Sensor de reputação online: técnicas de aprendizagem automática para a deteção e classificação de opiniões na Web

As redes sociais são plataformas em larga escala onde pessoas de todo o mundo se podem conhecer, partilhar imagens e vídeos ou trocar opiniões. Saber as opiniões dos utilizadores que podem afetar a reputação de um produto ou serviço é uma das vantagens que as empresas podem retirar deste tipo de plataformas. O objetivo deste trabalho é apresentar um sistema com a capacidade de determinar, através de técnicas de aprendizagem automática, o sentimento de uma frase e respetivo impacto na afetação da reputação da entidade mencionada, classificando-o como positivo, negativo ou neutro. Este sistema foi desenvolvido na linguagem Python e utiliza recursos da ferramenta NLTK, como o reconhecimento de entidades (NE Chunk), o classificador gramatical (pos-tag) e os algoritmos para o classificador da polaridade de sentimentos (Naive Bayes, Decision Trees e SVM); Online Reputation Sensor: machine learning techniques for detection and classification of opinions in Web textual sources #### abstract: The social networks are large scale platforms where people around the world meet, share photos and videos and share opinions. Knowing people's opinions about a product or service is one of the advantages that companies can benefit from these type of plataforms. The purpose of this work is to present a system with the ability to predict, through machine learning techniques, the sense of a sentence and the respective reputation impact on the target entity, classifying it as negative, positive or neutral. This system was developed in Python and uses resources from NLTK framework, such as entity recognition (NE Chunk), the grammar classifier (pos-tag) and the algorithms used in system development (Naive Bayes, Decision Trees, and SVM)

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

Understanding the topics and opinions from social media content

Social media has become one indispensable part of people’s daily life, as it records and reflects people’s opinions and events of interest, as well as influences people’s perceptions. As the most commonly employed and easily accessed data format on social media, a great deal of the social media textual content is not only factual and objective, but also rich in opinionated information. Thus, besides the topics Internet users are talking about in social media textual content, it is also of great importance to understand the opinions they are expressing. In this thesis, I present my broadly applicable text mining approaches, in order to understand the topics and opinions of user-generated texts on social media, to provide insights about the thoughts of Internet users on entities, events, etc. Specifically, I develop approaches to understand the semantic differences between language-specific editions of Wikipedia, when discussing certain entities from the related topical aspects perspective and the aggregated sentiment bias perspective. Moreover, I employ effective features to detect the reputation-influential sentences for person and company entities in Wikipedia articles, which lead to the detected sentiment bias. Furthermore, I propose neural network models with different levels of attention mechanism, to detect the stances of tweets towards any given target. I also introduce an online timeline generation approach, to detect and summarise the relevant sub-topics in the tweet stream, in order to provide Internet users with some insights about the evolution of major events they are interested in