Local search five-element cycle optimized reLU-BiLSTM for multilingual aspect-based text classification.

Aspect-based sentiment analysis has gained wide popularity due to its benefits of text extraction, classification, and ranking the overall sentiments of each feature extracted. However, the aspect-based feature extraction techniques often result in acquiring more number aspects that refer to the same feature which arises the need for aspect-based text classification. Since most of the existing techniques focus on monolingual aspect-based sentimental analysis, we planned to develop a multilingual aspect-based text classification for Indian languages. We perform the multilingual aspect-based text classification on different morphologically rich and complex languages such as Hindi, Tamil, Malayalam, Bengali, Urdu, Telugu, and Sinhalese. To achieve this objective, in this article we present an optimized rectified linear unit (reLU) layer-based bidirectional long short-term memory (reLU-BiLSTM) deep learning tool is developed. The parameters of the reLU-BiLSTM architecture are optimized using the local search-based five-element cycle optimization algorithm (LSFECO) optimization algorithm. Initially, the proposed model preprocesses the multilingual texts obtained from the reviews using different techniques such as tokenization, special character removal, text normalization and so forth. The discrete and categorical features from the different languages are initially extracted by applying the bidirectional encoder representations from transformers (BERT) model which processes the sentences in the text in a layer-by-layer manner. The context learning and word embeddings (aspects) present in the text are identified using different approaches such as word mover's distance, continuous Bag-of-Words (CBOW), and Cosine similarity. The LSFECO optimized reLU-BiLSTM architecture classifies the different aspects present in the embedding document to its corresponding classes (flowers, plants, animals, sports, politics, etc). The efficiency of the proposed methodology is evaluated using the text obtained from different text documents such as semantic relations from Wikipedia, Habeas Corpus (HC) Corpora, Sentiment Lexicons for 81 Languages, IIT Bombay English-Hindi Parallel Corpus, and Indic Languages Multilingual Parallel Corpus. When compared to conventional techniques, the proposed methodology outperforms them in terms of entropy, coverage, purity, processing time, accuracy, F1-score, recall, and precision.