Phrase-level sentence patterns for estimating positive and negative emotions using Neuro-fuzzy model for information retrieval applications

Abstract

The paper proposes phrase-level emotion patterns using Neuro-Fuzzy model. At the initial stage, the emotional patterns at phrase level are obtained using POS Tags and EMOT Actifiers that results into 16 patterns. These patterns works well with the sentences having single emotion and classifies them into Positive and Negative polarities. However, it is observed that these patterns are unable to define the exact boundary between positive and negative polarities of these sentence patterns. Thus, this issue will affect the classification accuracy due to imprecise boundary between the sentences. Mixed emotions exist in long sentences with multi phrases and therefore the sentences are broken at Phrase-level. The patterns are extracted at phrase-level and converted as fuzzy rules for the classification of mixed emotion patterns. Intensity grades are calculated for the patterns based on the features of phrases and their structure in the sentence. These intensity grades classify the patterns at phrase level into Positive and Negative emotions. Based on the intensity grades, a suitable weighing mechanism is proposed for the multi phrasal sentence structure which decides the degree of Positive and Negative polarities of emotion in a sentence. Higher weighted phrasal pattern decides the Positive and Negative polarities of emotion in a sentence. Proposed approach performs well and achieves good F-Scores compared with other comparative approaches on benchmark datasets.