Feat: A Facebook Extraction And Analysis Toolkit

Social media usage has become mainstream. According to a recent study done by Edison Research in 2016, 78% of the U.S. population has a social media profile [8]. The number of active Facebook users is over one billion. In addition, 71% of adults use Facebook, which is the target of this thesis. Because Facebook is so widely used, it is also a popular medium for those wanting to promote their products and ideas, including presidential candidates. Many researchers have extracted data from social media sites, including Facebook, to predict the outcome of elections, to predict election turnout by political party, and to determine voter opinions. This thesis will discuss the development and use of a suite of tools for gathering and analyzing data collected from the social media site, Facebook. Although the suite of tools can be used to collect data from any public Facebook site, this thesis will specifically focus on using the tools to extract data from the pages of presidential candidates. In addition to extracting Facebook data and storing the data in a database, tools in the suite can be used to analyze and visualize the collected data

Towards Syntactic Iberian Polarity Classification

Lexicon-based methods using syntactic rules for polarity classification rely on parsers that are dependent on the language and on treebank guidelines. Thus, rules are also dependent and require adaptation, especially in multilingual scenarios. We tackle this challenge in the context of the Iberian Peninsula, releasing the first symbolic syntax-based Iberian system with rules shared across five official languages: Basque, Catalan, Galician, Portuguese and Spanish. The model is made available.Comment: 7 pages, 5 tables. Contribution to the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-2017) at EMNLP 201

Enhanced Topic-Based Modeling for Twitter Sentiment Analysis

abstract: In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models.Dissertation/ThesisMasters Thesis Computer Science 201

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods