Enhanced Semantic Graph Based Approach With Sentiment Analysis For User Interest Retrieval From Social Sites

TL;DR

This paper presents a semantic graph and sentiment analysis-based method to identify user interests from social media texts, enabling targeted marketing without relying on surveys or ratings.

Contribution

It introduces a novel semantic graph approach that automatically extracts and ranks keywords from user-generated content for interest analysis.

Findings

Effective keyword extraction and ranking from social media texts.

Semantic graph model accurately identifies user interests.

Method reduces reliance on traditional survey-based data collection.

Abstract

Blogs and social networking sites serve as a platform to the users for expressing their interests, ideas and thoughts. Targeted marketing uses the recommendation systems for suggesting their services and products to the users or clients. So the method used by target marketing is extraction of keywords and main topics from the user generated texts. Most of conventional methods involve identifying the personal interests just on the basis of surveys and rating systems. But the proposed research differs in manner that it aim at using the user generated text as a source medium for identifying and analyzing the personal interest as a knowledge base area of users. Semantic graph based approach is proposed research work that identifies the references of clients and users by analyzing their own texts such as tweets. The keywords need to be extracted from the text generated by the user on the social networking sites. This can be made possible by using several algorithms that extracts the keywords automatically from the available content provided by the user. Based on frequency and degree it ranks the extracted keywords. Furthermore, semantic graph based model assists in providing useful suggestions just by extracting the interests of users by analyzing their contents from social media. In this approach graph comprises of nodes and edges where nodes represents the keywords extracted by the algorithm and edges shows the semantic connection between the nodes. The method does not require internet related user activities like surveys or ratings to gather user interest related information.