Hybrid Hierarchical Attention Network‐Hierarchical Deep Learning for Text Classification in Opinion Mining

ABSTRACT In general, opinion mining indicates the process of evaluating the opinions of people on several topics that are accessible in text form. It is an important aspect of natural language processing as it sets up the effective planning and decision‐making for businesses and users. Opinion mining can be performed more effectively and conveniently by initially carrying out subjectivity recognition, which entails recognizing the text as objective or subjective. This research comprises various steps, like preprocessing, feature extraction, data augmentation and opinion mining. The complete procedure was implemented in the Spark framework that utilizes a master–slave framework. The preprocessing step is done with methods, such as stop‐word removal, stemming, and lemmatization. Afterwards, feature extraction is done by extracting sentiWordNet features and statistical features that involve capitalized words, exclamation marks, and hashtags. Followed by the data augmentation, the opinion mining phase uses a HAN–HDLTex approach proposed by the combination of HAN and HDLTex architectures. The experimentation is done for the proposed HAN–HDLTex model that shows better accuracy with a rate of 0.949, sensitivity with a rate of 0.969, and specificity with a rate of 0.939.