Sentiment analysis of microblogs using multilayer feed-forward artificial neural networks

Vladimir Despotovic, Dejan Tanikic

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1127-1142
Number of pages16
JournalComputing and Informatics
Volume36
Issue number5
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Machine learning
  • Microblogs
  • Neural networks
  • Opinion mining
  • Sentiment analysis
  • Twitter

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