Esta Tese tem como objetivo geral inserir a Análise de Sentimentos na gestão das Cidades Inteligentes, possibilitando a implementação de uma ferramenta que disponibilize informações que auxiliem na supervisão e gestão dessas cidades. Dentre os possíveis auxílios que podem ser prestados está a identificação de ações, meios de prevenção e predição de possíveis adversidades nos diversos Domínios de Interesse, além da busca por melhorias na qualidade vida da população, que pode ser feita por meio dessa análise, permitindo que os gestores dessas cidades possam tomar as melhores decisões de acordo com cada cenário. Este trabalho contribui com um novo método cujo o objetivo é o desenvolvimento de um Sistema de Análise de Sentimentos para Auxílio na Gestão das Cidades Inteligentes (ASCI). Esse Sistema é capaz de captar, tratar, processar, filtrar por Domínio de Interesse e avaliar os sentimentos contidos nas informações provenientes dos cidadãos de uma Cidade Inteligente. O método utiliza duas Fases de Mineração de Dados, uma para a classificação dos Domínios de Interesse e outra para a Análise de Sentimentos. Para o estudo de caso foi implementado o método ASCI por meio do qual são captadas informações provenientes da população de uma determinada região da cidade de São Paulo, por meio da Rede Social Twitter. Também foi realizado um estudo de classificação de sentimentos no Domínio específico do Transporte, no qual também foram utilizados, e tiveram seu desempenho avaliado, os classificadores do tipo Linear SVC, Logistic Regression, Multinomial Naive Bayes e Random Forest Classifier para identificar os sentimentos positivos, neutros e negativos dos tweets captados. Os dados foram avaliados usando duas técnicas de extração de características de texto: Bag of Words e TF-IDF. O método ASCI desenvolvido nesta Tese contribui de maneira relevante para a área de Análise de Sentimentos, uma vez que os resultados obtidos foram satisfatórios quando aplicado em cenários de Domínios de Interesse das Cidades Inteligentes. / The main objective of this work is to insert the Sentiment Analysis in the management of Smart Cities, enabling the implementation of a supervision and management tool in these cities. Among the possible aid services that can be applied, there is the identification of actions, ways of prevention and prediction of possible adversities in the various Domains of Interest, and also the search for improvements in the quality of life of the population. This can be done through this analysis, allowing the best decisions according to each scenario by the city managers. This work contributes to a new method whose objective is the development of a Sentiment Analysis System to Assist in the Management of Smart Cities (ASCI). This System is capable of capturing, classifying, processing, filtering by Domain of Interest and evaluating the sentiments of Smart City citizens. The method uses two Data Mining phases, one for the classification of Domains of Interest and the other for Sentiment Analysis. For the case study, the ASCI method was implemented, through which information was collected from a regional population in São Paulo city through Twitter Social Network data. A study of Sentiment Analysis in specific Domain of Interest Transport was also carried out, in which Linear SVC, Logistic Regression, Multinomial Naive Bayes and Random Forest classifiers were used to identify the positive, neutral and negative sentiments of collected tweets. The data were evaluated using two techniques of extraction of text characteristics: Bag of Words and TF-IDF. The ASCI method developed in this Thesis contributes significantly to the area of Sentiment Analysis and the results obtained were satisfactory when applied in Smart City Domain of Interest scenarios.

Social media offers a powerful outlet for people's thoughts and feelings -- it is an enormous ever-growing source of texts ranging from everyday observations to involved discussions. This thesis contributes to the field of sentiment analysis, which aims to extract emotions and opinions from text. A basic goal is to classify text as expressing either positive or negative emotion. Sentiment classifiers have been built for social media text such as product reviews, blog posts, and even Twitter messages. With increasing complexity of text sources and topics, it is time to re-examine the standard sentiment extraction approaches, and possibly to re-define and enrich sentiment definition. Thus, this thesis begins by introducing a rich multi-dimensional model based on Affect Control Theory and showing its usefulness in sentiment classification. Next, unlike sentiment analysis research to date, we examine sentiment expression and polarity classification within and across various social media streams by building topical datasets. When comparing Twitter, reviews, and blogs on consumer product topics, we show that it is possible, and sometimes even beneficial, to train sentiment classifiers on text sources which are different from the target text. This is not the case, however, when we compare political discussion in YouTube comments to Twitter posts, demonstrating the difficulty of political sentiment classification. We further show that neither discussion volume or sentiment expressed in these streams correspond well to national polls, putting in question recent research linking the two. The complexity of political discussion also calls for a more specific re-definition of "sentiment" as agreement with the author's political stance. We conclude that sentiment must be defined, and tools for its analysis designed, within a larger framework of human interaction.

