SENTIDA: A New Tool for Sentiment Analysis in Danish

In the midst of the Era of Big Data, tools for analysing and processing unstructured data are needed more than ever. Being among these, sentiment analysis has experienced both a substantial proliferation in popularity and major developmental progress. However, the development of sentiment analysis tools in Danish has not experienced the same rapid development as e.g. English tools. Few Danish tools exist, and often the ones available are either ineffective or outdated. Moreover, authoritative validation tests in low-resource languages, are missing, which is why little can be deduced about the competence of current Danish models. We present SENTIDA, a simple and effective model for general sentiment analysis in Danish, and compare its competence to the current benchmark within the field of Danish sentiment analysis, AFINN. Combining a lexical approach with several incorporated functions, we construct SENTIDA and categorise it as a domain-independent sentiment analysis tool focusing on polarity strength. Subsequently, we run different validation tests, including a binary classification test of Trustpilot reviews and a correlation test based on manually rated texts from different domains. The results show that SENTIDA excels across all tests, predicting reviews with an accuracy above 80% in all trials and providing significant correlations with manually annotated texts

You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP

Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this information in a static way, treating all connections uniformly. This ignores the fact, well known in sociolinguistics, that an individual may be part of several communities which are not equally relevant in all communicative situations. We present a model based on Graph Attention Networks that captures this observation. It dynamically explores the social graph of a user, computes a user representation given the most relevant connections for a target task, and combines it with linguistic information to make a prediction. We apply our model to three different tasks, evaluate it against alternative models, and analyse the results extensively, showing that it significantly outperforms other current methods.Comment: To appear in Proceeding of EMNLP 201

Social Media Analytics in Disaster Response: A Comprehensive Review

Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems

In recent years, social media has become one of the most popular platforms for communication. These platforms allow users to report real-world incidents that might swiftly and widely circulate throughout the whole social network. A social event is a real-world incident that is documented on social media. Social gatherings could contain vital documentation of crisis scenarios. Monitoring and analyzing this rich content can produce information that is extraordinarily valuable and help people and organizations learn how to take action. In this paper, a survey on the potential benefits and applications of event detection with social media data analysis will be presented. Moreover, the critical challenges and the fundamental tradeoffs in event detection will be methodically investigated by monitoring social media stream. Then, fundamental open questions and possible research directions will be introduced

Sentiment Analysis in Unstructured Textual Information with Deep Learning

This document analyses the current State-of-the-Art algorithms in the fields of Natural Language Processing and Sentiment Analysis. It continues with a step-by-step explication of the development process of pre-processing techniques and neural networks architectures that allow to perform sentiment predictions (predicting rating stars) on Amazon.com customer reviews. An accuracy comparison has been made between 4 different models to check their performance. The second part of the project has been the development of a demo web application to show the potential of a Product Analytics Tool, which allows to perform sentiment predictions of any product on Amazon website. This app scrapes the reviews, loads the previously trained model and makes the predictions, generating different insights such as the most positive and negative features of the product based exclusively on the most reliable and objective data, customer reviews. The source code of the app can be found here: https://github.com/albergar2/SA_Project At the end of the document an appendix has been added providing information and estimates of the cost and tasks required to replicate this project in a professional environment.Doble Grado en Ingeniería Informática y Administración de Empresa