Extraction and Classification of Semantic Data from Twitter

被引：3

作者：

Xavier, Clarissa Castella ^{[1
]}

Souza, Marlo ^{[2
]}

机构：

[1] Univ Fed Rio Grande do Sul, Inst Informat, Porto Alegre, RS, Brazil

[2] Univ Fed Bahia UFBA, Inst Matemat & Estat, Salvador, BA, Brazil

来源：

WEBMEDIA'18: PROCEEDINGS OF THE 24TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB | 2018年

关键词：

Twitter; Semantic Extraction; Information Extraction; Text Analysis;

D O I：

10.1145/3243082.3264606

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Twitter is a social network and microblogging service in which registered users read and post messages called Tweets. Tweets have a maximum of 280 characters and cover every conceivable subject, from simple activity updates and news coverage to opinions about arbitrary topics. In this way, Twitter emerges as a valuable data source to get information about what people think and feel about the most different subjects. In this context, this work presents different approaches for extracting and processing information from Twitter using Natural Language Processing (NLP) and Machine Learning techniques, examining tools and methods to collect and analyze semantic information from Tweets.

引用

页码：15 / 18

页数：4

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