Indian Machinery and Transport Equipment Exports - Forecasting with External Factors Using Chain of Hybrid Sarimax-Garch Model
Volume-5 | Issue-2

Enhancing Road Safety: A Driver Fatigue Detection and Behaviour Monitoring System using Advanced Computer Vision Techniques
Volume-6 | Issue-2

Green Lights Ahead: An IoT Solution for Prioritizing Emergency Vehicles
Volume-5 | Issue-3

Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2

Smart Farming: Enhancing Network Infrastructure for Agricultural Sustainability
Volume-6 | Issue-1

Predictive Analytics with Data Visualization
Volume-4 | 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

Split-Capacitor Five-Level Transformerless Grid Connected Single Phase PV System using Level Shifted PWM Technique
Volume-4 | 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

Home / Archives / Volume-4 / Issue-3 / Article-8

Volume - 4 | Issue - 3 | september 2022

Sentiment Analysis and Topic Modeling on News Headlines Open Access
Vijay Yadav  , Subarna Shakya  328
Pages: 204-218
Cite this article
Yadav, Vijay. "Sentiment Analysis and Topic Modeling on News Headlines." PhD diss., IOE Pulchowk Campus, 2022.
DOI
10.36548/jucct.2022.3.008
Published
22 September, 2022
Abstract

Sentiment analysis and topic modeling has wide range of applications from medical to entertainment industry, corporates, politics and so on. News media play vital role in shaping the views of public towards any product or people. The dataset used for this work is news headlines dataset of one of the leading new portals of India i.e., Times of India. This research aims to perform comparative study of both supervised and unsupervised learning for text analysis and use the best performing models in both the category for prediction of sentiment and topic classification of news headlines. For sentiment analysis, supervised techniques like Machine learning ensemble model and Bi-LSTM have used. Similarly, unsupervised techniques like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) have been for topic modeling.

Keywords

Sentiment analysis Topic modeling Data visualization Bi-LSTM LDA LSA

×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

To access the full PDF, please complete the payment process.

Subscription Details

Category Fee
Article Access Charge
15 USD
Open Access Fee Nil
Annual Subscription Fee
200 USD
After payment,
please send an email to irojournals.contact@gmail.com / journals@iroglobal.com requesting article access.
Subscription form: click here