文章基本信息

标题：Sentiment Analysis on Data of Social Media
本地全文：下载
作者：Aditya Zaware ; Tanmay Karnawat ; Sneha Pisey 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：10
页码：8215-8218
DOI：10.15680/IJIRCCE.2018. 0610043
出版社：S&S Publications
摘要：This paper deals with sentiment analysis in text documents, especially text valence detection. The survey tend to concentrate on modeling user generated review and aim to spot linguistic aspects from knowledge of opinion. It also tries to predict overall sentiments for a user generated post on social media. Sentiment analysis over Twitter and other Social Media platforms offer organizations a fast and effective way to monitor the publics’ feelings towards their general post or any post related to their brand, business etc. A wide range of features and methods for training sentiment classifiers for Twitter and other Social Media platforms have been researched in recent years with varying results. Till date not many researches have been made to extract sentiments from emoticons. So the purpose of this paper is to perform sentiment analysis on text data as well as on emoticons through known researches and learning new concepts about data analysis.
关键词：text valence; linguistic aspects; modeling user;generated review; sentiment analysis