Twitter Big Data Projects

Twitter Big Data Projects

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Twitter Big Data Projects

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About Twitter Data:

  • It holds huge amount of data
  • Million tweets plus meta data available
  • It is in JSON format
  • It can perform some basic big data analysis on the large dataset

          -Tweetiest time zones

          -Busiest zones

          -Common hashtags

Challenges of Twitter Data:

Learning language

  • Unix+SQL
  • Python
  • Tweet API

Dynamic Lookups

  • Filtering junk

          -Justin Bieber

          -Prepositions and pronouns

          -FTW, FML and LOL

  • Normalizing output

          -Android Mobile Browser

          -Twitter for Android

          -Android 4.0.XXX

Big data Analytics

  • Data storage
  • Data processing
  • Data classification

Why we go for Twitter Data?

    Twitter data is more personal, open and expensive for all users. Twitter is a gold mine of data social platform to tweet all users tweets that are pull or public completely. Due to this benefit, a large amount of data gets and run on big data analytics tool. We can pull certain tweet topic within the last 20 minutes or we can pull certain users non-retweeted tweets using Twitter’s API. Through this analysis, we can perform

  • Sentiment Analysis,
  • Syntax Analysis Applications
  • Entity Recognition Analysis
  • Frequency Analysis
  • Segmentation Or Categorization
  • Emotion Level Computation
  • Anomaly Detection Algorithms
  • Geo Location Based Transportation Systems
  • Correlation Analysis (Spatial And Temporal)
  • Spatial Traffic Information

 Tools to Analysis Twitter Big Data:

  • Apache S4
  • BirdSong Analytics
  • Cyfe
  • NodeXl
  • TWChat
  • TweepsMap
  • Audiense
  • Keyhole
  • Twitonomy
  • Twenty Feet
  • Twitter Counter
  • Visible Tweets
  • Tweetstats
  • Twistori
  • Tweepsmap
  • Python 2.7 [Python library called “Tweepy” is used]
  • Hadoop Mahout [Service provider is used]
  • Combination of two technologies [R and Hadoop]
  • Tweeter Streaming API
  • StreamReduce [Apache Storm]

Techniques/Methods to Analysis Twitter Data:

  • Opinion mining approach
  • Emotion detection approaches
  • Latent Dirichlet Allocation
  • Knowledge Extraction Technique
  • Lexicon based method
  • Naïve Bayes Classifier
  • Clustering algorithms

Current Twitter Big Data Projects Topics:

  • Support Advanced Big Data Analytics Using Big Social Data Multidimensional Mining
  • Natural Epidemics and Events Characterization from Twitter
  • Social Data and Conventional Network for Detect Real-Time Earthquake
  • Visual and Textual Features for Twitter Photo Geo-Localization
  • Large Scale Bipartite Rating Graphs Using Scaling Collaborative Filtering by Spark and Lenskit
  • Dependable Services Using Time Series Anomaly Detection in Cloud Computing Systems
  • Top-K Item Frequency and Frequent Items Through Any Sizes Sliding Windows
  • Spatial Traffic Information Based Twitter Sources in Big Data Enabled Self-Organizing Networks
  • Word Embedded Based Clustering to Survey Public Health From Social Media for Twitter Classification
  • Evaluate Mahout Clustering Algorithms Performance Using Twitter Streaming Dataset
  • Normalize Informal Short Text Messages Using Semi-Supervised Probabilistic Method
  • Clustering Appearance Patterns for Detect the Automated Cyber Bullying
  • Twitter Data Visualization and Analysis Using R Language on Hadoop Framework
  • Using Scalable Uncertainty Aware Discovery Scheme Cyber Physical Systems in Big Data Sensing Applications
  • Big Social Data for Earthquake Consequences Now-casting