Annotation tools are a valuable asset for the construction of labelled textual datasets. However, they tend to have a rigid structure, closed back-end and front-end, and are built in a non-user-friendly way. These downfalls difficult their use in annotation tasks requiring varied text formats, prevent researchers to optimise the tool to the annotation task, and impede people with little programming knowledge to easily modify the tool rendering it unusable for a large cohort. Targeting these needs, we present a web-based collaborative annotation and consolidation tool (AWOCATo), capable of supporting varied textual formats. AWOCATo is based on three pillars: (1) Simplicity, built with a modular architecture employing easy to use technologies; (2) Flexibility, the JSON configuration file allows an easy adaption to the annotation task; (3) Customizability, parameters such as labels, colours, or consolidation features can be easily customized. These features allow AWOCATo to support a range of tasks and domains, filling the gap left by the absence of annotation tools that can be used by people with and without programming knowledge, including those who wish to easily adapt a tool to less common tasks. AWOCATo is available for download at https://github.com/TDaudert/AWOCATo.

We present the pilot SemEval task on Suggestion Mining. The task consists of subtasks A and B, where we created labeled data from feedback forum and hotel reviews respectively. Subtask A provides training and test data from the same domain, while Subtask B evaluates the system on a test dataset from a different domain than the available training data. 33 teams participated in the shared task, with a total of 50 members. We summarize the problem definition, benchmark dataset preparation, and methods used by the participating teams, providing details of the methods used by the top ranked systems. The dataset is made freely available to help advance the research in suggestion mining, and reproduce the systems submitted under this task

With the rising popularity of social media in the society and in research, analysing texts short in length, such as microblogs, becomes an increasingly important task. As a medium of communication, microblogs carry peoples sentiments and express them to the public. Given that sentiments are driven by multiple factors including the news media, the question arises if the sentiment expressed in news and the news article themselves can be leveraged to detect and classify sentiment in microblogs. Prior research has highlighted the impact of sentiments and opinions on the market dynamics, making the financial domain a prime case study for this approach. Therefore, this paper describes ongoing research dealing with the exploitation of news contained sentiment to improve microblog sentiment classification in a financial context.

Social media’s popularity in society and research is gaining momentum and simultaneously increasing the importance of short textual content such as microblogs. Microblogs are affected by many factors including the news media, therefore, we exploit sentiments conveyed from news to detect and classify sentiment in microblogs. Given that texts can deal with the same entity but might not be vastly related when it comes to sentiment, it becomes necessary to introduce further measures ensuring the relatedness of texts while leveraging the contained sentiments. This paper describes ongoing research introducing distributional semantics to improve the exploitation of news-contained sentiment to enhance microblog sentiment classification.

This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.

In today’s world, globalisation is not only affecting inter-culturalism but also linking markets across the globe. Given that all markets are affecting each other and are not only driven by fundamental data but also by sentiments, sentiment analysis regarding the markets becomes a tool to predict, anticipate, and milden future economic crises such as the one we faced in 2008. In this paper, an approach to improve sentiment analysis by exploiting relationships among different kinds of sentiment, together with supplementary information, from and across various data sources is proposed.