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—User generated content on Twitter (produced at an enormous rate of 340 million tweets per day) provides a rich source for gleaning people's emotions, which is necessary for deeper understanding of people's behaviors and actions. Extant studies on emotion identification lack comprehensive coverage of " emotional situations " because they use relatively(More)
The problem of automatic extraction of sentiment expressions from informal text, as in microblogs such as tweets is a recent area of investigation. Compared to formal text, such as in product reviews or news articles , one of the key challenges lies in the wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or(More)
Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated(More)
Existing studies on predicting election results are under the assumption that all the users should be treated equally. However, recent work [14] shows that social media users from different groups (e.g., " silent majority " vs. " vocal minority ") have significant differences in the generated content and tweeting behavior. The effect of these differences on(More)
Social Media Analytics The practice of gathering data from social media websites and analyzing that data to gain new insights, facilitate informed decisions and actions Semantic Web Semantic Web is a group of methods and technologies to help machines and humans understand the meaning — or " semantics " — of data on the World Wide Web(More)
Peoples emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse. Emotions are both prevalent in and(More)
In domain-specific search systems, knowledge of a domain of interest is embedded as a backbone that guides the search process. But the knowledge used in most such systems 1. exists only for few well known broad domains; 2. is of a basic nature: either purely hierarchical or involves only few relationship types; and 3. is not always kept up-to-date missing(More)
Many research studies adopt manually selected patterns for semantic relation extraction. However, manually identifying and discovering patterns is time consuming and it is difficult to discover all potential candidates. Instead, we propose an automatic pattern construction approach to extract verb synonyms and antonyms from English newspapers. Instead of(More)
We present a method for growing the amount of knowledge available on the Web using a hermeneutic method that involves background knowledge, Information Extraction techniques and validation through discourse and use of the extracted information. We exemplify this using Linked Data as background knowledge, automatic Model/Ontology creation for the IE part and(More)