Combining a Rule-based Classifier with Weakly Supervised Learning for Twitter Sentiment Analysis
Date
2016-10-28
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Microblog, especially Twitter, have become an integral part of our daily life, where millions of user sharing
their thoughts daily because of its short length characteristics
and simple manner of expression. Monitoring and analyzing
sentiments from such massive amount of twitter posts provide
enormous opportunities for companies and other organizations
to learn about what user think and feel about their products
and services. But the ever-growing unstructured and informal
user-generated posts in twitter demands sentiment analysis tools
that can perform well with minimum supervision. In this paper,
we propose an approach for sentiment analysis on twitter, where
we combine a rule-based classifier with weakly supervised NaiveBayes classifier. To classify the tweets sentiment, we introduce a
set of rules for the rule-based classifier based on the occurrences
of emoticons and sentiment-bearing words, whereas several sentiment lexicons are applied to train the Naive-Bayes classifier. We
conducted our experiments based on the Stanford sentiment140
dataset. Experimental results demonstrate the effectiveness of our
method over the baseline in terms of recall, precision, F1 score,
and accuracy.
Description
Keywords
Microblogs, sentiment analysis, sentiment classification, twitter, sentiment lexicons, emoticons.
Citation
IIUC-ICISET2016-ID-116