A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.Comment: Data available at http://www.cs.cornell.edu/people/pabo/movie-review-data

Proton block of proton-activated TRPV1 current.

The TRPV1 cation channel is a polymodal nociceptor that is activated by heat and ligands such as capsaicin and is highly sensitive to changes in extracellular pH. In the body core, where temperature is usually stable and capsaicin is normally absent, H(+) released in response to ischemia, tissue injury, or inflammation is the best-known endogenous TRPV1 agonist, activating the channel to mediate pain and vasodilation. Paradoxically, removal of H(+) elicits a transient increase in TRPV1 current that is much larger than the initial H(+)-activated current. We found that this prominent OFF response is caused by rapid recovery from H(+) inhibition of the excitatory current carried by H(+)-activated TRPV1 channels. H(+) inhibited current by interfering with ion permeation. The degree of inhibition is voltage and permeant ion dependent, and it can be affected but not eliminated by mutations to acidic residues within or near the ion selectivity filter. The opposing H(+)-mediated gating and permeation effects produce complex current responses under different cellular conditions that are expected to greatly affect the response of nociceptive neurons and other TRPV1-expressing cells

Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200

Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied to various sequence-to-sequence learning problems. In this work, we applied CTC to abstractive summarization for spoken content. The "blank" in this case implies the corresponding input data are less important or noisy; thus it can be ignored. This approach was shown to outperform the existing methods in term of ROUGE scores over Chinese Gigaword and MATBN corpora. This approach also has the nice property that the ordering of words or characters in the input documents can be better preserved in the generated summaries.Comment: Accepted by Interspeech 201