TEXT SENTIMENT ANALYSIS BASED ON FUZZY RULES AND INTENSITY MODIFIERS
Keywords:
Sentiment analysis, fuzzy rules, intensity modifiers, sentiment dictionaryAbstract
Expressing feelings is a hidden part of hard life and communication. To create computers that can
better serve humanity, computer science continues to research into developing machine learning algorithms
that can process text data and perform sentiment analysis tasks on natural language texts. Additionally,
the availability of online reviews and increased end-user expectations are driving the development
of system intelligence that can automatically categorize and share user reviews. Every year, research
in this area has discovered more and more emotions in text, but only a small part of it has been devoted to
the use of fuzzy logic. This mainly happens because the researchers often use binary classification – «positive
» and «negative», less often adding a third class – «neutral». The use of fuzzy logic helps to determine
emotions, and not just «good» and «bad», but the degree of these emotions. The number of classes is defined
by determines of the level of detail. Previously, we proposed a fuzzy dictionary-based sentiment
model, in this paper we propose an improved text sentiment determination model based on a sentiment
dictionary (SentiWordNet) and fuzzy rules. To determine the accuracy and precision of sentiment analysis,
coefficients were applied to observe the emotional load of words of different parts of speech and action
modifiers that contribute to the strengthening or weakening of emotional tones. The quantitative value of
the sentiment of the text is obtained by aggregating normalized data by emotional classes using fuzzy result
methods. As a result of the study, it was found that taking into account all modifiers can significantly
increase the accuracy of the method previously proposed by the authors, and also ensures the determination
of boundaries when determining a detailed assessment of relationships in 7 classes (“very positive”,
“positive”, “somewhat positive”, “neutral” , “somewhat negative”, “negative”, “very negative”).
