We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference of its inverse document frequencies in the positive and negative training sets. While this technique is remarkably easy to implement, it gives a statistically significant improvement over the standard bag-ofwords approaches using support vector machines on a range of classification tasks. Our results show that our technique is robust and broadly applicable. We provide an analysis of why the approach works and how it can generalize to other domains and problems.

This paper describes our Align-andDifferentiate approach to the SemEval 2015 Task 2 competition for English Semantic Textual Similarity (STS) systems. Our submission achieved the top place on two of the five evaluation datasets. Our team placed 3rd among 28 participating teams, and our three runs ranked 4th, 6th and 7th among the 73 runs submitted by the 28 teams. Our approach improves upon the UMBC PairingWords system by semantically differentiating distributionally similar terms. This novel addition improves results by 2.5 points on the Pearson correlation measure.

No abstract

Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using three well known data sets.

This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from online reviews. Unlike prior approaches that first extract features and then group them into categories, the proposed approach combines feature and aspect discovery instead of chaining them. In addition, prior work on feature extraction tends to require seed terms and focus on identifying explicit features, while the proposed approach extracts both explicit and implicit features, and does not require seed terms. We evaluate this approach on reviews from three domains. The results show that it outperforms several state-of-the-art methods on both tasks across all three domains.