Social Media Sentiment Analysis using Machine Learning and Optimization Techniques
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TLDR
The research uses a hybrid method of using Swarm Intelligence optimization algorithms with classifiers to classify a speaker's or a writer’s attitude towards various events or topics and arranging data into positive, negative or neutral categories.Abstract:
Recently, there are emergence and advent of data Inter-personal interaction web sites, micro blogs, wikis, in addition to Web applications and data, e.g. tweets and web-postings express views and opinions on different topics, issues and events in many applications, in addition to, different domains that includes business, economy, politics, sociology, and etc., which are resulted from offering immense opportunities for studying and analyzing human views and sentiment. The objective of sentiment analysis is to classify a speaker's or a writer’s attitude towards various events or topics and arranging data into positive, negative or neutral categories. Sentiment analysis means determining the views of a user from the textual content regarding that topic i.e. how one feels about it. It might be used to classify the text content. Various researchers have used a widespread sort of methods to teach the classifiers for the Twitter dataset with various results. The research uses a hybrid method of using Swarm Intelligence optimization algorithms with classifiers. For each tweet, pre-processing will be done by performing various processes i.e. Tokenization; removal of stop-words and emoticons;read more
Citations
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References
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
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NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
TL;DR: In this paper, two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and another to detect sentiment of a term within a message (termlevel task), were presented.
Proceedings Article
A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle
TL;DR: A system for real-time analysis of public sentiment toward presidential candidates in the 2012 U.S. election as expressed on Twitter, a micro-blogging service, offers a new and timely perspective on the dynamics of the electoral process and public opinion.
Posted Content
User-level sentiment analysis incorporating social networks
TL;DR: It is shown that information about social relationships can be used to improve user-level sentiment analysis and incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.