Semantic Filtering of Twitter Data Using Labeled Property Graph (LPG)

2020 ◽  
Vol 17 (1) ◽  
pp. 195-200
Author(s):  
Charan Teja Kalva ◽  
Kumar Abhishek ◽  
Arun Vutnoori ◽  
Vamshi Krishna Maloth
Keyword(s):  
Semantic Network ◽  
Profile Data ◽  
User Interests ◽  
Regular Behavior ◽  
Manual Intervention ◽  
Twitter Data

Hidden interests of an individual can be inferred by keenly observing their social profile data and blending this data with a semantic network. Getting user interests without user’s manual intervention is very beneficial for companies feeding on user’s regular behavior. This paper provides the entire idea of how to retrieve the user’s hidden interests and what is a semantic network. Twitter is the preferred social platform for entities extraction. We started basically by gathering entities like hashtags and keywords from the tweets posted by an individual. And simultaneously created a Semantic Network using Wikipedia’s taxonomy of categories and subcategories and pages implementing a concept called Labeled Property Graph (LPG). Matching the pre-obtained tweet entities with the Wikipedia graph of Categories and Pages a graph is generated called Hierarchical Interest Graph (HIG) which contains so called hidden interests of user. HIG of an individual is an isolated entity and may never match with others’ HIG.

scholarly journals Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Asad Masood Khattak ◽  
Rabia Batool ◽  
Fahad Ahmed Satti ◽  
Jamil Hussain ◽  
Wajahat Ali Khan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
User Profile ◽  
Information Loss ◽  
Syntactic Analysis ◽  
Network Data ◽  
User Interest ◽  
User Interests ◽  
Domain Specific ◽  
Social Network Data ◽  
Twitter Data

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.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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