With the upsurge of Web 2.0, customers are able to share their opinions and feelings about products and services, politics, economic shifts, current events and any number of other topics on the Web. This information, if leveraged effectively, can provide rich and valuable insights, such as: input for vendors to create successful marketing strategies, understanding of areas of improvement in products and services and tracking political opinion. The problem with this information is that it is unorganised and unstructured, therefore, it is difficult to assess automatically and in bulk. Studies in the field of sentiment analysis aim to provide a solution to determining the polarities of, and gain an overview of, the wider public opinion behind certain topics in a large volume of textual data. This research provides a novel framework and a solid, practical implementation of the proposed framework for fine-grained sentiment analysis. The framework supports mixed-opinion text and multiword expressions when analysing the sentiments expressed and the aspects that those sentiments relate to. This research uses datasets across two domains in the customer reviews area (phone products and hotel services) to evaluate the proposed framework for its reliability and validity. A sizeable performance improvement was noted whereby the proposed methodology yielded a result of 91.3% accuracy in sentiment classification, as compared to the baseline (SentiWordNet), which had a result of 71.0%. In addition, an accuracy of 92.5% was observed for the aspect analysis automatically generated across the two domains tested.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-07-03T18:15:15Z No. of bitstreams: 1 GilbertoNunes.pdf: 2115811 bytes, checksum: 7e6f22622b699f30f43f51d50a8be819 (MD5) / Made available in DSpace on 2017-07-03T18:15:15Z (GMT). No. of bitstreams: 1 GilbertoNunes.pdf: 2115811 bytes, checksum: 7e6f22622b699f30f43f51d50a8be819 (MD5) Previous issue date: 2016-08-19 / With the globalization of the Internet, the number of users using the means of social communication it is each time bigger. The social network Twitter is a good example. Twitter is often used to post comments on all kinds of subjects, such as artists, products, public health, among others. The spread of information in these media is very important because it can reach people from social class, anytime and anywhere in the world. Twitter supports geo-referenced comments. This feature allows georeferenced tweets. One can use the comments obtained from Twitter to evaluate how the reality of social network reflects the real world. In this sense, the present work proposes a generic Framework that besides evaluating concepts related to the opinion mining, describes the accomplishment of case studies, which analyze sources of textual opinions and proposes to mine opinions at the level of aspect, using as sources of opinion Twitter comments. A prototype extends and implements the proposed Framework to enable the process of opinion mining in social networks. The results show the feasibility of using this Framework to support decision making by its users. / Com a globalização da Internet, o número de usuários utilizando os meios de comunicação social é cada vez maior. A Rede Social Twitter é um bom exemplo disso. Frequentemente, o Twitter é utilizado para postar comentários sobre os mais variados tipos de assuntos, como: artistas, produtos, saúde pública, dentre outros. A propagação da informação nesses meios de comunicação é muito relevante, pois pode atingir pessoas de todas as classes sociais, a qualquer hora e lugar do mundo. O Twitter, além de apresentar tamanha abrangência, permite a postagem de comentários georreferenciados, ou seja, possibilita a localização de onde as postagens foram feitas. Diversos estudos propõem a utilização das postagens obtidas a partir do Twitter, para avaliar o quão esses meios de comunicação refletem o mundo real. Nesse sentido, o presente trabalho propõe um Framework genérico que, além de avaliar conceitos relacionados à mineração de opiniões, descreve a realização de estudos de caso, os quais analisam fontes de opiniões textuais e propõe minerar opiniões em nível de aspecto, utilizando como fontes de opinião comentários do Twitter. Um protótipo estende e implementa o Framework proposto para viabilizar o processo de mineração de opinião em redes sociais. Os resultados obtidos mostram a viabilidade da utilização desse Framework para suporte à tomada de decisão por parte de seus usuários.

We present results of a sentiment annotation study in the context of historical German plays. Our annotation corpus consists of 200 representative speeches from the German playwright Gotthold Ephraim Lessing. Six annotators, five non-experts and one expert in the domain, annotated the speeches according to different sentiment annotation schemes. They had to annotate the differentiated polarity (very negative, negative, neutral, mixed, positive, very positive), the binary polarity (positive/negative) and the occurrence of eight basic emotions. After the annotation, the participants completed a questionnaire about their experience of the annotation process; additional feedback was gathered in a closing interview. Analysis of the annotations shows that the agreement among annotators ranges from low to mediocre. The non-expert annotators perceive the task as very challenging and report different problems in understanding the language and the context. Although fewer problems occur for the expert annotator, we cannot find any differences in the agreement levels among non-experts and between the expert and the non-experts. At the end of the paper, we discuss the implications of this study and future research plans for this area