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Social Media Sentiment Analysis using Machine Learning and Optimization Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 41 |
| Year of Publication: 2019 |
| Authors: E. M. Badr, Mustafa Abdul Salam, Mahmoud Ali, Hagar Ahmed |
10.5120/ijca2019919306
|
E. M. Badr, Mustafa Abdul Salam, Mahmoud Ali, Hagar Ahmed . Social Media Sentiment Analysis using Machine Learning and Optimization Techniques. International Journal of Computer Applications. 178, 41 ( Aug 2019), 31-36. DOI=10.5120/ijca2019919306
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; stemming. Then their feature vectors are being made by the calculation of TF-IDF and optimized with (PSO) and (ACO) before performing the binary text categorization. Naïve Bayes and Support Vector Machine may be those machine learning technicalities used for the binary classification of tweets.
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