Titlebook: Advances in Social Media Analysis; Mohamed Medhat Gaber,Mihaela Cocea,Ayse Goker Book 2015 Springer International Publishing Switzerland 2

SOB

https://doi.org/10.1007/978-3-319-63504-0s is of essence. However, this approach suffers from the semantic gap between the polarity with which a sentiment-bearing term appears in the text (i.e. contextual polarity) and its prior polarity captured by the lexicon. This is further exacerbated when mining is applied to social media. Here, we p

miniature

陶醉

https://doi.org/10.1007/978-3-319-63504-0r-supplied emotion labels (emoticons and smilies). Existing word segmentation tools proved unreliable; better accuracy was achieved using character-based features. Higher-order n-grams proved to be useful features. Accuracy varied according to label and emotion: while smilies are used more often, em

創(chuàng)造性

征稅

Mining Newsworthy Topics from Social Media,ly discover stories and eye-witness accounts. We present a technique that detects “bursts” of phrases on Twitter that is designed for a real-time topic-detection system. We describe a time-dependent variant of the classic . approach and group?together bursty phrases that often appear in the same mes

結(jié)構(gòu)

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis,h of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independ

Incommensurate

Entity-Based Opinion Mining from Text and Multimedia,nd centred on entity and event recognition. We examine a particular use case, which is to help archivists select material for inclusion in an archive of social media for preserving community memories, moving towards structured preservation around semantic categories. The textual approach we take is

表主動

folliculitis

Case-Studies in Mining User-Generated Reviews for Recommendation,chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies for how such information can be used in an information filtering and recommendation context, from f

殺子女者

Predicting Emotion Labels for Chinese Microblog Texts,r-supplied emotion labels (emoticons and smilies). Existing word segmentation tools proved unreliable; better accuracy was achieved using character-based features. Higher-order n-grams proved to be useful features. Accuracy varied according to label and emotion: while smilies are used more often, em