Quantising opinions for political tweets analysis

There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world

Opinion Mining Using Twitter Feeds for Political Analysis

Sentiment analysis deals with identifying and understanding opinions and sentiments expressed in a particular text. The masses give their opinion regarding various subjects on social media platforms using tweets, status updates and blogs. By analyzing this very data, we can gain better insight of the public opinion on any subject in specific. On performing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters allowed in Twitter is 140. In this paper, we try to analyze the twitter posts about government issues and political reforms. The proposed framework uses Twitter as the platform to analyze the emotions of the users using Sentiment Analysis. The system will use the opinions of the users, analyze the reaction and then map it to the appropriate region

Predicción de tendencia política por Twitter: Elecciones Andaluzas 2012

Este trabajo investiga cómo los políticos y los principales partidos han utilizado Twitter durante las elecciones autonómicas andaluzas de 2012. Hemos seguido a los seis partidos políticos más importantes de dicha comunidad (PP, PSOE-A, IU LV-CA, UPyD, PA y eQuo), así como a los líderes de dichos partidos y hemos analizado sus tweets, el flujo de los mismos, las respuestas recibidas y el factor de impacto de sus mensajes. Los resultados de nuestro estudio muestran cómo por medio de Twitter se pueden predecir los sentimientos y las tendencias políticas de una comunidad determinada.Our research deepens on the way main politicians and parties made use of Twitter during Andalusian regional election, 2012. In order to do so, we followed the six main political options for such region (PP, PSOE-A, IU LV-CA, UPyD, PA and eQuo), as well as their leaders, analyzing the contents and flow of their tweets, the answers they received, and the impact factor of their messages. The results of this paper show how Twitter can be used to predict sentiment and political trends within a given community

Neutral Isn’t Neutral: An Analysis of Misinformation and Sentiment in the Wake of the Capitol Riots

January 6th, 2021 was a significant moment in the history of the United States of America. Protestors stormed the Capitol building over the results of the 2020 presidential election in which Joseph R. Biden defeated incumbent president Donald J. Trump. The Capitol riots were partially incited by the presence of misinformation on social media and was an example of the power misinformation has. This study presented two questions. Question one pertains to the sentiment analysis of verified Twitter users and their sentiment towards Trump. Question two pertains to analyzing tweets from verified accounts for misinformation between the dates of January 6th, 2021 and January 13th, 2021. To answer these questions, a machine learning sentiment analysis was conducted on 13 randomly selected Twitter accounts with noted liberal and conservative political leanings to assess their sentiment towards Trump. The accounts were analyzed and then categorized as being either anti-Trump or Trump-neutral. Once the accounts were appropriately categorized a collection of their tweets mentioning Trump were documented to create a consecutive day sample to examine their reporting and analyze how misinformation differed between the two. The results of this study show that one, sentiment analysis is a useful tool for examining and categorizing tweets and their overall accounts based on their sentiments and two, that there was a notable difference in the spread of misinformation between the two categories

Electoral Predictions with Twitter: A Machine-Learning approach

Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art.Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art