Suchergebnisse

Research on electoral events in conjunction with social media provides opportunities to describe an interesting phenomenon that can be analyzed using sentiment analysis techniques. The goal of the study is to analyze the support of political parties during electoral periods from Twitter comments including 250,000 tweets regarding the Spanish general elections of 2015 and 2016. Text mining and natural language processing techniques enable information analysis, and the methodology emphasizes good practices for large-scale data collection retrieved from Twitter through a quantitative analysis of text collection written in the Spanish language. After information extraction obtained in three Spanish regions, defined by geolocation, as well as feature selection based on keywords of the main four political parties, we conducted an in-depth examination of Twitter users´ support during the course of the election. By weighting the tendency of tweets, we were able to obtain a proposed indicator of support: the positiveness ratio (PR). The results suggest that the PR is a feasible barometer to demonstrate measurable patterns of support tendency regarding political parties and the users' behavioral activity to track their affinity on Twitter. Findings indicate a consistent support behavior by users towards traditional parties and an optimistic users' behavior regarding the emerging political parties. ; OBS: Tweets were collected through Twitter Scraper: https://github.com/taspinar/twitterscraper/