instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.
answer:
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages


question:
We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argumentbased features, e.g. the percentage of argumentative sentences, the evidencesconclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01% in average.
answer:
Using Argument-based Features to Predict and Analyse Review Helpfulness


question:
To improve user satisfaction, mobile app developers are interested in relevant user opinions such as complaints or suggestions. An important source for such opinions is user reviews on online app markets. However, manual review analysis for useful opinions is often challenging due to the large amount and the noisy-nature of user reviews. To address this problem, we propose M.A.R.K, a keyword-based framework for semiautomated review analysis. The key task of M.A.R.K is to analyze reviews for keywords of potential interest which developers can use to search for useful opinions. We have developed several techniques for that task including: 1) keyword extracting with customized regularization algorithms; 2) keyword grouping with distributed representation; and 3) keyword ranking with ratings and frequencies analysis. Our empirical evaluation and case studies show that M.A.R.K can identify keywords of high interest and provide developers with useful user opinions. Keywords—App Review, Opinion Mining, Keyword
answer:

Mining User Opinions in Mobile App Reviews: A Keyword-based Approach