Sentiment Analysis on the Social Networks Using Stream Algorithms  

作　　者：Nathan Aston Timothy Munson Jacob Liddle Garrett Hartshaw Dane Livingston Wei Hu 

机构地区：[1]Department of Computer Science, Houghton College, Houghton, USA

出　　处：《Journal of Data Analysis and Information Processing》2014年第2期60-66,共7页数据分析和信息处理（英文）

摘　　要：The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.

关 键 词：Modified BALANCED WINNOW SENTIMENT Analysis TWITTER Online Social Networks Feature Selection Data STREAMS 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]