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

Investigating and extending the methods in automated opinion analysis through improvements in phrase based analysis

Opinion analysis is an area of research which deals with the computational treatment of opinion statement and subjectivity in textual data. Opinion analysis has emerged over the past couple of decades as an active area of research, as it provides solutions to the issues raised by information overload. The problem of information overload has emerged with the advancements in communication technologies which gave rise to an exponential growth in user generated subjective data available online. Opinion analysis has a rich set of applications which are used to enable opportunities for organisations such as tracking user opinions about products, social issues in communities through to engagement in political participation etc.The opinion analysis area shows hyperactivity in recent years and research at different levels of granularity has, and is being undertaken. However it is observed that there are limitations in the state-of-the-art, especially as dealing with the level of granularities on their own does not solve current research issues. Therefore a novel sentence level opinion analysis approach utilising clause and phrase level analysis is proposed. This approach uses linguistic and syntactic analysis of sentences to understand the interdependence of words within sentences, and further uses rule based analysis for phrase level analysis to calculate the opinion at each hierarchical structure of a sentence. The proposed opinion analysis approach requires lexical and contextual resources for implementation. In the context of this Thesis the approach is further presented as part of an extended unifying framework for opinion analysis resulting in the design and construction of a novel corpus. The above contributions to the field (approach, framework and corpus) are evaluated within the Thesis and are found to make improvements on existing limitations in the field, particularly with regards to opinion analysis automation. Further work is required in integrating a mechanism for greater word sense disambiguation and in lexical resource development

Identificación de opiniones de diferentes fuentes en textos en español

Este documento presenta un estudio de las expresiones que transmiten opiniones de diferentes fuentes en textos en español. El trabajo incluye la definición de un modelo para los predicados de opinión y sus argumentos (la fuente, el asunto y el mensaje), la creación de un léxico de predicados de opinión que tienen asociada nformación proveniente del modelo y la realización de tres sistemas informáticos. Desarrollamos un primer sistema, basado en reglas contextuales, que obtiene valores de medida F parcial (incluyendo entre los elementos correctos los elementos reconocidos en forma parcial) satisfactorios: 92 % para el predicado, 81 % para la fuente, 75 % para el asunto, 89 % para el mensaje y 85 % para la opinión completa. En particular, para el reconocimiento de la fuente se obtuvo un 79 % de medida F exacta (sin incluir elementos reconocidos en forma parcial). El segundo sistema desarrollado se basa en el modelo Conditional Random Fields (CRF) y se realizó solo para el reconocimiento de las fuentes. El sistema alcanza un valor de medida F exacta de 76 %. Un tercer sistema, que combina las dos técnicas anteriores incorporando la salida del sistema de reglas para el reconocimiento de fuentes como un nuevo atributo del sistema basado en CRF, mejora sensiblemente los resultados obtenidos por los dos sistemas anteriores: 83 % de medida F exacta. En cuanto al reconocimiento de las fuentes de las opiniones, nuestro sistema obtiene resultados muy satisfactorios (83 % de medida F exacta), si tomamos como referencia trabajos realizados para otros idiomas que pueden considerarse similares al nuestro, si bien presentan varias diferencias en su enfoque y su alcance.Estos trabajos alcanzan valores de medida F (exacta o parcial) que se sitúan entre 63 % y 89,5 %. Por otro lado, durante el desarrollo de esta tesis generamos diversos recursos de utilidad para el procesamiento automático del español: un léxico de predicados de opinión, un corpus de 13.000 palabras anotado con las opiniones y sus elementos y un corpus de 40.000 palabras anotado con los predicados de opinión y sus fuentes