SENTIMENTAL ANALYSIS USING DEEP LEARNING TECHNIQUES

INFORMATION ENGINEERING

Sentiment analysis is an essential part of natural language processing (NLP) and is critical to understanding the subjective elements of textual data, such as product evaluations and social media posts. Because deep learning approaches can recognize complex sequential patterns in text data, they have become highly effective tools in this field. Two such techniques are Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN). The goal of this work is to give a thorough understanding of the effectiveness of LSTM and RNN architectures in sentiment analysis by presenting an in-depth examination and implementation of the technique. The methodology is a multi-step procedure that starts with textual data preprocessing. Tokenization, stemming, and vectorization are a few of the preprocessing activities that help transform unprocessed text into a format that deep learning models can understand. The pre processed data is then put into RNN and LSTM networks, which are built to handle sequential data by slowly retaining contextual information. By adding memory cells that may selectively keep or reject input, LSTM, a specialized type of RNN, solves the drawbacks of conventional RNNs and helps the model better capture long-range dependencies by reducing the vanishing gradient issue. The outcomes of the experiments show how well the sentiment analysis model based on LSTM and RNN can reliably identify sentiment from textual data in a variety of domains and datasets. Visualization approaches also improve the interpretability of the model by clarifying the learned representations and illuminating the underlying decision-making process. Overall, by demonstrating the efficiency of deep learning techniques—more especially, LSTM and RNN architectures—in extracting sentiment information from textual data, this research advances sentiment analysis methodologies and opens the door to applications in sentiment monitoring, opinion mining, and market analysis.

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