A Case-Based Approach to Cross Domain Sentiment Classification

This paper considers the task of sentiment classification of subjective text across many domains, in particular on scenarios where no in-domain data is available. Motivated by the more general applicability of such methods, we propose an extensible approach to sentiment classification that leverages sentiment lexicons and out-of-domain data to build a case-based system where solutions to past cases are reused to predict the sentiment of new documents from an unknown domain. In our approach the case representation uses a set of features based on document statistics, while the case solution stores sentiment lexicons employed on past predictions allowing for later retrieval and reuse on similar documents. The case-based nature of our approach also allows for future improvements since new lexicons and classification methods can be added to the case base as they become available. On a cross domain experiment our method has shown robust results when compared to a baseline single-lexicon classifier where the lexicon has to be pre-selected for the domain in question

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

dispel4py: A Python framework for data-intensive scientific computing

This paper presents dispel4py, a new Python framework for describing abstract stream-based workflows for distributed data-intensive applications. These combine the familiarity of Python programming with the scalability of workflows. Data streaming is used to gain performance, rapid prototyping and applicability to live observations. dispel4py enables scientists to focus on their scientific goals, avoiding distracting details and retaining flexibility over the computing infrastructure they use. The implementation, therefore, has to map dispel4py abstract workflows optimally onto target platforms chosen dynamically. We present four dispel4py mappings: Apache Storm, message-passing interface (MPI), multi-threading and sequential, showing two major benefits: a) smooth transitions from local development on a laptop to scalable execution for production work, and b) scalable enactment on significantly different distributed computing infrastructures. Three application domains are reported and measurements on multiple infrastructures show the optimisations achieved; they have provided demanding real applications and helped us develop effective training. The dispel4py.org is an open-source project to which we invite participation. The effective mapping of dispel4py onto multiple target infrastructures demonstrates exploitation of data-intensive and high-performance computing (HPC) architectures and consistent scalability.</p

A classification-based review recommender

Paper presented at Twenty-ninth SGAI International Conference (AI-2009), Cambridge, UK, 15th-17th December 2009Many online stores encourage their users to submit product/service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews. We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the most helpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.Science Foundation IrelandConference detailshttp://www.bcs-sgai.org/ai2009/?section=hom