LDP-based Social Content Protection for Trending Topic Recommendation

YEAR : 2021

Category: Tags: , ,

Description

The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.

ADDITIONAL INFORMATION

HARDWARE REQUIREMENTS

System : Intel i3 and above
Hard Disk : 40GB
RAM : Minimum 4GB
Processer : 64-bit, four-core, 2.5 GHz minimum per core

SOFTWARE REQUIREMENTS

Front End Language : HTML, CSS, JAVA, JSP SERVELTS
Backend : My SQL
Operating System : Windows 10 or 11
IDE : JAVADEVELIPEMENKIT

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