This was a 3-day assignment that I worked on while I was in the Analytics program at Northwestern University. It is an implementation of the Multinomial Naive Bayes Algorithm in Java. Text Analytics was by far my favorite course at the program and I thoroughly enjoyed working on this one. Hope you guys like it and is helpful. Suggestions/comments/criticism are welcome!
Problem: Classify books based on their Title name, Author Name, and Content into pre-defined categories. The categories were:
Input data format:
First line contains N, H where N = number of training data records and
H = list of headers. N lines of training data will follow this. Each
field in N lines is tab separated. The next line will have M, H where
M = number of test data records and H = list of headers. M lines of
test data will follow this, each field in a line is tab separated.
Training data has following columns:
categoryLabel, bookID, bookTitle, bookAuthor
Test data has following columns:
bookID, bookTitle, bookAuthor
Example of training data:
N=3 H=[categoryLabel, bookID, bookTitle, bookAuthor]
AMERICAN HISTORY b9418230 American History Survey Brinkley, Alan
SOCIOLOGY b16316063 Life In Society Henslin, James M.
ENGLISH b14731993 Reading for Results Flemming, Laraine E.
M=2 H=[bookID, bookTitle, bookAuthor]
b15140145 Efficient and Flexible Reading McWhorter
b15857527 These United States Unger, Irwin
A list of all books from the Test dataset with their Book Ids and their Predicted Category.
For the given document classification problem, I decided to implement Multinomial Naive Bayes model. Classification process: classify(feat1,…,featN) = argmax(P(cat))*PROD(P(featI|cat)). I implemented this in Java (using Eclipse). Here features are words.
- – Multinomial (a document is represented by a feature vector with integer elements whose value is the frequency of that word in the document) preferred over Bernoulli (a document is represented by a feature vector with binary elements taking value 1 if the corresponding word is present in the document and 0 if the word is not present)
- – Laplace’s law of succession or add one smoothing included to eliminate possibility of zero probability
- Read the input data and split it into Training dataset and Test dataset
- Built the Multinomial Naïve Bayes Classifier using the Training dataset
- I first started with considering just the ‘title’ field to build and classify documents (excluded Stop words and normalized the remaining words)
- Next, I tried using ‘title’ and ‘categoryName’ fields to build and classify documents
- Then, I tried using ‘title’, ‘categoryName’ and ‘author’ fields to build and classify documents
- Lastly, I tried combinations of ‘title’, ‘author’ and ‘contents’ fields to build and classify documents
- Also, I experimented by excluding the ‘categoryPriorProbability’ in the final computation
- The Results of each of these are summarized below
- Classified the documents in Test dataset using this classifier
- categoryName – Name of the Category
- categoryProbability – Prior Probability of the Category
- wordProbability – HashMap of (word-probability) pair of the ‘title’ field of the documents in the category where probability is the category conditional probability of that word
- authWordProb – HashMap of (word-probability) pair of the ‘author’ field of the documents in the category where probability is the category conditional probability of that word
- contentWordProb – HashMap of (word-probability) pair of the ‘contents’ (table of contents from input2.txt) field of the documents in the category where probability is the category conditional probability of that word
- Methods to ‘set’ and ‘get’ each of these attributes
- probabilityCalculation – Calculates P(feat(i)|C) – the probability of feat(i) occurring in that document class
- readData – to read in data from both input files and split the first into training and test datasets
- buildClassifier – Builds the classifier. Creates an array of category objects, each of which has the Vocabulary of features (feat) and P(feat(i)|C) – the probability of feat(i) occurring in that document class (done in the class function) – for ‘title’, ‘author’ and ‘table of contents’. Also calculates the category prior probabilities
- createWordList – take inputs as String of words and they are tokenized and normalized using an English Analyzer
- classifyDocuments – Classifies the documents into a category which has the class conditional probability. Words are selected from ‘title’, ‘author’, ‘table of contents’ columns separately (only words from ‘title’ used in the final implementation) for each document in the test dataset and corresponding (P(cat)*PROD(P(feat(i)|cat)) are calculated. Finally, classify(feat1,…,featN) = argmax(P(cat)*PROD(P(feat(i)|cat))
- The final implementation achieved an 86.67% accuracy (52/60)
- This final model had only the ‘title’ field considered to build and classify the documents
- The prior category probabilities was not included in the P(C(i)|D(k)) calculation
- Started using the contents of the books but wasn’t too helpful in improving the accuracy (more time and appropriate tweaking of the model might result in improvement of accuracy)
The above table is compiled from the training dataset
|Trial #||Fields included||Accuracy|
|Title||Author||Category Name||Prior Probabilities||Words Tokenized||Numeric values in fields|
- The first trial was based only on ‘Title’ using the standard formula of Naïve Bayes model. When I observed a few misclassifications, I found that there were some documents which had the word “Historical” and yet wasn’t categorized in “American History”. So I thought I could include the categoryNames as a part of the Vocabulary (Trial #2)
- As we see there wasn’t any significant change in the overall model accuracy. Hence decided not to use it
- Also, in trial #1, I had Normalized (excluded stop words) all the words that appear in the title of the documents in the training dataset during building the model and classification of the test data. Hence I tried retaining the words as they were and the accuracy dipped. Hence Normalization helped
- Including ‘Author’ field didn’t improve the results. In fact deteriorated it further.
