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Looking for other opinions on approach to classification problem

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2

$begingroup$

I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:

1. "remove me from your list"


2. "remove from list"


3. "please unsubscribe from list"


4. etc.

All other messages are "good", and should not be removed.

My thoughts on approaches:

I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.

I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.

classification naive-bayes-classifier

share|improve this question

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

$endgroup$

add a comment | 

2

$begingroup$

I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:

1. "remove me from your list"


2. "remove from list"


3. "please unsubscribe from list"


4. etc.

All other messages are "good", and should not be removed.

My thoughts on approaches:

I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.

I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.

classification naive-bayes-classifier

share|improve this question

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

$endgroup$

add a comment | 

$begingroup$

I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:

1. "remove me from your list"


2. "remove from list"


3. "please unsubscribe from list"


4. etc.

All other messages are "good", and should not be removed.

My thoughts on approaches:

I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.

I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.

classification naive-bayes-classifier

share|improve this question

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

$endgroup$

share|improve this question

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

share|improve this question

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

asked Apr 8 at 15:55

wheresmycookiewheresmycookie

1235

1 Answer
1

active

oldest

votes

1

$begingroup$

You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html

If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/

share|improve this answer

answered Apr 8 at 20:22

pythinkerpythinker

8641314

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1

$begingroup$

You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html

If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/

share|improve this answer

answered Apr 8 at 20:22

pythinkerpythinker

8641314

$endgroup$

add a comment | 

1

$begingroup$

You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html

If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/

share|improve this answer

answered Apr 8 at 20:22

pythinkerpythinker

8641314

$endgroup$

add a comment | 

$begingroup$

You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html

If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/

share|improve this answer

answered Apr 8 at 20:22

pythinkerpythinker

8641314

$endgroup$

share|improve this answer

answered Apr 8 at 20:22

pythinkerpythinker

8641314

answered Apr 8 at 20:22

pythinkerpythinker

8641314

answered Apr 8 at 20:22

pythinkerpythinker

8641314