Title Course opinion mining methodology for knowledge discovery, based on web social media Authors...

Title

Course opinion mining methodology for knowledge discovery, based on

web social media

AuthorsSotirios Kontogiannis

Ioannis KazanidisStavros ValsamidisAlexandros Karakos

PCI 2014 Quality in Education Technologies

Outline


• Introduction

• Method

• Case study

• Results

• Proposed framework

• Proposed opinion mining system architecture

• Discussion and conclusions

Introduction


Primarily, authors used the LMS

platforms of academic institutions

where course knowledge and course

interest collide (apart from the

classroom)

Then the authors focused on student-

course appreciation by using

questionnaires and course grades

The results of this twofold evaluation

were sometimes contradicting

Introduction


Since LMS course evaluation is based

on a scientific evaluation approach,

authors concluded that a similar

approach is needed to replace the

questionnaires with a less guided and

manipulous scientific methodology

That is, knowledge extraction and

discovery from existing social networks

where people express freely and non

guided opposition for an academic

course

Introduction


This paper This paper proposes a framework for

applying opinion mining in social networks.

The goals of the proposed framework is to

(a) extract useful textual information from social networks of blogs regarding learning course activities or processes and (b) apply opinion mining techniques

on the extracted text in order to discover the positive or negative opinions concerning each course.

Framework


The 4 steps for opinion mining in a

social network

Case study


Study population and context

a microblog by following posts relative to the

educational level and institutional services of a Greek

Technological Educational Institute (TEI)

a period of six months

comments of different commentators from the

same department in the Greek language

It can be accessed at https://www.facebook.com/

groups/69887509784/

Case study


View of the experimental

microblog

Case study


1st process Create, train and store the classification model for automatic categorization of text as positive or negative

Case study


2nd process Apply the model to new data to automatically be categorized into positive and negative

Results


Words like “painful”, “discourage”, “damage”,

“unemployment”, etc. were found to have negative sentiment

whereas words like “profitability”, “prosperity”, “interest”,

“success”, etc. were found to have positive sentiment in

comments.

The comments from the microblog of our study were split in

half between positive and negative about the educational

institute offered services and knowledge.

In other words our sample was split evenly on feelings.


Twofold methodology for the evaluation of an academic course LMS course web usage mining evaluation process

with the use of a three tier evaluation architecture and the measures, metrics and algorithms

Opinion mining process Proposed Framework


Opinion mining process Step 1

Source selection and monitoring of UGC sources educational institution channels general source channels

Step 2

Source crawling engine and initialization mechanism -

crawling design and crawled content storage

capabilities

Proposed Framework



Semantic enrichment engine – semantic

enrichment design If the text content is semi-structured, then the use of

either natural language processing (NLP) or other text analysis techniques in order to interpret (grammatically and syntactically process) each sentence, prior to the assignment if possible a sentiment to it

The effectiveness of the different approaches largely depends on the quality of the raw text to be analyzed; in general, NLP and therefore semantic enrichment is effective on syntactically-correct texts while it falls short on ill-formed sentences or when Internet dialects are used

Proposed Framework



Sentiment Analysis processes, metrics and

algorithms This step involves the use of opinion mining over a

adequate level of enriched sentences of user text. For this process to be accurate a very well trained dataset of opposite and negative sentences is required, with a high level of polarity among those datasets

For such functionality to be performed in an automatic and real-time manner or even to be a self trained feedback mechanism, appropriate metrics or scores need to be defined as well polarity judgment algorithms need to be proposed and validated

Proposed Framework



Sentiment Analysis processes, metrics and algorithms

If a polarized dataset of high confidence is pertained, then using a Part Of Speech sentence (POS) tagger (or NLP clustering of a sentence) and a trained Bayesian sentence classifier, we can pinpoint that a sentence tokens belong to a class of sentences by looking at the tokens probability

The likelihood of a sentence can be calculated to be negative as the number of negative sentences in that class over the total number of negative sentences in all classes

Likelihood_sentence_Negative = Number of class negative sentences / Total number of Negative sentences

Proposed Framework



Visualization and courses ranking mechanism

based on opinion results

Proposed Framework


This paper proposes a framework and a testbed system used for

applying opinion mining in blogs regarding course educational content.

a context sensitive sentiment analysis methodology which provides human like sentiment analysis based on semi supervised learning structures

Discussion and

Conclusion


The expected benefits of applying such a framework are the following: Qualitative presentation of people concerns

over a course and course user preferences. Recording user’s problems and negative or

positive user opinions concerning educational courses they are interested in without spatial and temporal restrictions

Discussion and

Conclusion

Discussion


Research Limitations The accuracy of the WSD (Word Sense

Disambiguation) program (OpenNLP) within this approach, so that the exact sense of each term can be identified and exact sentiment scores can be calculated

The framework has been only applied to a specific microblog for a set of three courses. In order to better benchmark it, it must be also applied to other blogs as well

THANK YOU FOR YOUR ATTENTION

Course opinion mining methodology for knowledge discovery, based on web social media

Sotirios Kontogiannis

Ioannis Kazanidis

Stavros Valsamidis

Alexandros Karakos


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Title Course opinion mining methodology for knowledge discovery, based on web social media Authors...

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