ANLY482 AY2016-17 T2 Group7: Lawnet

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Exploratory Data Analysis

Text Analytics

Gap Analysis

With reference to Chart 11 in the Exploratory Data Analysis, we have selected 2 databases, Lawnet and Euromonitor to focus on. This is due to the fact that these 2 databases are the most commonly used amongst the Law and Business students respectively, as these 2 schools are the 2 biggest contributors to the searches during the Term. In addition, Marketline has been chosen for further analyses requested by the sponsor.

Chart 13: Text mining process First, we use SAS Enterprise Miner 14.1 to carry out text analytics. We import ‘euromonitor_text_data’ and ‘lawnet_text_data’ respectively by using the File Import function and running though the text mining process in Chart 13: Text mining process.

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Chart 14: Text Parsing Configuration We configure text parsing so that Parts of Speech such as ‘Aux’, ‘Conj’, ‘Det’, ‘Interj’, ‘Part’, ‘Prep’, ‘Pron’ and Types of Attributes including ‘Num’ and ‘Punct’ are all ignored.

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Text Analytics

Euromonitor

Lawnet

Marketline

Lawnet

Among 172363 cases, 36066 are dropped (20.92%). As compared to euromonitor, lawnet has a larger amount of searches.

The most popular search Terms are ‘slr’, ‘ltd’ followed by ‘pte’. These stands for Singapore Law Review, Ltd as in Pte Ltd and Pte as in Pte Ltd respectively. This means that students search for Singapore Law Review a lot.

From the graph above, we noticed that ‘slr’ which stands for Singapore Law Review, is linked to words which are presumably names such as ‘chum tat’, ‘ngiam’, ‘chiew’, ‘chiew hock’, ‘chum’ and the time period ‘1974-1976’. These could possibly tell us the popular cases associated with the Singapore Law Review and the time period for which cases took place in.

From the graph above, we noticed that ‘singapore’ is linked to words such as ‘overseas enterprise’, ‘pte’ (presumably pte in Pte Ltd, the short form for Private Limited), ‘global singapore’, ‘southeast’, ‘finance’, ‘institutional’, ‘law’, ‘ltd’ (presumably Ltd in Pte Ltd) and ‘development bank’.

This is the result shown by function Text Topic.

From the table above, results from Text Topic function shows that “slr”, “wlr”, “teck”, “attorney-general” are of the same topic, this is possibly because people who searched for singapore law review (slr), also searched for world law review (wlr) while the attorney-general is the legal advisor to the government and “teck” could be someone’s name. “sghc”, “bin”, “rahmart” and “iskandar” are of the same topic as “sghc” stands for singapore high court.

The name ‘Rahmart’, ‘bin’ and ‘Iskandar’ is an interesting search Term whereby it features a former policeman of the name ‘Iskandar bin Rahmat’ who was charged for committing double murder at Kovan MRT in 2013. This is a widely known local criminal case which most probably is being used as a prime example of criminal cases in the SMU Bachelor of Laws Curriculum, thereby explaining the popularity of these keywords. More interestingly, the ‘Rahmart’ is in fact a misspelling of the name ‘Rahmat’. This could possibly indicate that majority of the searches for ‘Rahmart’ were performed by users who are not of the Malay descent. Or this could possibly be due to a misspelling from the course material that was provided to the users, presumably Law students.

When we searched “rahmart” or “iskandar bin rahmart”in lawnet we could not find anything as the correct name of the case should be “rahmat”, but SAS Text Miner grouped “rahmart” and “iskandar”, “bin” together so we speculate that many students searched for “iskandar bin rahmart” and found nothing. A recommendation system which will automatically link “rahmart” and the “iskandar bin rahmat” case would be welcomed.

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