- Exclusion of numeric occurrences in the fields doesn’t improve accuracy either (numeric years help in prediction)
- There we a few documents that were being misclassified narrowly and this was because of the prior category probabilities (one was far greater than the other and without prior probability the classification of that document would have been correct). Hence I decided try out by excluding prior category probabilities and the accuracy considerably improved and that was the best I could get from these experimentation (87%)
I have come up with this post so that it can serve as a source of information about the Northwestern University’s MS in Analytics (MSiA) program beyond what is provided on the official university website: http://www.analytics.northwestern.edu/. If you are interested in this program, I recommend you to go through the official website thoroughly before going through this post.
I hope this post answers the questions you have in your mind and outside it. If there are still any questions which are not touched upon here, then please do leave a comment and I shall try answering it for you.
NOTE: ALL THE ANSWERS MENTIONED BELOW ARE STRICTLY PERSONAL BASED ON MY UNDERSTANDING, EXPERIENCE AND DISCUSSIONS WITH THE ADMINISTRATORS AND STUDENTS HERE.
1) Background and skills relevant to this program
· A background (education/work) in any of the fields like Economics/Econometrics, Math, Stats, Computer/Information Science/Technology, Business Administration, etc. is relevant to this program. This because Analytics is primarily a combination of Business, Math and Technology. To evaluate whether you are a right candidate for this program you could ask the following questions to yourself and research to get the answers for it:
- What do I know about Analytics?
- Why Analytics for me?
- Where is it applied?
- Is it aligned to my career goals?
- Where do I see myself in future after this program?
- What skill-set do I already possess and what would I need to develop to be successful in this field?
2) What is acceptance criteria?
· According to me the following are the criteria on which the Admission Committee would base their decision on:
- Education or courses taken in under grad in a relevant field (Economics, Math, Stats, Computer/Information Science/Technology, Business Administration) class distribution can be found here: http://www.analytics.northwestern.edu/current-students/index.html
- Performance in undergrad will add a lot of weight to your case (Good acads*, Good University*)
- Relevant work experience – if any (any work-ex related to working with data, technology or business management)
- Need some prior exposure to computer programming (for applicants from non-computer science background)
- GRE/GMAT test scores (2015-16 batch will be the first one for which standardized test score will be considered. So hard to provide a benchmark for this one at this stage)
- Needless to say that your SOP has to be top notch and very convincing; Resume very professional and of really high standards; Recommendations that are aligned with your case from reliable and from credible supervisors/colleagues
*see next question
3) But do they really focus of undergraduate grades/university for international students?
· Since there is no direct conversion for international grades to US 4-pt GPA, I do not think they would focus too much on your grades from your under-grad as long as you are above the 3.0/4.0 cutoff or equivalent of that (there are various avenues where you can get this conversion done if you are an international student and I think if you have more than 50% from any of the Indian universities then you should be good). Their main focus is your suitability to the program through your under-grad courses and/or your work-experience and how your career goals align to this field etc.
4) How important is work experience to get into this program?
· There are students in both the cohorts thus far who were just out of their under-grad school with minimal work experience (internships) when they joined this program. But all of them had relevant educational background required for this program. So work experience is not necessary but a relevant one would definitely add a lot of weight to your application.
5) Pre-requisites courses or preparation required before joining MSiA program.
· There are no pre-requisites as such since students come from really diverse background. But knowledge of elementary stats, probability and calculus is pretty important. So do it whenever you get time. More information can be found here: http://www.analytics.northwestern.edu/prospective-students/index.html
6) Placements at MSiA
· Job prospects are excellent after graduating from MSiA. Most of the guys from the first cohort of 2012-13 have got multiple good offers across industries like finance, technology, retail, insurance. If you have any specific questions or queries regarding jobs then you can try getting in touch with them or the program director/asst. director. This page will provide more information: http://www.analytics.northwestern.edu/current-students/career%20placement.html
7) Program expenses (entire duration excluding internship period)
· 60k-61k (tuition fees) + 3k (Health insurance) + 10k (off campus expenses) + additional 2k (Text books, travel, partying, etc.) – rough estimates and varies from person to person. Tuition fee is $15,038 per quarter for 2013-14 (3 Qs). You can expect it to increase by another 5% may be for the Fall quarter of next year (cant bet on this!). So all together it will be around $60-61k as tuition fee. Additional costs would be a $3.4k annual health insurance for international students and living expenses (staying off-campus is cheaper average rent per month per person could be anywhere between $400 and $600). One can stay on-campus which generally is more expensive than staying off campus (http://www.northwestern.edu/living/). Most text books have either an e-version or are available in the library. So you can expect the buying of textbooks to be minimum. For more information: http://www.analytics.northwestern.edu/prospective-students/tuition-and-fees.html.
8) Paid internships, on-campus jobs and assistant-ships.
· There are on campus opportunities here which you can research on the University website. Also there are a few guys who work part-time for companies (paid). As far as my knowledge goes there are no assistant-ships available but you can always mail Lindsay (email@example.com) and enquire about it. There are 7 scholarships (50% tuition waiver) also provided by the NU for this program but I am not sure what is the basis on which they provide these (I personally feel it is for the early applicants). For more information: http://www.analytics.northwestern.edu/prospective-students/tuition-and-fees.html
9) Industry exposure and relevance in this program
· MSiA is a professional program instituted to cater to the growing industry demand of skillful Analytics professionals – the dearth of which is plaguing small and large businesses alike. Hence this program is structured in a way so the industry exposure is maximized enabling students to directly apply the concepts learned in the classroom out in the real world. The following link gives more information: http://www.analytics.northwestern.edu/prospective-students/index.html expressed
10) Eligible for OPT
· Yes, it is eligible of OPT since it comes under the STEM category
11) Acceptance rate at MSiA?
· I do not have an exact figure to quote but all I can say is that there are very few established pure-analytics programs in this country (and probably the world), though this count is increasing year over year, and MSiA is one among them. But at the same time it’s a new field and yet to reach its peak in terms of popularity among candidates unlike, say, a computer science program. So if you have a great case for yourself on why you should be admitted to the program and if you satisfy the necessary pre-requisites mentioned above, then you can definitely get through.
12) Which are the other universities that offer Analytic degree programs?
· As mentioned earlier, there is a huge surge in Analytics related programs in the US. A few years back there were only a handful that offered a full-fledged degree concentrated purely on Analytics and candidates didn’t have a lot of options to get a degree in this field. But this is changing at a rapid pace. The following link gives you a great overview of all the analytics degree programs in this country: https://analytics.ncsu.edu/?page_id=4184
13) For more FAQ’s: http://www.analytics.northwestern.edu/prospective-students/faqs.html
This is one of the projects that I worked on with Ling Jin (@ljin8118) and Peter Schmidt (@pjschmidt007)as a part of our Text Analytics course at Northwestern University. It was one of the most exciting projects to have worked on and in the process learnt the latest and cutting edge techniques used in the field of Text Analytics and Text Mining. Hope you will enjoy it as much as we enjoyed doing it! Cheers 🙂
Provide a textual analysis of the movie script, The Dark Knight, which was robbed of the best picture Oscar at the 81st Annual Academy Awards on February 22, 2009. All project team members are still bitter about this fact. This assignment hopes to resurrect the greatness that is The Dark Knight.
More seriously though, if given a script, the text analytics conducted in this assignment would be able to produce insights into the genre, mood, plot, theme and characters. Ideally the analysis is intended to understand and answer the who, what, when, where and why in regards to a movie.
Specifically, the objectives of the textual analysis of The Dark Knight will cover:
- Determine the major characters in the script
- Show the character to character interaction
- Provide insights into sentiment by character
- Show how sentiment changes over time
- Determine major themes/topics of the script
- Acquire the movie script of choice
- Parse the script into lines by scenes and lines by character
- Tokenize, normalize, stem the lines of dialogue as appropriate
- Build an index based on available components for subsequent queries
- Perform part of speech (POS) Tagging on the lines of dialogue
- From the POS Tagging, perform sentiment scoring on the lines of dialogue
- Perform named entity recognition
- Perform co-reference
- Perform topic modeling
- Analyze results
- Produce visualizations
The above two visuals carry the same information, just two different representations, about the important characters in the movie. The first visual is a Bubble chart where the size of the bubble is proportional to the # of lines said by the character.
The second one is a Heat map diagram where again, the area represents the quantity of lines of dialogue across scenes by characters. These two visuals help us identify the major characters of the movie. One can see that Harvey Dent (aka Two-Face), Gordon, The Joker, Bruce Wayne, Batman, Rachel, Fox, and Alfred were easily the major characters of the movie, with Lau, Chechen, Maroni and Ramirez all playing supporting roles. It is interesting to note that in the script, Two-Face is never named as a separate character, unlike Bruce Wayne and Batman. Combining Bruce Wayne and Batman’s line would have made him the most prominent character over Harvey Dent.
Now that the major characters are established, the next obvious step would be to identify how these characters interact with each other.
The above visual gives us an insight into this. Each node is a character and each edge tells us that the two nodes connected by that edge have interacted at least once in the movie. Our definition of interaction is when two or more characters speak in a single scene. Hence more the number of interactions with distinct characters, bigger will be the size of the node.
The nodes (characters) marked in Red are the central characters. Most of the characters whom have a lot of dialogues also have more interactions with distinct characters. But are there some exceptions, i.e. are there characters who have a lot of lines but less interactions (may be someone like Alfred – having watched the movie) or vice versa? Let’s look further to see what was observed.
Sentiment over time
Below is a visual description of the sentiment of the scenes over time. The methodology to calculate the sentiment for each scene was to first split each scene into dialogues by individuals. Then each dialogue was run through the Design process explained above. At the end of it we get a score for each dialogue and an average of senti scores of all these dialogues gave us the senti score of the scene. As we can see, this was a dark dark movie.
We also looked how character sentiment varied over time. Again the methodology to calculate this was similar to the one above, but this is by character and not by scene.
BATMAN vs. JOKER
What does the Batman say?
As the superhero in this movie, Batman does not talk that much (based on the IDENTIFICATION OF IMPORTANT CHARACTERS and part the real movie). He does mention his opponents, all the killings and of course the word “hero
What does the Joker say?
The joker talks quite often actually, which was confirmed earlier. He talks about his scar /the smile, his childhood and the whole plan stuff. He also mentions all the names quite often.
Design and Implementation Challenges
One of the first things was to find an appropriate script, which turned out to be a little harder than expected. It was sort of like finding a needle in a haystack. But after some perseverance, The Dark Knight Script was found at:
There were 8704 actual lines that needed to be parsed and fit together in the above script.
There were several nuances that needed to be taken into consideration for the actual script parsing. First of all, the script that was found was not broken down by multiple html tags representing the different portions of the script. Instead, the entire script was basically under one tag, which meant parsing was for an entire block of unstructured text. Hence we had to carefully find patterns and parse the script.
Tokenization and Lemmatization
The Standard Analyzer in the Stanford NLP was chosen to handle the tokenization and any normalization required. It also provided lower case and stop word filtering. As it was decided that stemming was not going to be necessary for the analysis that was to be conducted, the Standard Analyzer was chosen over the English Analyzer as the aggressive stemming performed by the PorterStemFilter was not necessary to support the other downstream pipeline processes. The Standard Analyzer was then used consistently across the pipeline to prevent any inconsistency concerns.
POS and Sentiment
There were a couple of options available to perform sentiment mining on the dialogue in the script.
The initial selection was to use SentiWordNet, http://sentiwordnet.isti.cnr.it SentiWordNet is a lexical resource that is based on WordNet 3.0, http://wordnet.princeton.edu and is used for opinion mining. SentiWordNet assigns a score to each synset, defined as a set of one or more synonyms, of a word for a particular part of speech. The parts of speech in SentiWordNet are defined as:
a = adjective
n = noun
r = adverb
v = verb
Obtaining the parts of speech from the Stanford NLP part of speech annotator would then require mapping from the parts of speed defined in the Penn Tree Bank Tag set, http://www.computing.dcu.ie/~acahill/tagset.html, to the part of speech defined in SentiWordNet so that a sentiment score could be produced.
The SentiWordNet resource is constructed as follows:
POS = part of speech
ID = along with pos, uniquely identifies a WordNet (3.0) sunset.
PosScore = the positivity score assigned by SentiWordNet to the synset.
NegScore = the negativity score assigned by SentiWordNet to the synset.
SynsetTerms = terms, including the sense number, belonging to the sunset
Gloss = glossary
Note: The objectivity score can be calculated as: ObjScore = 1 – (PosScore + NegScore)
Another option instead of SentiWordNet was to use the sentiment annotator in the Standford NLP pipeline. The team discovered this new addition to the Stanford NLP during the course of the project. It is recent “bleeding edge” sentiment technology that Stanford is now including the Stanford NLP. Excerpted from there website, as most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. Which by the way in essence is what we were doing. That way, the order of words is ignored and important information is lost. In contrast, the sentiment annotation that is part of the Stanford NLP institutes a new deep learning model that builds up a representation of whole sentences based on the sentence structure, computing the sentiment based on how words compose the meaning of longer phrases. There are 5 classes of sentiment classification: very negative, negative, neutral, positive, and very positive.
There were several methods available in calculating a sentiment for character lines in a given scene. This is due to the fact that the actual “sense” of the word was not known when passed to the parser to do the actual sentiment. So if “good” had n-senses in the lexical resource, it was not known which sense was used in the dialogue.
The first method was to sum all the senti scores within the body of text then divide by the sum of all scores. This was the method that was provided in the demo on the SentiWordNet web site.
The second method was to sum all the senti scores within the body of text then divide by the count of all scores.
The third method was to just sum all the senti scores within the body of text. Although we actually implemented both options for obtaining sentiment (i.e SentiWordNet and Stanford NLP sentiment annotation), the option that was chosen to score the dialogue was SentiWordNet. The scoring method that was then used, although each were explored, was method 2 defined above. This is the method we finally decided to use in our sentiment analysis of the script.
Text analytics is quite the involved process. As with most data analysis activities a major portion of the time is spent identifying, acquiring and cleansing the source data. The field of text analytics is quite broad with many best of breed components. However, text analytics does not have well integrated toolsets, so you can observe from the solution that was crafted in having to leverage several technologies (Java, R, Excel, Gephi, Tableau) using different libraries (Stanford NLP, Lucene) and various other packages to perform specific functions within the data pipeline. All in all, though, it has been shown that with some blood, sweat and tears (over 1200 lines of code were written for this assignment), and by all means time, a text analytics tool can be built to analyze movie scripts with a pretty accurate view when compared to the overall reality of the movie. And lastly, it should be mentioned, then the inherent complexities in the dialogue and the richness of the script should have guaranteed the Oscar for The Dark Knight!
WHY SO SERIOUS?
It’s been more than 2 and half years since we started out with our initiative ABC (www.abc-org.blogspot.in) and I am happy as well as a little disappointed on how the organization has churned out to be during this period. Happy for the fact that it still exists (yes, trust me it’s pretty easy for such initiatives to die down soon after the initial burst), is bigger (in terms of the projects that we have been undertaking) and is functioning better as well (for the school we have been associated with during this time). And disappointed because we had envisaged it to become a larger organization (in terms of the no. of members) and one with a larger reach (in terms of no. of schools we helping out with). But at the end of the day I am glad that at least the people who are still actively participating in ABC’s activities are really committed and are self-driven in this common journey of ours.
During this time, we have undertaken a number of activities for the kids of these schools like
- Stationery Drive
- Shoe Drives in 2010 and 2013
- Annual scholarship program for the 7th std kids
- Annual sponsorship of 10th std. students’ exam fees
- Water filter project, the biggest project we have undertaken
to name a few…
But the main objective of starting this organization was to provide good quality education to these kids, similar to the one which the likes of me were blessed to have, and we are definitely strive towards it.
But during this time I also started realizing that we, a group of about 5-7 friends, were able to pull off something that helped improve, in whatever little way, the studying conditions of more than 400 kids.
They had uniforms and shoes to wear, pens and pencils to write and give their exams, money to pay their exam fee and access to clean drinking water. This triggered a thought in my mind that why can’t then each group of friends pick up a small school around their locality and provide whatever support, financial if not anything else, they can to improve the studying environment of the school? Because I believe that in this context and at the juncture where India is today, something is definitely and always better than nothing.
From my little experience and interactions with people across all age groups and different backgrounds, everyone has been appreciative when I have talked about ABC to them and most of them also expressed a desire that they too want/wanted to do something like this. This shows that people are grateful to our society and have a sense of giving-back to it from which we have received so much. But still only a small percentage of these people actually venture out and do something about it. So who/what is stopping them?
The most common reasons I can think of that people give themselves, more than anyone else, are: lack of time and money, and a feeling that a lot has to be sacrificed to do something in this area. But according to me each one of them is not relevant today.
As I see it, our country is definitely more prosperous, especially the middle class, compared to where the nation was a couple of decades ago. More people are better educated, have stable jobs, good salaries; more members of the family (women mainly) are working hence increasing the household income as well. Hence the youth coming from this class definitely can’t complain of both lack of money and time. I am not saying that the youth are filthy rich or they have all the time in the world. They do have career ambitions, work/study and certain other responsibilities to fulfill as well. In fact, none of us in ABC are any different. But we also know that we can definitely spare some time off to fulfill our responsibilities in this regard.
Then there is SACRIFICE, something that is commonly associated when it comes to serving the society. Of course, it is true and I greatly appreciate the likes of Anna Hazare and Medha Patkar for giving their entire lives for the upliftment of people, but if you don’t intend to do that but still wish to contribute in your own little way, then that is something not wrong at all. And I assure you, after having been a part of the activities undertaken by ABC, I haven’t sacrificed anything at all. Not studies, job, family, friends, fun or anything else. All you need to have is the willingness to take up something like this and then the drive to continue doing it. Other practical issues such as finance and funds for bigger projects, the right place to start etc will automatically be eliminated as hindrances.
So now, what exactly can a group of friends do?
1) They can just visit a nearby school which is not in a good state. Talk to the principal and enquire what are the problems that the school children are facing in getting the right education
2) Then you can see where are the places that you can fill in – teaching something I personally don’t advocate being done unless it is a very structures and professional. Else it is of no use. But again if someone believes otherwise
3) Filling in the infrastructure voids is definitely where each one of us can contribute towards. The projects that we have done at ABC are testaments of that. Every year thousands of students, many of them bright and talented, are not able to write their annual exams just because their families can’t afford his/her exam fees. And what’s the cost of the exam fee? A few hundred rupees at max. Can’t we fill in this void to at least ensure kids are able to give their exams? Kids walk bare footed to schools; can’t we provide them with a pair of shoes which hardly costs a 100 bucks but can go a long way in enabling that kid to go to school study and play!
4) Next thing that come to your mind is finance since you can’t provide only one kid with shoes. What if there are about 100 children in the school that doesn’t have shoes which means you need to raise 10k bucks. And I think that shouldn’t be a really big problem either since I don’t think it’s hard to find about 15-20 people in our circle family and friends and in this era of social networking who can contribute 500-1000 buck each to buy these shoes
5) And finally on a very selfish level, there is always this nice feeling of playing your part in nation building. Also it is a great reason for all your friends to meet up more regularly and enjoy even more!!! A definite win-win situation as I see it.
This is what according to me is the power and potential of the youth and friendship in particular. I hope I have made sense in whatever I have jotted down and it appeals to all who read this.
April 2008: That was the month when began the greatest sporting championship the country had ever seen, The Indian Premier League or better known as the IPL.
Based on the concept of the hugely successful and popular English Premier League for football in England, the IPL broke all the traditional barriers to embrace the latest, shortest and the most exciting form of one of the oldest field sports recorded in the history of mankind. The formation of the IPL was something similar to the story of State Bank of India (SBI) eventually embracing ATMs in India. Well, the story goes something like this:
SBI initially criticized and ridiculed the concept of ATMs, saying that in a country with so many illiterates, people living in villages, lack of lawlessness in many parts of the nation etc, a person using a card with protected magnetic strip, interacting with a machine to withdraw money, and that too safely, is definitely going to be a flop show. But other banks thought otherwise and started setting up their own ATMs in various parts of the country. Yes, there were isolated incidents of loot outside ATMs, people not being comfortable dealing with the machine etc, just like the problems faced initially when a new system is introduced. Slowly and steadily there was a rise in the popularity of the ATMs and banks having more ATMs started becoming more profitable as well. This was when SBI realized its folly and found itself lacking in terms of the competition with other newer and smaller sized banks and thus accepted this wonder invention and how. Today SBI by far has the maximum no. of ATMs in the country and is one of the few govt. owned institutions in the country to be hugely successful and giving other private banks a run for their money!
Indian cricket too treaded on the same path. After the wonder invention of the T20 cricket, the board dismissed it as a too-short-for-cricket format and a diluter of the traditional 5-day game, the TEST cricket which still remains the ultimate test for a cricketer. It was 2007 and the mood of the most spectacular event in cricket was about to start, the ICC World Cup. India sent a decently strong team under the leadership of Rahul Dravid. But India’s hopes of making it to the knockout stages were dashed in the very first match itself after losing against Bangladesh. It was a big flop show from the Indian cricket team and which in turn flopped the whole of WC itself considering that even Pakistan crashed out unceremoniously. But the team got a great chance to heal its World Cup wounds by doing nothing but performing better in the upcoming World Cup, only that it was different ball game (not literally!) since it was the T20 WC which the Indians neither had favored since its inception nor did they have enough experience in the format. The experienced players took a back seat and withdrew their participation from the tournament and a young team under a young leader was sent to the competition. And rest as they say is history and one which no one expected. The T20 WC came home and opened the eyes of many people against it and showed the potential it held in this country.
In the mean time came the Indian Cricket League (ICL) as well. The ICL went against the all powerful BCCI to start a T20 cricket league of its own. BCCI used its entire mite and even threatened the people who showed interest in being associated with the league that they would be banned for life from playing matches for their country or their respective domestic tournaments. Still the ICL happened, defying all the warnings and threats from cricket boards. It made a lot of noise and had its fair of success in terms of viewership but more importantly it sent a message to the BCCI that cricket without it can happen! Suddenly the young lot Indian cricket started getting lured by this new league and undermined the threats issued by the board. National prospects quit domestic cricket and participated in the league. This was the state not just in India but even outside. Former and current overseas players too started giving up their desire of playing for their nation by accepting the lucrative offers of the ICL. Kerry Packer was again back to haunt cricket since he had treaded on a similar path back in the late 70s which ruined careers of many bright cricketers like Tony Grieg. This was too much for the egoistic BCCI to be mum and meekly accept defeat. And to merely teach the ICL a lesson for its deeds, BCCI came up the concept of IPL. Loosely based on the functioning of the English Premier League for football in England, it was an instant success amongst the players, team-owners and fans all around the world.
April/May 2011: IPL is still alive and kicking in its 4th season. Just like the SBI, the BCCI too took time to realize the potential of a new invention. But after the realization the invention was taken to an altogether different level in terms of its reach!
Now where does hockey come into the picture? Once the pride of our country, hockey today has been relegated to merely being called the national game in different school text books but not living up to the name and benchmarks set by the predecessors in this beautiful and enthralling game. The game is definitely not getting its due and it is high time we started taking it seriously and bring back the lost glory. Hockey needs a large scale revamp to match to the popularity that cricket has managed to garner. And IPL is one great model which the hockey authorities can emulate and create a league of its own. Yes, it was tried previously and didn’t succeed to the expected levels but the way it organized can be changed with the IPL model coming into the picture. 2 things about hockey that need urgent attention are: improving the image of the game and attracting youngsters pick up the “stick” and not the “bat”. Short sports have always been more exciting than their longer-duration cousins. Still cricket has managed to become popular amongst the young and the old alike in this country. Surely the quality of cricket and players playing has improved for which the credit should be given to the various cricket boards for having a sound domestic set up to nurture young cricketers. This surely can be done for hockey too. But as all of us know the ever efficient Hockey India, if wished, would have sweated it out done that long back. So this option no more remains an option. Then what is the way out? Commercialization of the game is one way that comes to my mind. So many youngsters are today attracted by the fame and name that T20 cricket gets along with it for the players, which makes them take their game seriously and not just as a hobby. This in turn churns out talent from the lot. Any day, having more options to choose from is better than having few. That’s what the case with the advent of IPL is. So can’t we have an HPL for hockey too, where corporates can be asked to pick up teams and players and have a tournament among them? What will this ultimately do?
a) It will serve as a great platform for young players to showcase their talent and be recognized
b) More youngsters will take up the game seriously as a career option since they can see a bright future in the game with the corporate bigwigs involved.
c) Better playing facilities (which ideally should have been provided by the hockey board, but… never mind!) from the team owners.
d) The game will again get a chance to connect with the people since they can catch it live on the television.
For starters, the hockey board can approach the BCCI itself to help them out with the whole procedure to begin with and I think the BCCI would be more than happy to extend a helping hand to Hockey India. But the initiative should come from the hockey board. The existing IPL team owners can be called for a meeting and can be asked for their opinions on it which would come in very handy, since they have vast experience in their field and also for the very fact that they might ultimately be the likely owners of the teams. Hopefully this happens and we can get to cheer our regional teams.
Finally, I am not sure if this idea is novel since I feel the IPL success is very visible and at the same time Hockey India is thinking of how to get the sports back into news for the right reasons. The question and the answer both are with the hockey board. But the big question is whether they are really looking out for the solution??? My guess is as good as yours.
When I was 7 yrs. old, I wanted to become a Policeman and save the people from the vices of the world. But the very next moment I saw a cockroach and got scared like hell and my cousins who were with me laughed at me marking the end of my dream to ever wear the starred khaki uniform.
When I was 14, Samrat, our cricket team captain, came up to me and asked me if I was interested in playing cricket for the school junior team. It was literally like a dream come true since, like any other Indian, even I had idolized the stalwarts of Indian cricket and wanted to be one too. I thought that this was just the ideal start for it. Couldn’t sleep that night and dreamt happily bout hitting centuries and guiding my team home to victories. But li’l did I know that even this dream was as short lived as the sleep I got that night.
When I was in my ninth grade, I joined the NCC. Thanks to it that I didn’t remain a shortie that I was till then! Trained hard for 9 months, sweated in the hot sun, screamed my throat off during the parades, withered the harsh Delhi winter and came back home looking as if I was a Nigerian. Then the thought came to my mind, how about being an Army Officer?? But alas, I was destined not be in J&K saving our borders but to be in Mangalore, Karnataka, writing this blog.
And in between I also aspire to be a cricket commentator, a pilot, an actor (which still somewhere probably I wanna be!) and so on…
But why am I talking about all this?
