Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.

User-supplied reviews are widely and increasingly used to enhance ecommerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.

This paper presents a method for identifying an opinion with its holder and topic, given a sentence from online news media texts. We introduce an approach of exploiting the semantic structure of a sentence, anchored to an opinion bearing verb or adjective. This method uses semantic role labeling as an intermediate step to label an opinion holder and topic using data from FrameNet. We decompose our task into three phases: identifying an opinion-bearing word, labeling semantic roles related to the word in the sentence, and then finding the holder and the topic of the opinion word among the labeled semantic roles. For a broader coverage, we also employ a clustering technique to predict the most probable frame for a word which is not defined in FrameNet. Our experimental results show that our system performs significantly better than the baseline.

No abstract

In this paper, we introduce a methodology for analyzing judgment opinions. We define a judgment opinion as consisting of a valence, a holder, and a topic. We decompose the task of opinion analysis into four parts: 1) recognizing the opinion; 2) identifying the valence; 3) identifying the holder; and 4) identifying the topic. In this paper, we address the first three parts and evaluate our methodology using both intrinsic and extrinsic measures.

In this paper, we present a system that automatically extracts the pros and cons from online reviews. Although many approaches have been developed for extracting opinions from text, our focus here is on extracting the reasons of the opinions, which may themselves be in the form of either fact or opinion. Leveraging online review sites with author-generated pros and cons, we propose a system for aligning the pros and cons to their sentences in review texts. A maximum entropy model is then trained on the resulting labeled set to subsequently extract pros and cons from online review sites that do not explicitly provide them. Our experimental results show that our resulting system identifies pros and cons with 66% precision and 76% recall.

No abstract

Technical bottlenecks in protein production and secretion often limit the efficient and robust industrial use of microbial enzymes. The potential of nonthermal atmospheric pressure plasma to overcome these technical barriers was examined. Spores of the fermenting fungus Aspergillus oryzae (A. oryzae) were submerged in potato dextrose broth (PDB) (5 3 10 6 per ml) and treated with micro dielectric barrier discharge plasma at an input voltage of 1.2 kV and current of 50 to 63 mA using nitrogen as the feed gas. The specific activity of a-amylase in the broth was increased by 7.4 to 9.3% after 24 and 48 h of plasma treatment. Long-lived species, such as NO 2 À and NO 3 À , generated in PDB after plasma treatment may have contributed to the elevated secretion of a-amylase. Observations after 24 h of plasma treatment also included increased accumulation of vesicles at the hyphal tip, hyphal membrane depolarization and higher intracellular Ca 2+ levels. These results suggest that long-lived nitrogen species generated in PDB after plasma treatment can enhance the secretion of a-amylase from fungal hyphae by depolarizing the cell membrane and activating Ca 2+ influx into hyphal cells, eventually leading to the accumulation of secretory vesicles near the hyphal tips.