Learning to Identify Ambiguous and Misleading News Headlines

Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines "clickbaits" and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.Comment: Accepted by IJCAI 201

SemEval-2016 task 5 : aspect based sentiment analysis

International audienceThis paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams

The Role of Text Pre-processing in Sentiment Analysis

It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature