Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
Volume-3 | Issue-2
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
A Review on Data Securing Techniques using Internet of Medical Things
Volume-3 | Issue-3
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
Volume-3 | Issue-2
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
Volume-3 | Issue-4
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
Volume-2 | Issue-4
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Ethereum and IOTA based Battery Management System with Internet of Vehicles
Volume-3 | Issue-3
Volume - 5 | Issue - 4 | december 2023
Published
13 January, 2024
With the advent of smartphones and the ease of access to the internet, people are mainly interested in sending textual messages through social media platforms. In many cases, customers would like to review the services provided by different providers in order to express satisfaction or dissatisfaction. The sentiments of users make a huge difference in the success of any business idea in the present digital age. As there are many competitors in every field of technology, health, and education, people would selectively want to use the resources that have positive opinions about them from the user community in the online reviews. There are different techniques to effectively estimate the user reviews, whether they are for or against a particular concept or the product. There are different techniques, like lexicon-based techniques, machine learning-based techniques, and deep learning-based techniques which are used to analyse the sentiments of the users’ reviews in order to improve user expectations. Lexicon-based techniques have many challenges, like the wrong interpretation of the meanings of the words and giving wrong sentiment scores to the words used by ignoring the grammatical constraints in the user reviews. There are many machine learning algorithms, like Logistic regression (LR), and Support Vector Machines (SVM) which can overcome the shortcomings of lexicon-based sentiment analysis models and could be used in various spheres of applications. The manuscript presents a detailed study in this regard.
KeywordsLexicon Machine Learning Sentiment analysis Deep Learning.
Full Article PDF