INFO 246-10
Information Technology Tools and Applications – Advanced Topic: Web/Text/Data Mining for LIS
Spring 2016 Greensheet

Dr. Geoffrey Z. Liu
Other contact information: telephone: (408) 924-2467
Office location: Clark Hall 418L, SJSU Campus
Office Hours: Email, Blackboard IM, and in-person by appointment

Greensheet Links
Additional Links
Online Resource
Text/Data Mining Tools
iSchool eBookstore

Canvas Information: Courses will be available beginning January 28th, 6 am PT unless you are taking an intensive or a one unit or two unit class that starts on a different day. In that case the class will open on the first day that the class meets.

You will be enrolled into the Canvas site automatically.

Class activities will be carried out in both the Canvas and Blackboard Collaborate (previously known as Elluminate) systems.

Course Description

This course is an introduction to web, text, and data mining from the perspective of library and information services. Students will learn basic concepts, approaches, and practical techniques of web/text/data mining by conducting group topical research and completing one individual mining project (consisting of ten stages/exercises) with Rapid Miner (a free data mining software with extensions for web/text processing).

Course Requirements

Students' performance in this class will be evaluated on the basis of the following assignments,

  • Self introduction (5%)  -- CLO #1, #2
  • Group online discussion (two sessions, 5% each)  -- CLO #1, #2
    • Lead/moderating a thread
    • Participation by responding
  • Group report of topical research (15%)  -- CLO #1, #2, #3
  • Individual mining project (10 stages, 7% each) -- CLO #4, #5
    • RapidMiner installation/configuration | Excel data import/exporting
    • Reading/writing text file | Correlation analysis
    • Web crawling and text extraction | Preprocessing of texts
    • Document clustering | Constructing random set for model training
    • Building | testing a data model (NN/Bayesian classifier)

At the start of semester, students will be randomly assigned into groups (of optimal size five) to complete topical research and conduct online discussion. For the group topical research, each group will choose a topic of focus and work collaboratively to produce a final written report, by reading/summarizing book chapters and reviewing recent scholarly publications.

Throughout the semester, students will conduct two sessions of online discussion in their group forums, to share findings and to comment on related issues. The first session is on the topic "Web/text/data mining in the library and information science fields", and the second session on the topic "Ethical/social issues related to big data and data minig". In each session, every member in a group will create/moderate one thread in the group's forum, by making a lead post. The lead post may be critical review of a scholarly article, digested summary of book chapter, analysis of a related project, or reflection on personal experience/observation. Reference and article PDF should be included when applicable. Others will participate by responding.

Detailed instruction on each stage of the individual mining project will be provided in the Canvas class site, along with other course materials.

Of both the group topical research and the individual mining project, students are encouraged (but not required) to record presentations in Blackboard Collaborate. The presentation recordings may be used later for INFO289 e-portfolio as competency evidence or for job interview as demo of skills.

All written work should be professionally prepared following the APA editorial style and established convention of academic writing, free of grammatical errors and spelling mistakes. Tutorial, assistence, and resources for improving academic writing skills are available at the Writing Resources Center.

It is students' responsibility to submit and maintain the electronic version of their works until the final grade is issued.

Course Calendar
(Brief and tentative. A final/extensive version will be provided in the Canvas class site.)



Individual Tasks & Dues

Group Tasks & Dues


Sat. 1/30


  • Course overview & logistics
  • Teaching approach
  • Groups for researching a chosen topic
  • Textbook -- None Required!

Blackboard Collaborate
(Sat. 1/30 9:00-11:45 PST)

Self Intro (in class forum)





  • What text/data/web (TDW) mining is about
  •  TDW mining vs. Information Retrieval
  •  Toward knowledge/intelligence discovery
  • TDW mining in relation to LIS

Ex-1: Install RapidMiner



Sat. 2/6

Lab Session 1: Initiation on Rapid Minor

(mandatory for whole class)

(Self Intro End)

(Ex-1 completed. No submission)

Ex-2: First Process: Import/export excel data

GR: Propose a topic




Survey of Software Tools

  • TDW mining software
  • Visualization function and setting
  • Selection of software tools

(Ex-2 Due)

Ex-3: Read/write text files

GR: Topic approved



Statistical Data Analysis (for mining) 

