Opinion mining with the SentWordNet lexical resource

Sentiment classification concerns the application of automatic methods for predicting the orientation of sentiment present on text documents. It is an important subject in opinion mining research, with applications on a number of areas including recommender and advertising systems, customer intelligence and information retrieval. SentiWordNet is a lexical resource of sentiment information for terms in the English language designed to assist in opinion mining tasks, where each term is associated with numerical scores for positive and negative sentiment information. A resource that makes term level sentiment information readily available could be of use in building more effective sentiment classification methods. This research presents the results of an experiment that applied the SentiWordNet lexical resource to the problem of automatic sentiment classification of film reviews. First, a data set of relevant features extracted from text documents using SentiWordNet was designed and implemented. The resulting feature set is then used as input for training a support vector machine classifier for predicting the sentiment orientation of the underlying film review. Several scenarios exploring variations on the parameters that generate the data set, outlier removal and feature selection were executed. The results obtained are compared to other methods documented in the literature. It was found that they are in line with other experiments that propose similar approaches and use the same data set of film reviews, indicating SentiWordNet could become an important resource for the task of sentiment classification. Considerations on future improvements are also presented based on a detailed analysis of classification results

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

Sentiment Classification Using Negation as a Proxy for Negative Sentiment

We explore the relationship between negated text and neg- ative sentiment in the task of sentiment classification. We propose a novel adjustment factor based on negation occur- rences as a proxy for negative sentiment that can be applied to lexicon-based classifiers equipped with a negation detec- tion pre-processing step. We performed an experiment on a multi-domain customer reviews dataset obtaining accuracy improvements over a baseline, and we further improved our results using out-of-domain data to calibrate the adjustment factor. We see future work possibilities in exploring nega- tion detection refinements, and expanding the experiment to a broader spectrum of opinionated discourse, beyond that of customer reviews

RCES: Rapid Cues Exploratory Search Using Taxonomies For COVID-19

To assist the COVID-19 focused researchers in life science and healthcare in understanding the pandemic, we present an exploratory information retrieval system called RCES. The system employs a previously developed EVE (Explainable Vector-based Embedding) model using DBpedia and an adopted model using MeSH taxonomies to exploit concept relations related to COVID-19. Various expansion methods are also developed, along with explanations and facets that collectively form rapid cues for a valuable navigational and informed user experience

Case report: tuberous breast

To report a case of tuberous breast with significant breast asymmetry, describe the technique used and evaluate the outcome of the case. Different techniques were used on each breast, although studies in literature recommend the use of similar strategy in both breasts. The patient progressed without complications and had, 6 months after the surgery, extremely satisfactory result without tuberous breast stigmas, and significant improvement of breast asymmetry. The literature establishes that not only one surgical technique is adequate to correct different types of malformations. Tuberous breast constitutes a challenge in breast plastic surgery and it becomes more complex when the asymmetry is more severe. However, surgeons who is trained in a variety of aesthetic and breast reconstructive techniques can achieve a satisfactory result

Ecchymosis evaluation after internal and external continuous lateral nasal osteotomy in open rhinoplasty

Introduction: The objective is to evaluate the presence of ecchymosis 7 and 15 days after internal and external lateral nasal osteotomy in open rhinoplasty. Methods: A prospective evaluation of 15 patients who underwent open rhinoplasty with lateral nasal osteotomy was conducted. The patients were allocated into two groups. Those who underwent external lateral nasal osteotomy were included in group A (n = 6), while those who underwent internal osteotomy were included in group B (n = 9). The patients were evaluated on postoperative days 7 and 15, and the presence or absence of ecchymosis was recorded. Results: In group A, we observed that on postoperative day 7, 3 patients (50%) had ecchymosis and 3 (50%) showed no changes in skin color. On postoperative day 15, the same group had 2 patients (25%) with ecchymosis and 4 (75%) without changes. On the other hand, in group B, 3 patients (33.4%) had ecchymosis and 6 (66.6%) showed no changes on postoperative day 7. In the same group, 1 patient (11.1%) had ecchymosis and 8 (88.9%) showed no changes 15 days after surgery. Conclusion: Despite the lower incidence of ecchymosis in internal fractures on postoperative days 7 and 15, no statistical significance was observed between the two techniques

Sentiment Classification of Reviews Using SentiWordNet

Sentiment classification concerns the use of automatic methods for predicting the orientation of subjective content on text documents, with applications on a number of areas including recommender and advertising systems, customer intelligence and information retrieval. SentiWordNet is an opinion lexicon derived from the WordNet database where each term is associated with numerical scores indicating positive and negative sentiment information. This research presents the results of applying the SentiWordNet lexical resource to the problem of automatic sentiment classification of film reviews. Our approach comprises counting positive and negative term scores to determine sentiment orientation, and an improvement is presented by building a data set of relevant features using SentiWordNet as source, and applied to a machine learning classifier. We find that results obtained with SentiWordNet are in line with similar approaches using manual lexicons seen in the literature. In addition, our feature set approach yielded improvements over the baseline term counting method. The results indicate SentiWordNet could be used as an important resource for sentiment classification tasks. Additional considerations are made on possible further improvements to the method and its use in conjunction with other techniques