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On Summarization and Timeline Generation for Evolutionary Tweet Streams
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On Summarization and Timeline Generation for Evolutionary Tweet Streams

Category : Data Mining


Sub Category : DOTNET


Project Code : ITDDM09


Project Abstract

ON SUMMARIZATION AND TIMELINE GENERATION

FOR EVOLUTIONARY TWEET STREAMS

 

 

ABSTRACT

       In this paper, we propose a novel continuous summarization framework called Sumblr to alleviate the problem. Short-text messages such as tweets are being created and shared at an unprecedented rate. Tweets, in their raw form, while being informative, can also be overwhelming. For both end-users and data analysts, it is a nightmare to plow through millions of tweets which contain enormous amount of noise and redundancy. In contrast to the traditional document summarization methods which focus on static and small-scale data set, Sumblr is designed to deal with dynamic, fast arriving, and large-scale tweet streams. Our proposed framework consists of three major components. First, we propose an online tweet stream clustering algorithm to cluster tweets and maintain distilled statistics in a data structure called tweet cluster vector (TCV). Second, we develop a TCV-Rank summarization technique for generating online summaries and historical summaries of arbitrary time durations. Third, we design an effective topic evolution detection method, which monitors summary-based/volume-based variations to produce timelines automatically from tweet streams. Our experiments on large-scale real tweets demonstrate the efficiency and effectiveness of our framework.

 

 

 

 

 

 

 

 

 

 

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In existing we focus on static and small-sized data sets, and hence are not efficient and scalable for large data sets and data streams.

To provide summaries of arbitrary durations, they will have to perform iterative/recursive summarization for every possible time duration, which is unacceptable.

Their summary results are insensitive to time. Thus it is difficult for them to detect topic evolution

PROPOSED CONCEPT:-

We introduce a novel summarization framework called Sumblr (continuouS sUMmarization By stream cLusteRing). To the best of our knowledge, our work is the first to study continuous tweet stream summarization.

Proposed a scalable clustering framework which selectively stores important portions of the data, and compresses or discards other portions.

We focus on extractive summarization. Extractive document summarization has received a lot of recent attention. Most of them assign salient scores to sentences of the documents, and select the top-ranked sentences.

EXISTING TECHNIQUE:-

Stream Data Clustering

PROPOSED ALGORITHM:-

TCV-Rank summarization algorithm

TECHNIQUE DEFINITION:-

CluStream is one of the most classic stream clustering methods. It consists of an online micro-clustering component and an offline macro-clustering component.

ALGORITHM DEFINITION:-

This algorithm computes centrality scores for tweets kept in TCVs, and selects the top-ranked ones in terms of content coverage and novelty.

DRAWBACKS:-

Stores all the data.

Not efficient and scalable.

ADVANTAGES:-

Stores important parts of data.

It is highly efficient and scalable.

 
 
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