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System recommending text content and hashtags to gain the biggest audience on social media

IP.com Disclosure Number: IPCOM000250471D
Publication Date: 2017-Jul-24
Document File: 1 page(s) / 14K

Publishing Venue

The IP.com Prior Art Database

Abstract

Proposed method is for increasing number of followers by identifying interests of followers and cross-checking them with worldwide, regional or country trends to suggests text content and hashtags for new posts.

Two phase systems which in phase one learns what your followers are interested in and what are the most trendy keywords/tags nowadays, and finally in phase 2 advises which topic should be covered in your text/comment and/or which words should be used.

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System consists of several modules: 


Module A: Analyzing interests of your followers 1. Gather tweets and re-tweets from your followers Today, by using Twitter REST API, we are able to gather information such as tweet, information if tweet is

re-tweet or original, tweet creation date, user id, user screen name, user location, user name, user followers count, user friends count etc.

2. Data cleaning and processing pipeline to perform various operations on collected tweets such as

removing stop words, hashtag extraction, concept extraction, grouping and creating proper data structures for analysis.

3. Exploratory analysis to identify top content and hashtags. Module B: Analyzing top trends globally, regionally or on country level. 1. Gather tweets and re-tweets from your followers Today, by using Twitter REST API, we are able to gather information such as tweet, information if tweet is

re-tweet or original, tweet creation date, user id, user screen name, user location, user name, user followers count, user friends count etc.

2. Data cleaning and processing pipeline to perform various operations on collected tweets such as

removing stop words, hashtag extraction, concept extraction, grouping and creating proper data structures for analysis.

3. Exploratory analysis to identify top content and hashtags. Module C: The analyses that we have done in Module A and Module B are merged in this module. 1. Merge the analyses which have been done in Module A and Module B and create proper...