Automatic Sentiment Analysis in On-line Text

The growing stream of content placed on the Web provides a huge collection of textual resources. People share their experiences on-line, ventilate their opinions (and frustrations), or simply talk just about anything. The large amount of available data creates opportunities for automatic mining and analysis. The information we are interested in this paper, is how people feel about certain topics. We consider it as a classification task: their feelings can be positive, negative or neutral. A sentiment isn't always stated in a clear way in the text; it is often represented in subtle, complex ways. Besides direct expression of the user's feelings towards a certain topic, he or she can use a diverse range of other techniques to express his or her emotions. On top of that, authors may mix objective and subjective information about a topic, or write down thoughts about other topics than the one we are investigating. Lastly, the data gathered from the World Wide Web often contains a lot of noise. All of this makes the task of automatic recognition of the sentiment in on-line text more difficult. We will give an overview of various techniques used to tackle the problems in the domain of sentiment analysis, and add some of our own results

Active Learning Enhanced Document Annotation for Sentiment Analysis

Part 1: Cross-Domain Conference and Workshop on Multidisciplinary Research and Practice for Information Systems (CD-ARES 2013)International audienceSentiment analysis is a popular research area devoted to methods allowing automatic analysis of the subjectivity in textual content. Many of these methods are based on the using of machine learning and they usually depend on manually annotated training corpora. However, the creation of corpora is a time-consuming task, which leads to necessity of methods facilitating this process. Methods of active learning, aimed at the selection of the most informative examples according to the given classification task, can be utilized in order to increase the effectiveness of the annotation. Currently it is a lack of systematical research devoted to the application of active learning in the creation of corpora for sentiment analysis. Hence, the aim of this work is to survey some of the active learning strategies applicable in annotation tools used in the context of sentiment analysis. We evaluated compared strategies on the domain of product reviews. The results of experiments confirmed the increase of the corpus quality in terms of higher classification accuracy achieved on the test set for most of the evaluated strategies (more than 20% higher accuracy in comparison to the random strategy)

Combining similarity and sentiment in opinion mining for product recommendation

In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains.Science Foundation Irelan