Enrique Costa-Montenegro

TL;DR: The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc.

TL;DR: This work proposes a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons.

TL;DR: An integrated solution which recommends to the users applications by considering a big amount of information: that is, according to their previously consumed applications, use pattern, tags used to annotate resources and history of ratings is presented.

TL;DR: The UrBan Interactions research program has created a middleware layer on top of the panOULU wireless network and opened it up to ubiquitous-computing researchers, offering opportunities to enhance and facilitate communication between citizens and the government.

TL;DR: This work automatically constructed a novel emoji sentiment lexicon using an unsupervised sentiment analysis system based on the definitions given by emoji creators in Emojipedia and automatically created lexicon variants by also considering the sentiment distribution of the informal texts accompanying emojis.