December, 2009: Released a super blockbuster, Aamir Khan Starrer, “3 Idiots”. It was an extraordinary movie with a very contemporary theme of “herd mentality” among today’s generation of students backed some truly powerful performances and amazing direction.
The movie spoke about how one should not just follow the crowd and instead should do something where one’s heart lies. It beautifully portrayed this with the lives of 3 friends and how 2 of them were literally forced into the college where they studied despite having interests elsewhere, just because it was a path where most of the people who walked on it eventually became “successful”. But during the course of their journey, the main protagonist of the movie throws light on how the whole system itself is flawed and it has just become a “rat-race” to say the least, without the students even realizing what they actually want to be in life and what do they need to do in order to achieve it.
The movie itself was a great lesson to all the parents – to allow and support their children in pursuing their dreams. It also was an encouragement for today’s youth to have the courage to speak up about their ambitions, fearlessly, no matter how wild and off-beat they are, and do anything and everything to achieve it. That is what ultimately happened in the movie where none of the three friends joined any MNC after their graduation despite it being every student’s ultimate aim in that college or rather the ultimate aim as per what was fed into their minds by their parents and society (even RajuRastogi (Sharman Joshi) who got through that interview, quit it later and took up a research job, publishing papers and journals relating to it). Farhan (R. Madhavan) went on to become a wild life photographer and Ranchhod das/phunsukwangdu (Aamir Khan) went on to open a school for the orphans in the beautiful valleys of J&K where he taught them pure sciences and focused on creativity and innovation rather than making them mug pots.
It was definitely an eye opener to the society and showed them the loop holes of the system that we are all an integral part of. It definitely opened mine. But what did I realize after I “opened my eyes”?
I realized that I too was a part of the same system which was mocked in the movie (yup I wanna become an MBA too despite having graduated as an engineer in Computer Science!), that I too was one of those thousands of other students who did their engineering just because it was the easiest way to get a good job and settle down in life without even taking some time out and asking myself what I actually want to be life. But as they say it’s never too late to pursue your dreams. So I began thinking, what I want to be life?? And it’s January, 2011 and I am still thinking what I want to be in life. An MBA? An actor? Cricket commentator? Policeman? Army person? MS in USA? Still as confused as I’ve always been in the past 23 yrs.
And during this period of realization I also realized few other things. First, that I am really not passionate about anything, for me to pursue a career in! Yes, I do love cricket, but I wouldn’t say am passionate about it. Yes I do love acting, (my frens curse me for always being in the “acting mode”, when sometimes am really not) but again I don’t feel that am really passionate about movies and acting either. Yes I am a Computer Science Engineer but just like 90% of the CS engineers, I too don’t wanna do an IT job for the rest of my life. I was beginning to wonder what have I been doing all these years and where do I want to see myself 10-20 yrs. hence. So I thought I‘ll ask a few people casually what they wanted to be in life. And then I had my second realization. They were as confused and clueless as I was if not more.
So what do people who do not have any real great passion or aspiration in life do? How does one find out what they want to see themselves as, in future, realistically? Do they wander all their life in search of it and in the deal ignore all the responsibilities they need to shoulder towards their parents and family, towards their nation? What is the right age to decide what u wanna do in life? 15? 17? When you are just out of school?? Are we actually so matured at that age that we can decide for ourselves what we wanna do in life?? I don’t think I was and I can safely assume that neither, the majority of the so called “herd” was. This led me to the third realization or rather a question in my mind. Is it right for us to be mocking the system??
Okay, now let us consider that everyone followed what was portrayed in the movie? Majority of the people would be without a job busy trying to find what they want to be in life and spend weeks, months and probably years in doing the same. What would have happened to those thousands of parents and families of such students? And especially in a country like India where the financial and societal problems of people are well known to everyone (like Raju’s family in the movie. Do they eat haired rotis throughout their lives??). What would have happen to the students themselves who would be feeling worthless and frustrated seeing their peers earn lakhs and crores whereas they would be still trying to find what they wanna do.
So what is the ultimate conclusion of all what I ve told till now? I think it’s not right for us to mock the system that exists no matter how many loopholes are there. Instead we should appreciate its very existence for providing so many jobs and “lighting so many homes” (as my dad always says) and making “unemployment” an obsolete political agenda for the politicians in most parts of the country. Instead we should all contribute and try to remove the loop holes out of the system and make it more transparent and merit based. And in the mean time we as individuals should continue dreaming and trying to find where our dream lays and what do we want to be. And so the confusion continues… wish I had time machine which would help me see what I would be in January, 2031. cricketer? Actor?Entrepreneur? Or still in Infosys Technologies Limited? Don’t you wanna see for yourself too???