1. Vocabulary Spectral Analysis as an Exploratory Tool for Scientific Web Intelligence Mike Thelwall Professor of Information Science University of Wolverhampton

2. Contents Introduction to Scientific Web Intelligence Introduction to the Vector Space Model Vocabulary Spectral Analysis Low frequency words

3. Part 1 Scientific Web Intelligence

4. Applying web mining and web intelligence techniques to collections of academic/scientific web sites Uses links and text Objective: to identify patterns and visualize relationships between web sites and subsites Objective: to report to users causal information about relationships and patterns

5. Academic Web Mining Step 1: Cluster domains by subject content, using text and links Step 2: Identify patterns and create visualizations for relationships Step 3: Incorporate user feedback and reason reporting into visualization This presentation deals with Step 1, deriving subject-based clusters of academic webs from text analysis

6. Part 2 Introduction to the Vector Space Model

7. Overview The Vector Space Model (VSM) is a way of representing documents through the words that they contain It is a standard technique in Information Retrieval The VSM allows decisions to be made about which documents are similar to each other and to keyword queries

8. How it works: Overview Each document is broken down into a word frequency table The tables are called vectors and can be stored as arrays A vocabulary is built from all the words in all documents in the system Each document is represented as a vector based against the vocabulary

9. Example Document A –“A dog and a cat.” Document B –“A frog.” adogandcat 2111 afrog 11

10. Example, continued The vocabulary contains all words used –a, dog, and, cat, frog The vocabulary needs to be sorted –a, and, cat, dog, frog

11. Example, continued Document A: “A dog and a cat.” –Vector: (2,1,1,1,0) Document B: “A frog.” –Vector: (1,0,0,0,1) aandcatdogfrog 21110 aandcatdogfrog 10001

12. Measuring inter-document similarity For two vectors d and d’ the cosine similarity between d and d’ is given by: Here d X d’ is the vector product of d and d’, calculated by multiplying corresponding frequencies together The cosine measure calculates the angle between the vectors in a high-dimensional virtual space

13. Stopword lists Commonly occurring words are unlikely to give useful information and may be removed from the vocabulary to speed processing –E.g. “in”, “a”, “the”

14. Normalised term frequency (tf) A normalised measure of the importance of a word to a document is its frequency, divided by the maximum frequency of any term in the document This is known as the tf factor. Document A: raw frequency vector: (2,1,1,1,0), tf vector: (1, 0.5, 0.5, 0.5, 0)

15. Inverse document frequency (idf) A calculation designed to make rare words more important than common words The idf of word i is given by Where N is the total number of documents and n i is the number that contain word i

16. tf-idf The tf-idf weighting scheme is to multiply the tf factor and idf factors for each word Words are important for a document if they are frequent relative to other words in the document and rare in other documents

17. Part 3 Vocabulary Spectral Analysis

18. Subject-clustering academic webs through text similarity 1 1. Create a collection of virtual documents consisting of all web pages sharing a common domain name in a university. –Doc. 1 = cs.auckland.ac.uk 14,521 pgs –Doc. 2 = www.auckland.ac.nz 3,463 pgs –… –Doc. 760 = www.vuw.ac.nz 4,125 pgs

19. Subject-clustering academic webs through text similarity 2 2. Convert each virtual document into a tf-idf word vector 3. Identify clusters using k-means and VSM cosine measures 4. Rank words for importance in each ‘natural’ cluster Cluster Membership Indicator 5. Manually filter out high-ranking words in undesired clusters Destroys the natural clustering of the data to uncover weaker subject clustering

20. Cluster Membership Indicator For a cluster C of documents and tdf-idf weights w ij The next slide shows the top CMI weights for an undesired non-subject cluster

21. WordFrequencyDomainsCMI massey329913640.30587 palmerston90233050.09137 and18835346740.0794 the36051076890.0746 of22638126830.06782 in13179416550.06556 north213484140.06431 students1271785500.05753 research1861615460.05687 a12540046590.05616

22. Eliminating low frequency words Can test whether removing low frequency words increases or decreases subject clustering tendency –E.g. are spelling mistakes? Need partially correct subject clusters Compare similarity of documents within cluster to similarity with documents outside cluster

23. Eliminating low frequency words

24. Summary For text based academic subject web site clustering: –need to select vocabularies to break natural clustering and allow subject clustering –consider ignoring low frequency words because they do not have high clustering power –Need to automate the manual element as far as possible The results can then form the basis of a visualization that can give feedback to the user on inter-subject connections