Reference : Sentiment Analysis of Microblogs Using Multilayer Feed-forward Artificial Neural Networks
Scientific journals : Article
Engineering, computing & technology : Computer science
Sentiment Analysis of Microblogs Using Multilayer Feed-forward Artificial Neural Networks
Despotovic, Vladimir mailto [University of Belgrade > Technical Faculty in Bor]
Tanikic, Dejan [University of Belgrade > Technical Faculty in Bor]
Computing and Informatics
Slovak Academic Press
Yes (verified by ORBilu)
[en] Sentiment analysis ; Opinion mining ; Microblogs ; Twitter ; Neural networks ; Machine learning
[en] Sentiment analysis aims to extract public opinion on a particular topic and microblogs, especially Twitter as the most influential platform, represent a significant source of information. The application to microblogs has to cope with difficulties, such as informal language with abbreviations, internet jargons, emoticons, hashtags that do not appear in conventional text documents. Sentiment analysis technique for microblogs based on a feed-forward artificial neural network (ANN) with sigmoid activation function is proposed in this paper and compared to machine learning approaches, i.e. Multinomial Naive Bayes, Support Vector Machines and Maximum Entropy. Experiments were performed on Stanford Twitter Sentiment corpus, a balanced dataset which contains noisy training labels weakly annotated using emoticons as sentiment indicators; and SemEval-2014 Task 9 corpus, an unbalanced dataset which contains manually annotated training examples. The obtained results show that ANN produces superior or at least comparable results to state-of-the-art machine learning techniques.

File(s) associated to this reference

Fulltext file(s):

Limited access
3141-10085-1-PB.pdfPublisher postprint570.38 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.