Crowdsourced real-world sensing: sentiment analysis and the real-time web

The advent of the real-time web is proving both challeng- ing and at the same time disruptive for a number of areas of research, notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis and discusses the motivations and challenges behind such a direction

An evaluation of the role of sentiment in second screen microblog search tasks

The recent prominence of the real-time web is proving both challenging and disruptive for information retrieval and web data mining research. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user's query at a point in time, automated methods are required to sift through this information. Sentiment analysis offers a promising direction for modelling microblog content. We build and evaluate a sentiment-based filtering system using real-time user studies. We find a significant role played by sentiment in the search scenarios, observing detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users' prior topic sentiment

A study of inter-annotator agreement for opinion retrieval

Evaluation of sentiment analysis, like large-scale IR evalu- ation, relies on the accuracy of human assessors to create judgments. Subjectivity in judgments is a problem for rel- evance assessment and even more so in the case of senti- ment annotations. In this study we examine the degree to which assessors agree upon sentence-level sentiment anno- tation. We show that inter-assessor agreement is not con- tingent on document length or frequency of sentiment but correlates positively with automated opinion retrieval per- formance. We also examine the individual annotation cate- gories to determine which categories pose most di±culty for annotators

Classifying sentiment in microblogs: is brevity an advantage?

Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs

On using Twitter to monitor political sentiment and predict election results

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content

DCU at the TREC 2008 Blog Track

In this paper we describe our system, experiments and re- sults from our participation in the Blog Track at TREC 2008. Dublin City University participated in the adhoc re- trieval, opinion finding and polarised opinion finding tasks. For opinion finding, we used a fusion of approaches based on lexicon features, surface features and syntactic features. Our experiments evaluated the relative usefulness of each of the feature sets and achieved a significant improvement on the baseline

Combining social network analysis and sentiment analysis to explore the potential for online radicalisation

The increased online presence of jihadists has raised the possibility of individuals being radicalised via the Internet. To date, the study of violent radicalisation has focused on dedicated jihadist websites and forums. This may not be the ideal starting point for such research, as participants in these venues may be described as “already madeup minds”. Crawling a global social networking platform, such as YouTube, on the other hand, has the potential to unearth content and interaction aimed at radicalisation of those with little or no apparent prior interest in violent jihadism. This research explores whether such an approach is indeed fruitful. We collected a large dataset from a group within YouTube that we identified as potentially having a radicalising agenda. We analysed this data using social network analysis and sentiment analysis tools, examining the topics discussed and what the sentiment polarity (positive or negative) is towards these topics. In particular, we focus on gender differences in this group of users, suggesting most extreme and less tolerant views among female users

Perspective: Matching, Mate Choice, and Speciation

Matching was developed in the 1960s to match such entities as residents and hospitals, colleges and students, or employers and employees. This approach is based on “preference lists,” whereby each participant ranks potential partners according to his/her preferences and tries to match with the highest-ranking partner available. Here, we discuss the implications of matching for the study of mate choice and speciation. Matching differs from classic approaches in several respects, most notably because under this theoretical framework, the formation of mating pairs is context-dependant (i.e., it depends on the configuration of pairings in the entire population), because the stability of mating pairs is considered explicitly, and because mate choice is mutual. The use of matching to study mate choice and speciation is not merely a theoretical curiosity; its application can generate counter-intuitive predictions and lead to conclusions that differ fundamentally from classic theories about sexual selection and speciation. For example, it predicts that when mate choice is mutual and the stability of mating pairs is critical for successful reproduction, sympatric speciation is a robust evolutionary outcome. Yet the application of matching to the study of mate choice and speciation has been largely dominated by theoretical studies. We present the hamlets, a group of brightly colored Caribbean coral reef fishes in the genus Hypoplectrus (Serranidae), as a particularly apt system to test empirically specific predictions generated by the application of matching to mate choice and speciation

Topic-dependent sentiment analysis of financial blogs

While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches

Exploring the use of paragraph-level annotations for sentiment analysis of financial blogs

In this paper we describe our work in the area of topic-based sentiment analysis in the domain of financial blogs. We explore the use of paragraph-level and document-level annotations, examining how additional information from paragraph-level annotations can be used to increase the accuracy of document-level sentiment classification. We acknowledge the additional effort required to provide these paragraph-level annotations, and so we compare these findings against an automatic means of generating topic-specific sub-documents