Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

Asad Masood Khattak,Jamil Hussain,Rabia Batool,Bashir Hayat,Fahad Ahmed Satti,Wajahat Ali Khan,Adil Mehmood Khan,Atif Khan

doi:10.1155/2020/8892552

Abstract

Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.

Highlights

In the last decade, social networks have witnessed multifold advancements due to the rapid digitization of the service industry and other advancements in the field of information technology
In terms of information discovery and knowledge creation, this plethora of user created content allows the application of sentiment analysis, which aims to provide an automated mechanism for determining the writer’s attitude towards the subject or its overall contextual polarity [13]. ese insights are especially useful for digital marketing, allowing organizations and in some cases governments to monitor and measure social media and gain actionable business/social intelligence, allowing to understand how people view their brands, products, and services and to improve brand visibility
We have demonstrated a personalized recommendation system, based on user profile matching

Summary

Introduction

Social networks have witnessed multifold advancements due to the rapid digitization of the service industry and other advancements in the field of information technology. A plethora of information sharing platforms and the increased connectivity with the Internet [1] have led to a change in the general perspective of networking, socialization, and personalization [2]. According to PwC Health Research Institute [26], almost 90% users in the age of 18–24 were willing to share their health information on social networks. Such large use of social media has introduced the problem of information overload. E proposed classification and sentiment analysis system uses a semantic structure, important keywords, and opinion words from tweets to monitor user interests and generates personalized healthcare and wellness-related tweet recommendations.

Related Work

The Proposed System Architecture

Implementation and Result

Findings

Conclusion