Long Short-Term Memory Models
buttons.Introduction
Over a decade, so many users have generated lots of content on the internet, mostly on social platforms. Millions of individuals use any social platform to express their opinions, and these contents create a considerable amount of raw data. Such a massive volume of raw data brings lots of exciting tasks. These tasks are under Natural Language Processing (NLP) applications. NLP is a branch of Artificial Intelligence (AI) focusing on text-related problems, and one of its goals is to understand the human language. In NLP, there are various language-related fields to focus on, such as machine translation, chatbots, summarizations, question answering, sentiment analysis.
Sentiment Analysis (SA) is closely linked to NLP. Sentiment analysis is a scale result that shows the sentiment and the opinions coming from a raw text. It is essential and helpful application to analyze an individual's thoughts. The sentiment analysis result may help various fields from industrial purposes such as advertising and sales to academic purposes. Even though sentiment analysis has been a focus of authors for a while, the challenges in this field, such as having sarcasm and irony in a text, make this task still unfinished. Therefore, there is still colossal attention on sentiment analysis, and new approaches have arisen.
Recently, many novel approaches to AI systems have been developed using Machine Learning (ML). Also, with the help of Deep Learning (DL) techniques, a subfield of ML, the algorithms such as Generative Adversarial Networks and transformers. Many studies have focused on sentiment analysis in NLP fields. Today, comprehensive survey studies and novelty approaches to sentiment analysis are still being carried out. In the paper, levels of sentiment analysis, challenges and trends in this field, and the genetic process are mentioned detail. Here, sarcasm detection was shown as one of the challenges, and related studies to solve this challenge are examined. Instead of traditional machine learning approaches, other techniques such as DL and reinforcement learning resulted in more robust solutions to challenges.
Authors proposed a hybrid model by combining the DL approach and sentiment analysis model to predict the stock prices. Sentiment analysis in the stock market is critical to estimating future price changes. In this article, the authors created a hybrid model using a Convolutional Neural Network (CNN) to create a sentiment analysis classifier on investors' comments and Long Short-Term Memory (LSTM) Neural Network to analyze the stock. Implementation of this hybrid model on the real-life data on the Shanghai Stock Exchange (SSE) showed that the hybrid approach outperformed.
The study conducts a novel approach to ML-based classifiers. From Twitter, related tweets have been retrieved from eight countries, and people's behavior on the infectious disease was aimed to analyze. In the proposed model, Naïve Bayes Support Vector Machines (NBSVM), CNN, Bidirectional Gated Recurrent Network (BiGRU), fastText, and DistilBERT were used as base classifiers, and the fusion of these approaches was represented as “Meta Classifier”. The proposed model gave better results than four DL and one machine learning approach.
This paper gives the comparison works on sentiment analysis using state-of-art ML approaches: LSTM, Bag of Words (BoT), CNN, and transformer. The aim is to compare the performances of deep learning approaches in terms of accuracy and time complexity. Moreover, the impact of hyperparameters on the model's accuracy was analyzed.
This paper is organized as follows. In the second section, background information about sentiment analysis, particularly in NLP, is discussed. In the third section, the approach to the methodology and the state-of-art approach are explained. The fourth section explains the results, and the paper is concluded in the last section.