SENTIMENT ANALYSIS OF TEXT REVIEWS USING TONE DICTIONARIES AND FUZZY SET CARDINALITY

Abstract

Sentiment or opinion analysis aims to determine the polarity of people's opinions in relation
to any product, service, event or any person. One of the most common methods used in sentiment
analysis of text content is natural language processing. Sentiment analysis of natural language
text can be assessed using numerous methodologies such as machine learning algorithms and
statistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic was
chosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way of
defining the problem leads to a reduction in bias, both positively and negatively. Secondly, learn ing approaches based on fuzzy rules are fundamentally different from those learning approaches
that are widely used in sentiment classification, such as support vector machines, naive Bayes,
etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree to
which an instance belongs to each individual class. The proposed model for sentiment analysis of
text reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.
The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,
and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure of
the evaluation of the indicators of the polarity of words. Word polarity values are obtained by
applying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were created
depending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparison
of the presented approach based on fuzzy logic with other dictionary-based methods demonstrates
the superiority of the developed models based on the application of fuzzy logic.