of Sentiment Analysis

Abstract Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data. Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral, or negative. Also referred to as opinion mining or subjective mining, sentiment analysis has applications that range from marketing and customer service to clinical medicine. The application of sentiment analysis in the epoch of big data has proved invaluable in classifying sentiment and, in general, determining opinions from the average person’s frame of mind Several sentiment analysis techniques have been developed over the years. In this regard, this article presents a brief survey on the sentiment analysis applications, as well as feature extraction and sentiment classification techniques. This article surveys various feature extractions techniques and concludes that each technique has its own pros and cons, and can be combined for better results. The survey on classification methods suggests that hybrid methods provide finer results than individual ones. The survey of applications surmises that sentiment analysis as applied to different sectors, helps expand business opportunities. Also, the paper presents a few open challenges in carrying out sentiment analysis

Public Perception and Influential Sources Toward Child Welfare Services

The purpose of this study is to answer the following questions: “What is the current public attitude toward public child welfare services and what sources of information have been used to formulate these attitudes?” In answering these questions, child welfare agencies would have a better understanding of what populations to promote public outreach, education, or further community involvement based on demographics and/or which venues to implement such outreach. This study provides information on previous studies where researchers have looked at the general role of social work and used the gathered information to assess public sentiment. In past research there has been discrepancy in the outcomes of this data. Past research has also incorporated news media and the portrayal of child welfare social workers, but has not attempted to measure the impacts of media and the public’s perception of the profession. With recent societal events, it has become more evident that public perception can be a driving force in policy change. The intent of this study is to identify individual demographic information (e.g., race/ethnicity, income level, household size, prior child welfare system involvement, etc.) that would show a significant relationship with a developed scale to measure participants’ attitude or sentiment toward child welfare social work. To obtain participants, a link to the developed survey was posted to multiple social media pages where the primary subject included the Victor Valley region of San Bernardino County, California. Participants were also asked to repost the link to the survey to their social media pages in order to increase participant numbers. For this project, 183 participants completed the survey to completion. Due to the level of measurement of the variables, multiple data analysis techniques were used in order to identify relationships between the independent demographic variables and the score on the sentiment scale. These techniques include t-tests, ANOVA, and correlation. Of the variables measured for statistical significance, only the participants past levels of child welfare services involvement showed significance. This was especially true for participants who had experiences both as a minor and as a parent. Income level, news sources, and other demographic identifiers did not show statistically significant differences in sentiment toward child welfare social work. With the information from this study, child welfare agencies might implement further outreach to the identified populations in order to provide further support. This information can also identify which specific factors contribute to the negative perceptions through qualitative analysis