  • Data types and the concept of "variable"
  • Plotting and visual data exploration
  • Descriptive and inferential statistical analyses
  • Statistical models & hypothesis testing
  • Correlation and regression analysis

(Ex-3 Due)

Ex-4: Correlation analysis

GD-1: Web/Text/Data mining in LIS



Web Mining (Content and Structure)

  • Web crawling and indexing for searching
  • Web mining vs. full-text IR
  • Content vs. structure vs. hyperlinks
  • Web content mining turned into text mining

(Ex-4 Due)

Ex-5: Crawling & extracting web docs



Sat. 3/5-

Lab Session 2: Web crawling & document extraction

(Hourly timeslots for individual tutoring)





NLP and Text Preprocessing

  • Natural language processing (NLP): lexical, syntactic & semantic
  • Word extraction, stemming, and stop words
  • Term frequency, TF-IDF & term weighting
  • Compiling full texts into "bags of words"

(Ex-5 Due)

Ex-6: Preprocessing texts

GD-1 End (3/14)


Sat. 3/19-

Lab Session 3: Text Preprocessing

(Hourly timeslots for individual meetings)




(Spring Recess 3/28-4/1)





Statistical Text Mining

  • Visualization of document/keyword distribution
  • Theme detection
  • Term association
  • Clustering, categorizing & classification

(Ex-6 Due)

Ex-7: Document clustering



Sat. 4/9-

Lab Session 4: Document clustering

(Hourly timeslots for individual meetings)


GD-2: Ethical/social issues



Data Transformation & Modeling 

  • Overview of data mining process
  • Decision tree and association rules
  • Clustering and classification
  • Time series analysis
  • Neural network training & testing

(Ex-7 Due)

Ex-8: Constructing random training set



Sat. 4/23-

Lab Session 5: NN-based data modeling

(Hourly timeslots for individual meetings)

(Ex-8 Due)

Ex-9: Building data model (NN classifier)

GD-2 End (4/23)



Transaction Log Analysis (Web Analytics)

  • Format and data elements of log entry
  • Preprocessing to extract variables
  • Session segmentation & analysis
  • Search engine query analysis





Competitive Intelligence

  • Corporative needs of intelligence
  • Gleaning of intelligence from public domain
  • Market analysis and research

(Ex-9 Due)

Ex-10: Testing data model (NN classifier)

GR Report DUE




  • Student Recording of Project Presentation (optional)

(Ex-10 Due)



* Ex-#: Stage of individual mining project (exercises); GR: Group topical research; GD-#: Group online discussion.

** Lab sessions 2-5 are individual meetings for 1-to-1 tutoring. Students will sign up for hourly timelots on the specified day(s), using an online appointment management service.

Students' work will be evaluated according to the following specific criteria:

  • Basic content as required (70%);
  • Originality and creativity (20%);
  • Quality of writing (format, rigor, and clarity)  (10%).

Letter grades will be assigned to all assignments, and online discussion will be graded quantitatively based on class/group average counts of postings. The Standard SJSU SLIS Grading Scale will be used to translate letter grades to points and vice versa to calculate a proportionate total of points for the final grade. No extra credit is offered for additional work to make up for missed assignment.

Late submission will not be accepted unless appropriate documentation of legitimate cause (such as unexpected medical urgency and/or personal hardship) for the delay is provided. Request for deadline extension will be treated the same as of Incomplete in accordance to the university/school policy.

Software Requirement

  • Microsoft Excel (version 2009 or later, included in Microsoft Office)
  • Screen capturer (such as the snipping tool of Windows)
  • Rapid Miner (free open source application)

Course Workload Expectations

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Prerequisites

INFO 246 has no prequisite requirements.

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

  1. Describe key concepts and terminologies in the field of text, data, and Web mining.
  2. Describe major approaches and techniques of text, data, and Web mining.
  3. Discuss the roles of text, data, and Web mining in intelligence and knowledge discovery.
  4. Use a software tool to accomplish a reasonably sophisticated text, data, or Web mining task.
  5. Integrate, summarize, and report the findings of mining research.

Core Competencies (Program Learning Outcomes)

INFO 246 supports the following core competencies:

  1. E Design, query, and evaluate information retrieval systems.
  2. G Demonstrate understanding of basic principles and standards involved in organizing information such as classification and controlled vocabulary systems, cataloging systems, metadata schemas or other systems for making information accessible to a particular clientele.
  3. H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.
  4. I Use service concepts, principles, and techniques to connect individuals or groups with accurate, relevant, and appropriate information.


