Cascaded Adaptive Dilated Temporal Convolution Network-Based Efficient Sentiment Analysis Model from Social Media Posts.

Sentimental analysis is one of the most complicated tasks in text mining. It is the process of converting unstructured text to structural text to identify the meaningful pattern of data. Different kinds of sentiments in social media posts are classified into positive and negative. Natural Language Processing (NLP) plays an imperative task in examining sentiments on social media. Sentimental analysis techniques frequently use text data to help to monitor customer feedback about products and understand the needs of customers in the field of business. The text data are gathered from social media comments that lead to several drawbacks such as spelling errors, noise, and unstructured data, and some of the data are in abbreviation form in which abbreviations are hard to understand from the text data. Therefore, the innovative sentiment analysis method is implemented to identify the kinds of sentiments from the collected social media posts. At first, the text data are assembled from the social media posts. The collected text data are subjected to text preprocessing techniques to improve the data quality. NLP techniques such as a bag of n-grams, glove embedding, and Bidirectional Encoder Representations from Transformers (BERT) are applied to separate the features from the text data. The removed features from these techniques are given to the fusion of the optimal feature stage, where the weight optimization takes place via the Mutated Random Parameter-based Serval Optimization Algorithm (MRP-SOA). The attained optimal weighted fused features contain the most relevant information, and it is fed to the classifier for analyzing the sentiments. For analyzing the sentiments, the Cascaded Adaptive Dilated Temporal Convolutional Network (CADTCN) is utilized, and the parameters from ADTCN are optimized with the same to get higher classification outcomes. The analysis outcomes are compared with the traditional sentiment analysis models in terms of various metrics to show the success of the advanced design. [ABSTRACT FROM AUTHOR]