TEXT BASED MASS OPINION MINING USING NEURAL NETWORK ALGORITHM WITH ABSTRACTIVE SUMMARIZATION

Abstract

Our work, which focuses specifically on Twitter data, presents an advanced methodology for large-scale opinion mining. Sentiment analysis, or opinion mining, is the process of mechanically identifying and categorizing sentiments from textual data. We achieve accurate sentiment categorization and informative result summarizing by combining the latest neural network methods with abstractive summarization techniques. Tokenization, a crucial stage in natural language processing (NLP) activities, is where we start by using BERT (Bidirectional Encoder Representations from Transformers). By capturing the contextual subtleties of language used in Twitter tweets, BERT's contextual embeddings facilitate accurate tokenization. Our model successfully captures the textual data by utilizing BERT's capabilities, which paves the way for further sentiment analysis. We then apply Bidirectional Long Short-Term Memory (BiLSTM) to sentiment categorization. Recurrent neural networks (RNNs) such as BiLSTM are particularly good at recognizing sequential dependencies in data. Sentiment analysis relies heavily on word order since sentiments are frequently influenced by the context that words that come before and after give. Our model's incorporation of BiLSTM allows it to precisely classify tweets as either positive or negative by capturing these sequential dependencies. We also present an abstractive summary element to produce brief summaries that capture dominant attitudes in the dataset. Abstractive summarization generates logical summaries that encapsulate the main ideas of numerous tweets, going beyond simple sentence selection and concatenation. We evaluate the quality of the generated summaries and the accuracy of sentiment categorization achieved by our model through extensive experimentation on a representative and diversified Twitter dataset. Our findings show how reliable and effective our approach is at identifying significant sentiment trends and offering insightful information about the general thoughts shared on Twitter. In the end, our work advances the field of opinion mining and provides useful instruments for deciphering and evaluating massive amounts of social media data.