This project conducts a computational text analysis of 4,000 tweets concerning the 2017 Catalonian independence movement. Using sentiment analysis and text mining techniques, we examine the emotional polarity, thematic framing, and dissemination patterns within this polarized online discourse. The primary objectives are to quantify the sentiment distribution, identify dominant narratives through hashtag analysis, and investigate whether message sentiment influenced its virality via retweets. This analysis aims to reveal how conflict-driven political debates manifest linguistically on social media, bridging lexical patterns with observable social behavior in digital public spheres.

Introduction This project applies text clustering techniques to a collection of documents written by two different authors with distinct interests. The aim is to automatically group the texts into meaningful categories or topics without prior knowledge of their authorship. Using natural language processing and unsupervised learning methods in R—specifically TF-IDF vectorization and k-means clustering—we analyze word usage patterns to uncover underlying themes and stylistic differences between the authors.

This document presents Text Mining Exercise 2, including text preprocessing, document-term matrix creation, clustering, and interpretation of industry-related themes.

This document contains the first exercise in Text Mining, focusing on: - Regular expressions - Text cleaning - Preprocessing tasks using the stringr package

This homework focuses on applying association rule mining to the European Social Survey (ESS) Round 11 dataset. The goal is to uncover meaningful patterns and relationships between variables, particularly focusing on the variable “Feeling about household’s income nowadays” (hincfel) as the rule consequent. By using data from a wide geographical range, the analysis aims to identify key factors influencing household income perceptions, such as economic satisfaction, employment stability, and personal happiness.

This study aims to investigate the relationships between different food preferences using association rule mining. Association rule mining is a data mining technique that discovers interesting relationships or dependencies among items within a dataset. By analyzing the “Food Preferences” dataset available on Kaggle, I aim to uncover hidden patterns and associations between different food categories.

Customer Personality Analysis is all about getting to know a company’s ideal customers on a deeper level. By understanding who they are, what they like, and how they behave, businesses can better tailor their products to fit the unique needs of different customer groups.