Arturo Montejo-Ráez

Experiments with SVM to classify opinions in different domains

Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains.

Ranked WordNet graph for Sentiment Polarity Classification in Twitter

This paper presents a novel approach to Sentiment Polarity Classification in Twitter posts, by extracting a vector of weighted nodes from the graph of WordNet. These weights are used in SentiWordNet to compute a final estimation of the polarity. Therefore, the method proposes a non-supervised solution that is domain-independent. The evaluation of a generated corpus of tweets shows that this technique is promising.

Integrating MeSH Ontology to Improve Medical Information Retrieval

This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retrieval subtask. The experiments accomplished are based on the integration of specific knowledge in the topics. We have used the MeSH ontology to expand the queries. The expansion consists in searching terms from the topic query in the MeSH ontology in order to add similar terms. We have processed the set of collections using Information Gain (IG) in the same way as in ImageCLEFmed 2006. In our experiments mixing visual and textual information we obtain better results than using only textual information. The weigth of the textual information is very strong in this mixed strategy. In the experiments with a low textual weight, the use of IG improves the results obtained.

Pessimists and optimists: Improving collaborative filtering through sentiment analysis

This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.

Using information gain to improve multi-modal information retrieval systems

Nowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have become an active research direction. In the past few years, a lot of content-based image retrieval (CBIR) systems have been developed. However, despite the progress achieved in the CBIR, the retrieval accuracy of current systems is still limited and often worse than only textual information retrieval systems. In this paper, we propose to combine content-based and text-based approaches to multi-modal retrieval in order to achieve better results and overcome the lacks of these techniques when they are taken separately. For this purpose, we use a medical collection that includes both images and non-structured text. We retrieve images from a CBIR system and textual information through a traditional information retrieval system. Then, we combine the results obtained from both systems in order to improve the final performance. Furthermore, we use the information gain (IG) measure to reduce and improve the textual information included in multi-modal information retrieval systems. We have carried out several experiments that combine this reduction technique with a visual and textual information merger. The results obtained are highly promising and show the profit obtained when textual information is managed to improve conventional multi-modal systems.

Crowd explicit sentiment analysis

With the rapid growth of data generated by social web applications new paradigms in the generation of knowledge are opening. This paper introduces Crowd Explicit Sentiment Analysis (CESA) as an approach for sentiment analysis in social media environments. Similar to Explicit Semantic Analysis, microblog posts are indexed by a predefined collection of documents. In CESA, these documents are built up from common emotional expressions in social streams. In this way, texts are projected to feelings or emotions. This process is performed within a Latent Semantic Analysis. A few simple regular expressions (e.g. “I feel X”, considering X a term representing an emotion or feeling) are used to scratch the enormous flow of micro-blog posts to generate a textual representation of an emotional state with clear polarity value (e.g. angry, happy, sad, confident, etc.). In this way, new posts can be indexed by these feelings according to the distance to their textual representation. The approach is suitable in many scenarios dealing with social media publications and can be implemented in other languages with little effort. In particular, we have evaluated the system on Polarity Classification with both English and Spanish data sets. The results show that CESA is a valid solution for sentiment analysis and that similar approaches for model building from the continuous flow of posts could be exploited in other scenarios.

A knowledge-based approach for polarity classification in Twitter

Until now, most of the methods published for polarity classification in Twitter have used a supervised approach. The differences between them are only the features selected and the method used for weighting them. In this article, we present an unsupervised method for polarity classification in Twitter. The method is based on the expansion of the concepts expressed in the tweets through the application of PageRank to WordNet. In addition, we integrate SentiWordNet to compute the final value of polarity. The synsets values are weighted with the PageRank scores obtained in the previous random walk process over WordNet. The results obtained show that disambiguation and expansion are good strategies for improving overall performance.

Selection strategies for multi-label text categorization

In multi-label text categorization, determining the final set of classes that will label a given document is not trivial. It implies first to determine whether a class is suitable of being attached to the text and, secondly, the number of them that we have to consider. Different strategies for determining the size of the final set of assigned labels are studied here. We analyze several classification algorithms along with two main strategies for selection: by a fixed number of top ranked labels, or using per-class thresholds. Our experiments show the effects of each approach and the issues to consider when using them.In multi-label text categorization, determining the final set of classes that will label a given document is not trivial. It implies first to determine whether a class is suitable of being attached to the text and, secondly, the number of them that we have to consider. Different strategies for determining the size of the final set of assigned labels are studied here. We analyze several classification algorithms along with two main strategies for selection: by a fixed number of top ranked labels, or using per-class thresholds. Our experiments show the effects of each approach and the issues to consider when using them.

Otium: A web based planner for tourism and leisure

This paper introduces the Otiŭm planner system for scheduling of leisure tasks in tourism. This novel service allows users to create their own agenda of activities within specified dates. Activities are selected from a list of recommended events according to last selected events, user preferences and other parameters. The proposed restrictions on the recommendation procedure have been found to capture static and dynamic user context. The recommendation function is linear and shows low computational cost. The events are extracted from web sources with almost no human manipulation, so the recommender is always showing new and recent events. The Ajax-based web interface eases the creation of the final plan, offering an interactive experience to the user. We consider that the trade-off between interactivity and recommendation complexity exits, and that the second issue is preferable in this type of services. The details about the design and implementation of the system are described, along with the issues the system resolves and some guidelines for enhancement. 2011 Elsevier Ltd. All rights reserved.

Query expansion on medical image retrieval: MeSH vs. UMLS

In this paper we explain experiments in the medical information retrieval task (ImageCLEFmed). We experimented with query expansion and the amount of textual information obtained from the collection. For expansion, we carried out experiments using MeSH ontology and UMLS separately. With respect to textual collection, we produced three different collections, the first one with caption and title, the second one with caption, title and the text of the section where the image appears, and the third one with the full text article. Moreover, we experimented with textual and visual search, along with the combination of these two results. For image retrieval we used the results generated by the FIRE software. The best results were obtained using MeSH query expansion on shortest textual collection (only caption and title) merging with the FIRE results.