Analysis using social network with Twitter

Twitter is one of the most easily available platform for such analysis. There are ample of things which can be studied, analysed and predict for future.

For instance, understanding the patterns of communication among twitter users, linking between tweets, user - user similarity etc. These needs to be studied within twitter data itself.

Haewoon Kwaak, Changhyun Lee, Hosung Park, Sue Moon, "What is Twitter, a Social Network or a News Media?", WWW 2010, April 26–30, 2010, Raleigh, North Carolina, USA

has been the most popular study. This is the base for understanding of such analysis. Removing spam tweets, user profiling, trending tweets are some of the key factors which can be referred as a good study from this article.

Many of the academic researchers and practitioners are working on this. It can be useful in future if considerable amount of work is done to automate some business units using social media data.


Comments

Post a Comment

Popular posts from this blog

Analysis and Research trends using Word Co-occurrence Network

Image
Co-occurrence networks   are basically used to provide and give a graphic visualization of potential relationships between people, community, organizations, concepts or other entities represented within the form of written material. The generation and visualization of   co-occurrence   networks has become practical with the advent of electronically stored and save the text amenable to   text mining . By way of definition, co-occurrence networks are the collective interconnection of terms based on their paired presence within a specified unit of text. Networks are generated by connecting pairs of terms using a set of criteria defining co-occurrence. For example, terms A and B may be said to “co-occur” if they both appear in a particular article. Another article may contain terms B and C. Linking A to B and B to C creates a co-occurrence network of these three terms. Rules to define co-occurrence within a   text corpus   can be set according to desired criteria. For example, more str
Read more

Significance of Woman in Data Science

Image
Research scientists, regardless of gender, are doing great in this field. This has been evidenced by a great conference " Global Women in Data Science " hosted by Stanford University (livestream) for 75+ locations all over the world on 3rd February 2017. Many talented academic researchers and practitioners have successful connected to make their dreams and goals burst into reality. It has been sponsored by Microsoft, Google, WalmartLabs, SAP and many more A Grade firms.  Fig 1: Global Women in Data Science Another great initiative, Women in Machine Learning   has been hosted on Facebook. Many female potential researchers join hands for research in machine learning. Many of them have achieved great milestones and many are still on the way.  The world is full of talented, successful and fantastic female speakers Women in Machine Learning and Data science are often un-noticable by traditional recruiters and we can turn it down. Another initiative at Meetup.c
Read more

Different types of clustering for textual documents

Clustering Model 1: Nearest Neighbour Algorithms: KD-Trees and Locality Sensitive Hashing Core Machine Learning Concepts: Distance Metrics and Approximation Algorithms Problem Domain: Finding similar documents Nearest neighbour: Find distance between all other documents and query document. Retrieve the document which is closest Critical Component Document Representation Computing Distances Scaling to large dataset: KD Trees - Not appropriate for very high dimensional documents Thus, Locality sensitive hashing is used for approximate nearest neighbour search. Clustering Model 2: Capturing uncertainty in clustering: Mixture Models Any document can be related to t i topic by x%. Can learn users topic preferences Making soft assignments to clusters -Expectation Maximization Clustering Model 3: Latent dirichlet allocation Mixed membership Probability of words in vocabulary Unsupervised learning task
Read more