A linguistically-driven methodology for detecting impending and unfolding emergencies from social media messages

Natural disasters have demonstrated the crucial role of social media before, during and after emergencies (Haddow & Haddow 2013). Within our EU project Sland \ub4 ail, we aim to ethically improve \ub4 the use of social media in enhancing the response of disaster-related agen-cies. To this end, we have collected corpora of social and formal media to study newsroom communication of emergency management organisations in English and Italian. Currently, emergency management agencies in English-speaking countries use social media in different measure and different degrees, whereas Italian National Protezione Civile only uses Twitter at the moment. Our method is developed with a view to identifying communicative strategies and detecting sentiment in order to distinguish warnings from actual disasters and major from minor disasters. Our linguistic analysis uses humans to classify alert/warning messages or emer-gency response and mitigation ones based on the terminology used and the sentiment expressed. Results of linguistic analysis are then used to train an application by tagging messages and detecting disaster- and/or emergency-related terminology and emotive language to simulate human rating and forward information to an emergency management system

Identifying nocuous ambiguity in natural language requirements

This dissertation is an investigation into how ambiguity should be classified for authors and readers of text, and how this process can be automated. Usually, authors and readers disambiguate ambiguity, either consciously or unconsciously. However, disambiguation is not always appropriate. For instance, a linguistic construction may be read differently by different people, with no consensus about which reading is the intended one. This is particularly dangerous if they do not realise that other readings are possible. Misunderstandings may then occur. This is particularly serious in the field of requirements engineering. If requirements are misunderstood, systems may be built incorrectly, and this can prove very costly. Our research uses natural language processing techniques to address ambiguity in requirements. We develop a model of ambiguity, and a method of applying it, which represent a novel approach to the problem described here. Our model is based on the notion that human perception is the only valid criterion for judging ambiguity. If people perceive very differently how an ambiguity should be read, it will cause misunderstandings. Assigning a preferred reading to it is therefore unwise. In text, such ambiguities should be located and rewritten in a less ambiguous form; others need not be reformulated. We classify the former as nocuous and the latter as innocuous. We allow the dividing line between these two classifications to be adjustable. We term this the ambiguity threshold, and it represents a level of intolerance to ambiguity. A nocuous ambiguity can be an unacknowledged or an acknowledged ambiguity for a given set of readers. In the former case, they assign disparate readings to the ambiguity, but each is unaware that the others read it differently. In the latter case, they recognise that the ambiguity has more than one reading, but this fact may be unacknowledged by new readers. We present an automated approach to determine whether ambiguities in text are nocuous or innocuous. We use heuristics to distinguish ambiguities for which there is a strong consensus about how they should be read. These are innocuous ambiguities. The remaining nocuous ambiguities can then be rewritten at a later stage. We find consensus opinions about ambiguities by surveying human perceptions on them. Our heuristics try to predict these perceptions automatically. They utilise various types of linguistic information: generic corpus data, morphology and lexical subcategorisations are the most successful. We use coordination ambiguity as the test case for this research. This occurs where the scope of words such as and and or is unclear. Our research contributes to both the requirements engineering and the natural language processing literatures. Ambiguity is known to be a serious problem in requirements engineering, but has rarely been dealt with effectively and thoroughly. Our approach is an appropriate solution, and our flexible ambiguity threshold is a particularly useful concept. For instance, high ambiguity intolerance can be implemented when writing requirements for safety-critical systems. Coordination ambiguities are widespread and known to cause misunderstandings, but have received comparatively little attention. Our heuristics show that linguistic data can be used successfully to predict preferred readings of very diverse coordinations. Used in combination, these heuristics demonstrate that nocuous ambiguity can be distinguished from innocuous ambiguity under certain conditions. Employing appropriate ambiguity thresholds, accuracy representing 28% improvement on the baselines can be achieved

Sentiment Analysis: State of the Art

We present the state of art in sentiment analysis which covers the purpose of sentiment analysis, levels of sentiment analysis and processes that could be used to measure polarity and classify labels. Moreover, brief details about some resources of sentiment analysis are included

New features for sentiment analysis: Do sentences matter?

1st International Workshop on Sentiment Discovery from Affective Data 2012, SDAD 2012 - In Conjunction with ECML-PKDD 2012; Bristol; United Kingdom; 28 September 2012 through 28 September 2012In this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. The TripAdvisor dataset is used to evaluate the effect of sentence level features on polarity classification. Our initial results indicate a small improvement in classification accuracy when using the newly proposed features. However, the benefit of these features is not limited to improving sentiment classification accuracy since sentence level features can be used for other important tasks such as review summarization.European Commission, FP7, under UBIPOL (Ubiquitous Participation Platform for Policy Making) Projec

On Being Negative

This paper investigates the pragmatic expressions of negative evaluation (negativity) in two corpora: (i) comments posted online in response to newspaper opinion articles; and (ii) online reviews of movies, books and consumer products. We propose a taxonomy of linguistic resources that are deployed in the expression of negativity, with two broad groups at the top level of the taxonomy: resources from the lexicogrammar or from discourse semantics. We propose that rhetorical figures can be considered part of the discourse semantic resources used in the expression of negativity. Using our taxonomy as starting point, we carry out a corpus analysis, and focus on three phenomena: adverb + adjective combinations; rhetorical questions; and rhetorical figures. Although the analysis in this paper is corpus-assisted rather than corpus-driven, the final goal of our research is to make it quantitative, in extracting patterns and resources that can be detected automatically

Political Success and the Media

How have different media affected the linguistic performativity of the most prominent American politicians throughout history? How have different types of media allowed certain linguistic features to flourish, and others to fail? I address these question’s through a diachronic analysis of three different periods of American history as well as an investigation into effective linguistic features that manifest over the radio, through television, and on social media. In addition, I confront the myth that there is a relationship between reading level of speech determined by the Flesch-Kincaid algorithm and success as an orator. I find relationships between linguistic features unique to the media through which it is presented and conclude on how that affects the overall expertise of the candidate, but find no relationship between Flesch-Kincaid reading level and expertise

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination