Tracking public opinion on social media

The increasing popularity of social media has changed the web from a static repository of information into a dynamic forum with continuously changing information. Social media platforms has given the capability to people expressing and sharing their thoughts and opinions on the web in a very simple way. The so-called User Generated Content is a good source of users opinion and mining it can be very useful for a wide variety of applications that require understanding the public opinion about a concept. For example, enterprises can capture the negative or positive opinions of customers about their services or products and improve their quality accordingly. The dynamic nature of social media with the constantly changing vocabulary, makes developing tools that can automatically track public opinion a challenge. To help users better understand public opinion towards an entity or a topic, it is important to: a) find the related documents and the sentiment polarity expressed in them; b) identify the important time intervals where there is a change in the opinion; c) identify the causes of the opinion change; d) estimate the number of people that have a certain opinion about the entity; and e) measure the impact of public opinion towards the entity. In this thesis we focus on the problem of tracking public opinion on social media and we propose and develop methods to address the different subproblems. First, we analyse the topical distribution of tweets to determine the number of topics that are discussed in a single tweet. Next, we propose a topic specific stylistic method to retrieve tweets that are relevant to a topic and also express opinion about it. Then, we explore the effectiveness of time series methodologies to track and forecast the evolution of sentiment towards a specific topic over time. In addition, we propose the LDA & KL-divergence approach to extract and rank the likely causes of sentiment spikes. We create a test collection that can be used to evaluate methodologies in ranking the likely reasons of sentiment spikes. To estimate the number of people that have a certain opinion about an entity, we propose an approach that uses pre-publication and post- publication features extracted from news posts and users' comments respectively. Finally, we propose an approach that propagates sentiment signals to measure the impact of public opinion towards the entity's reputation. We evaluate our proposed methods on standard evaluation collections and provide evidence that the proposed methods improve the performance of the state-of-the-art approaches on tracking public opinion on social media

USI Participation at SMERP 2017 Text Summarization Task

Abstract. This short report describes the participation of the Università della Svizzera italiana (USI) at the SMERP Workshop Data Challenge Track for the task text summarization of Level 1. Our participation is based on a linear interpolation for combining relevance and novelty scores of the retrieved tweets. Our method is fully automatic. For the relevance score we used the results from our runs at the text retrieval task whereas for the novelty we used a method based on Word2Vec. In total, we submitted four different runs and we used two different weight parameters. The results showed that when relevance and novelty have an equal contribution in selecting the tweets to use for the summary, the performance is better compared to favoring only the novelty. Additionally, information from POS tags improves the performance of the summarization task

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction