• Prof. Pradhnya Mehta
• Mr. Ashish Saroj
• Mr. Siddharth Sanas
• Mr. Rajat Doijad

Online social networks have produced bunches of online social groups where individuals can collaborate and shuffle their thoughts. In spite of, the real problems that conflicts with the user security and convenience are confidentiality break, groups without inception, confusion created out of various groups in which a user is a member of and difficulty in managing group regulations. This can be moderate to an extent by an automated filtering method required to categorizing members within a group based on their pattern of response. This paper proposes, the posts within a group are clustered based on stylistic, thematic, emotional, sentimental and psycholinguistic methods. After group members are categorized based on their response to the posts belonging to different clustering methods. The categorization provides security like the conflict associated with irrelevant notifications received from multiple groups, by recommending the users, posts that are likely to be of interest to them. It also helps to identify the group members intended towards spreading posts that violate group policies. The categorization posts shows increased performance in case of large number of candidate members in a famous group by performing clustering based on linguistic features. The contribution work is to implement location-aware personalized posts recommendation using both the users' personal interests and their geographical contexts. The system has been tested on Facebook group data where it offers a significant solution to an unaddressed problem associated with social networking groups.

Emotion analysis, Multi-level clustering, Psycholinguistics, Sentiment analysis, Stylistics Clustering.

"Linguistic Based and Location-Based Recommending Posts Using Various Clustering Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.407-415, May-2019, Available :http://www.jetir.org/papers/JETIR1905M65.pdf

"Linguistic Based and Location-Based Recommending Posts Using Various Clustering Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp407-415, May-2019, Available at : http://www.jetir.org/papers/JETIR1905M65.pdf