Semantic Sentiment Analysis of Microblogs

Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people's opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis on Twitter, and other similar microblogging platforms, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment (e.g., "great'', "terrible''). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. This is problematic since the sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., "great'' is negative in the context "pain'') or the conceptual meaning associated with the words (e.g., "Ebola" is negative when its associated semantic concept is "Virus"). This thesis investigates the role of words' semantics in sentiment analysis of microblogs, aiming mainly at addressing the above problem. In particular, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, several approaches are proposed in this thesis for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words' co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. Evaluation under each sentiment analysis task includes several sentiment lexicons, and up to 9 Twitter datasets of different characteristics, as well as comparing against several state-of-the-art sentiment analysis approaches widely used in the literature. The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider words' semantics for sentiment analysis at both, entity and tweet levels, surpass non-semantic approaches in most datasets

Invariance of quantum correlations under local channel for a bipartite quantum state

We show that the quantum discord and the measurement induced non-locality (MiN) in a bipartite quantum state is invariant under the action of a local quantum channel if and only if the channel is invertible. In particular, these quantities are invariant under a local unitary channel.Comment: 4 pages, no figures, proof of theorm 2 modifie

Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold

Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics' feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet "I love iPhone, but I hate iPad" can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets

Affordable interactive virtual reality system for the Dynamic Hip Screw surgery training in vitro

Interactive virtual reality systems provide safe and cost-effective training environment to improve the technical skills and competence of surgeons. The trainees can have as many practice sessions, without need to the trainer all the time, before even start carrying out the procedure on any real patient. In this paper, we present an affordable interactive virtual reality system for the Dynamic Hip Screw (DHS) surgery training in vitro, through 3D tracking. The system facilitates a safe (in vitro / off patient) training to improve the cognitive coordination of trainees and junior surgeons, in particular the Hands, Eyes and Brain coordination. The system is based on very cheap commercial off-the-shelf (COT) components, which are very affordable, and needs minimum setup effort and knowledge. It also provides a range of visual and quantitative feedback information and measures, such as position, orientation, insertion point, and depth of drilling. It is envisaged that improving this level of coordination, through the training system, will contribute to reducing the failure rate of the DHS procedure. This means better treatment for patients and less costs for the Health services systems (e.g. UK's NHS system)

A Linked Open Data Approach for Sentiment Lexicon Adaptation

Social media platforms have recently become a gold mine for organisations to monitor their reputation by extracting and analysing the sentiment of the posts generated about them, their markets, and competitors. Among the approaches to analyse sentiment from social media, approaches based on sentiment lexicons (sets of words with associated sentiment scores) have gained popularity since they do not rely on training data, as opposed to Machine Learning approaches. However, sentiment lexicons consider a static sentiment score for each word without taking into consideration the different contexts in which the word is used (e.g, great problem vs. great smile). Additionally, new words constantly emerge from dynamic and rapidly changing social media environments that may not be covered by the lexicons. In this paper we propose a lexicon adaptation approach that makes use of semantic relations extracted from DBpedia to better understand the various contextual scenarios in which words are used. We evaluate our approach on three different Twitter datasets and show that using semantic information to adapt the lexicon improves sentiment computation by 3.7% in average accuracy, and by 2.6% in average F1 measure

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier’s feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space

A systemic review of barriers to accessing paediatric eye care services in African countries

Background: Global estimate reported that 1.4 million children are blind of which three-quarters live in developing countries. Childhood Visual Impairment is a major public health problem globally especially in rural areas of developing countries.Objective: To review barriers to accessing paediatric eye care services in African countriesMethods: The studies in this review were searched in online databases (PubMed, Web of Sciences, ProQuest, Scopus, Google Scholar, African Index Medicus and Medline) for studies published between January 2000 and April 2020. The articles included in this review, which was conducted in Africa to assess the barriers for accessing paediatric eye care services with regards availability, accessibility, affordability, socio cultural barriers of parents/caregivers and community.Results: Of 22 705 articles screened, the study found 29 publications from 10 African countries which met the inclusion criteria. The main barriers were non-availability, non-accessibility, and non-affordability of paediatric eye care services. The studies reviewed revealed that there are other factors affecting the utilization of paediatric eye services which include the primary health system, geographic barriers, health beliefs, perception of parents; lack of knowledge, attitudes and practices about paediatric eye care. Furthermore, environmental, demographic barriers and socio-economic status has negative impact on accessing paediatric eye care services in African counties.Conclusion: The main barriers to accessing paediatric eye care services in Africa were affordability, accessibility and availability. There is therefore a need for all relevant stakeholders to play a significant role in addressing barriers to child eye carein African countries. Keywords: Paediatric eye care; Africa; availability; accessibility; affordability; visual impairment; refractive errors