With a growing amount of subjective content distributed across the Web, there is a need for a domain-independent information retrieval system that would support ad hoc retrieval of documents expressing opinions on a specific topic of the user’s query. While the research area of opinion detection and sentiment analysis has received much attention in the recent years, little research has been done on identifying subjective content targeted at a specific topic, i.e. expressing topical opinion. This thesis presents a novel method for ad hoc retrieval of documents which contain subjective content on the topic of the query. Documents are ranked by the likelihood each document expresses an opinion on a query term, approximated as the likelihood any occurrence of the query term is modified by a subjective adjective. Domain-independent user-based evaluation of the proposed methods was conducted, and shows statistically significant gains over Google ranking as the baseline.

With a growing amount of subjective content distributed across the Web, there is a need for a domain-independent information retrieval system that would support ad hoc retrieval of documents expressing opinions on a specific topic of the user’s query. While the research area of opinion detection and sentiment analysis has received much attention in the recent years, little research has been done on identifying subjective content targeted at a specific topic, i.e. expressing topical opinion. This thesis presents a novel method for ad hoc retrieval of documents which contain subjective content on the topic of the query. Documents are ranked by the likelihood each document expresses an opinion on a query term, approximated as the likelihood any occurrence of the query term is modified by a subjective adjective. Domain-independent user-based evaluation of the proposed methods was conducted, and shows statistically significant gains over Google ranking as the baseline.

Sentiment analysis is the automatic classification of a text, trying to determine the attitude of the writer with respect to a specific topic. The attitude may be either their judgment or evaluation, their feelings or the intended emotional communication. The recent increase in the use of review sites and blogs, has made a great amount of subjective data available. Nowadays, it is nearly impossible to manually process all the relevant data available, and as a consequence, the importance given to the automatic classification of unformatted data, has increased. Up to date, all of the research carried on sentiment analysis was focused on English language. In this thesis, two Turkish datasets tagged with sentiment information is introduced and existing methods for English are applied on these datasets. This thesis also suggests new methods for Turkish sentiment analysis.

No description available.

There is high demand for computational tools that can automatically label tweets (Twitter messages) as having positive or negative sentiment, but great effort and expense would be required to build a large enough hand-labeled training corpus on which to apply standard machine learning techniques. Going beyond current keyword-based heuristic techniques, this paper uses emoticons (e.g. ':)' and ':(') to collect a large training set with noisy labels using little human intervention and trains a Maximum Entropy classifier on that training set. Results on two hand-labeled test corpora are compared to various baselines and a keyword-based heuristic approach, with the machine learned classifier significantly outperforming both. / text

Pastaruosius keletą metų, žmonėms vis aktyviau pradėjus reikšti savo požiūrį, įsitikinimus ir potyrius internete, susiformavo nauja tyrinėjimų sritis, kuri apima nuomonių gavybą ir sentimentų analizę. Šios srities tyrinėjimus aktyviai skatina ir jais domisi įvairios verslo kompanijos, matančios didelį, dėka nuolat tobulėjančių rezultatų, praktinį potencialą. Šis darbas skirtas apžvelgti teorinius bei praktinius nuomonės gavybos ir sentimentų analizės rezultatus bei realizuoti prototipinę nuomonės analizės sistemą, skirtą tyrinėti trumpus komentarus, parašytus lietuvių kalba. Taip pat darbe aprašomos problemos, susijusios su lietuvių kalbos taikymu nuomonės gavybos ir sentimentų analizės sistemų veikloje. Galiausiai, baigiamojoje dalyje suformuluojami ir išdėstomi rekomendacinio pobūdžio etapai, skirti nuomonės analizės sistemų kūrimui bei tobulinimui. / In past few years, more and more people started to express their views, beliefs and experiences on the Internet. This caused the emergence of a new research field, which includes opinion mining and sentiment analysis. Various business companies are actively interested in researches of this domain and seeing big potential for practical adaptation of the results. This Master Thesis covers the review of theoretical and practical results of opinion mining and sentiment analysis, including attempt of creating prototype system for opinion analysis of comments in Lithuanian. Also this study aims to identify problems related to adaptation of Lithuanian language in opinion mining and sentiment analysis system work. Finally, last part contains of the formulated guidance steps for development and improvement of the opinion mining and sentiment analysis.

Vis aktyviau pasaulyje yra domimasi sentimentų analize. Verslininkai, garsių pasaulyje įmonių atstovai naudojasi sentimentų analizės įrankiais, kurie leidžia analizuoti tūkstančius vartotojų komentarų (Twitter, Facebook socialiniuose tinkluose, kituose tinklalapiuose). Išanalizavus internetinius komentarus suinteresuotos šalys mato kaip vertinami jų produktai ar paslaugos, prekės ženklai, darbuotojai. Tai naudinga informacija, kuri padeda valdyti savo verslą. Deja, tokių įrankių skirtų lietuvių kalbai nėra. Šio darbo tikslas išanalizavus nuomonių gavybos metodus parengti sprendimą tinkamą lietuviškų internetinių tekstų sentimentų analizei ir jį įgyvendinti. Šiame darbe yra analizuojami sentimentų analizės metodai, egzistuojantys sentimentų analizės įrankiai. Taip pat pateikiamas metodikos lietuviškų tekstų nuomonių analizei formulavimas, pagrindžiant bandymais. Darbo eigoje sukurtas įrankių rinkinys Python kalba, leidžiantis išbandyti siūlomą metodiką. Darbas užbaigiamas rekomendacijomis, kurios leistų patobulinti sukurtą įrankių rinkinį. / Interest in the analysis of sentiment in the world is rising. Entrepreneurs, representatives from world famous companies are using analysis tools ofsentiments that allow to analyze thousands of users comments (Twitter, Facebook,in social networks, or other sites). After analysis of online comments interested parties can see how is valued their products or services, brands, and employees. This is a useful information, that helps you to manage your business. Unfortunately, there is no such tools for the Lithuanian language. The aim of this analysis is to develop and implement methods for extracting the proper sentiments of decision in Lithuanian texts online. In this paper is an overview of analytical methods, existing sentiment analysis tools. It is also provided formulation of a methodology of Lithuanian texts opinion for its analysis, based on justification tests. During a work process was created a set of tools developed in Python that allows to test the proposed methodology. Work is completed with recomendations, which allows to improve the developed Toolkit.

Genre and domain influence an author\'s style of writing and therefore a text\'s characteristics. Natural language processing is prone to such variations in textual characteristics: it is said to be genre and domain dependent. This thesis investigates genre and domain dependencies in sentiment analysis. Its goal is to support the development of robust sentiment analysis approaches that work well and in a predictable manner under different conditions, i.e. for different genres and domains. Initially, we show that a prototypical approach to sentiment analysis -- viz. a supervised machine learning model based on word n-gram features -- performs differently on gold standards that originate from differing genres and domains, but performs similarly on gold standards that originate from resembling genres and domains. We show that these gold standards differ in certain textual characteristics, viz. their domain complexity. We find a strong linear relation between our approach\'s accuracy on a particular gold standard and its domain complexity, which we then use to estimate our approach\'s accuracy. Subsequently, we use certain textual characteristics -- viz. domain complexity, domain similarity, and readability -- in a variety of applications. Domain complexity and domain similarity measures are used to determine parameter settings in two tasks. Domain complexity guides us in model selection for in-domain polarity classification, viz. in decisions regarding word n-gram model order and word n-gram feature selection. Domain complexity and domain similarity guide us in domain adaptation. We propose a novel domain adaptation scheme and apply it to cross-domain polarity classification in semi- and unsupervised domain adaptation scenarios. Readability is used for feature engineering. We propose to adopt readability gradings, readability indicators as well as word and syntax distributions as features for subjectivity classification. Moreover, we generalize a framework for modeling and representing negation in machine learning-based sentiment analysis. This framework is applied to in-domain and cross-domain polarity classification. We investigate the relation between implicit and explicit negation modeling, the influence of negation scope detection methods, and the efficiency of the framework in different domains. Finally, we carry out a case study in which we transfer the core methods of our thesis -- viz. domain complexity-based accuracy estimation, domain complexity-based model selection, and negation modeling -- to a gold standard that originates from a genre and domain hitherto not used in this thesis.