Recommended Textbooks:

  • Feldman, R., & Sanger, J. (2006). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press. Available through Amazon: 0521836573 arrow gif indicating link outside sjsu domain
  • Han, J., & Kamber, M. (2005). Data mining: Concepts and techniques (2nd ed.). Morgan Kaufmann. Available through Amazon: 1558609016 arrow gif indicating link outside sjsu domain
  • Hofmann, M., & Klinkenberg, R. (Ed.). (2013). RapidMiner: Data mining use cases and business analytics applications. Chapman & Hall/CRC. Available through Amazon: 1482205491arrow gif indicating link outside sjsu domain
  • Markov, Z., & Larose, D. (2007). Data mining the web: Uncovering patterns in web content, structure, and usage. John Wiley & Sons, Inc. Available through Amazon: 0471666556 arrow gif indicating link outside sjsu domain
  • Zanasi, A. (2007). Text Mining and Its Applications to Intelligence, CRM and Knowledge Management. WIT Press. Available through Amazon: 1845641310 arrow gif indicating link outside sjsu domain

Grading Scale

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100 A
94 to 96 A minus
91 to 93 B plus
88 to 90 B
85 to 87 B minus
82 to 84 C plus
79 to 81 C
76 to 78 C minus
73 to 75 D plus
70 to 72 D
67 to 69 D minus
Below 67 F


In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

  • C represents Adequate work; a grade of "C" counts for credit for the course;
  • B represents Good work; a grade of "B" clearly meets the standards for graduate level work;
    For core courses in the MLIS program (not MARA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation.  You must repeat the class the following semester. If -on the second attempt- you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  • A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA).

University Policies

General Expectations, Rights and Responsibilities of the Student

As members of the academic community, students accept both the rights and responsibilities incumbent upon all members of the institution. Students are encouraged to familiarize themselves with SJSU's policies and practices pertaining to the procedures to follow if and when questions or concerns about a class arises. See University Policy S90-5 at More detailed information on a variety of related topics is available in the SJSU catalog at In general, it is recommended that students begin by seeking clarification or discussing concerns with their instructor. If such conversation is not possible, or if it does not serve to address the issue, it is recommended that the student contact the Department Chair as a next step.

Dropping and Adding

Students are responsible for understanding the policies and procedures about add/drop, grade forgiveness, etc. Refer to the current semester's Catalog Policies section at Add/drop deadlines can be found on the current academic year calendars document on the Academic Calendars webpage at The Late Drop Policy is available at Students should be aware of the current deadlines and penalties for dropping classes.

Information about the latest changes and news is available at the Advising Hub at

Consent for Recording of Class and Public Sharing of Instructor Material

University Policy S12-7,, requires students to obtain instructor's permission to record the course and the following items to be included in the syllabus:

  • "Common courtesy and professional behavior dictate that you notify someone when you are recording him/her. You must obtain the instructor's permission to make audio or video recordings in this class. Such permission allows the recordings to be used for your private, study purposes only. The recordings are the intellectual property of the instructor; you have not been given any rights to reproduce or distribute the material."
    • It is suggested that the syllabus include the instructor's process for granting permission, whether in writing or orally and whether for the whole semester or on a class by class basis.
    • In classes where active participation of students or guests may be on the recording, permission of those students or guests should be obtained as well.
  • "Course material developed by the instructor is the intellectual property of the instructor and cannot be shared publicly without his/her approval. You may not publicly share or upload instructor generated material for this course such as exam questions, lecture notes, or homework solutions without instructor consent."

Academic integrity

Your commitment, as a student, to learning is evidenced by your enrollment at San Jose State University. The University Academic Integrity Policy F15-7 at requires you to be honest in all your academic course work. Faculty members are required to report all infractions to the office of Student Conduct and Ethical Development. The Student Conduct and Ethical Development website is available at

Campus Policy in Compliance with the American Disabilities Act

If you need course adaptations or accommodations because of a disability, or if you need to make special arrangements in case the building must be evacuated, please make an appointment with me as soon as possible, or see me during office hours. Presidential Directive 97-03 at requires that students with disabilities requesting accommodations must register with the Accessible Education Center (AEC) at to establish a record of their disability.

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