I thank my parents, husband, and family for all support and encouragement for the realization of this dream. I thank also my friend, Richard McGill, for the support and review of papers related to this Thesis."Obstacles are those frightful things you see when you take your eyes off your goal."

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This work investigates information retrieval methods to address the existing difficulties on the Preliminary Search, part of the law making process from the Brazilian Chamber of Deputies. For such, different preprocessing approaches, stemmers, language models, and BM25 variants were compared. Two legislative corpora from Chamber were used to build and validate the pipeline. All texts were converted to lowercase and had stopwords, accentuation, and punctuation removed. Words were represented by their stem combined with word unigram and bigram language models. Retrieving the bill that was originated from a specific job request, the BM25L with Savoy stemmer reached a R@20 of 0.7356. After removing queries with inconsistencies or which made reference exclusively to attachments, to other job requests, or to bills, the R@20 increased to 0.94.

Abstract. The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. Opinion Mining (OM) is a sub-field of text mining in which the main task is to extract opinions from UGC. Given that Portuguese is one of the most common spoken languages in the world, and it is also the second most frequent on Twitter, the goal of this work is to plot the landscape of current studies that relates the application of OM for Portuguese. A systematic mapping review (SMR) method was applied to search, select and to extract data from the included studies. Manual and automated searches retrieved 6075 studies up to year 2014, from which 25 articles were included. Almost 70 % of all approaches focus on the Brazilian Portuguese variant. Naïve Bayes and Support Vector Machine were the main classifiers and SentiLex-PT was the most used lexical resource. Portugal and Brazil are the main contributors in processing the Portuguese language.

Abstract. The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. These electronic Word of Mouth statements expressed on the web are prevalent in business and service industry to enable a customer to share his/her point of view. However, it is impossible for humans to fully understand it in a reasonable amount of time. Opinion mining (also known as Sentiment Analysis) is a sub-field of text mining in which the main task is to extract opinions from UGC. Thus, this work presents an open source pipeline to analyze the costumer's opinion or sentiment in Twitter about products and services offered by Brazilian companies. The pipeline is based on General Architecture for Text Engineering (GATE) framework and the proposed hybrid method combines lexicon-based, supervised learning, and rulebased approaches. Case studies performed on Twitter real data achieved precision of almost 70 %.

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