https://github.com/rupav/predict-happiness

Hackerearth Challenge 😃😃😃
https://github.com/rupav/predict-happiness

hackerearth hotel-review-sentiments multinomial-naive-bayes naive-bayes nlp-machine-learning sentiment-analysis stoplists

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Hackerearth Challenge 😃😃😃

• Host: GitHub
• URL: https://github.com/rupav/predict-happiness
• Owner: rupav
• License: gpl-3.0
• Created: 2017-10-19T11:33:46.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-10-04T10:44:03.000Z (about 5 years ago)
• Last Synced: 2024-08-09T04:35:03.280Z (3 months ago)
• Topics: hackerearth, hotel-review-sentiments, multinomial-naive-bayes, naive-bayes, nlp-machine-learning, sentiment-analysis, stoplists
• Language: Jupyter Notebook
• Homepage: https://www.hackerearth.com/challenge/competitive/predict-the-happiness/machine-learning/predict-the-happiness/
• Size: 28.2 MB
• Stars: 2
• Watchers: 2
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# Predict-Happiness

Hackerearth Challenge( deadline- 30th nov. 2017)

![Smileys]( https://github.com/rupav/Predict-Happiness/blob/master/smileys.jpg )

## Stats of 70% of the test dataset, as checked on Hackerearth:

Submission date|My Score | Leaderboard Max score| Approach

-----------    | ------- |        ------------- | ---------------

18th oct. 17   | 86.781  | 90.624               | multinomialNB with standard stoplist|

20th oct. 17   |86.363   | 90.624               | using MultinomialNB with TF1 stoplist only |

20th oct. 17   |84.070   | 90.624               | using MultinomialNB with TF1 stoplist only and TFIDF approach|

20th oct. 17   |86.300   | 90.624               | using MultinomialNB with tf_high(thresh 7500) stoplist in addition to tf1|

20th oct. 17   | 86.630  | 90.624               | using MultinomialNB with tf_high(thresh 7500) stoplist in addition to tf1 and standard stoplist|

23rd oct. 17   |80.878   | 90.624               | Random Forest Classiffier

28th oct. 17   |86.668   | 90.624               | Removed hyphens and used Lemmatizer, used MultinomialNB

* My Final Private Leaderboard Ranking and score : 177/554 and 86.781

* Private Leaderboard top score : 91.051

* My Final Public Leaderboard Ranking and score : 

* Public Leaderboard top score :

## Comments:

This repository is made to accumulate and test various techniques in sentiment analysis.

Couldnt make any submissions in nov. because of college exams :( . 

Should have tried Deep Learning. 

# References:

[Stopwords analysis](http://www.lrec-conf.org/proceedings/lrec2014/pdf/292_Paper.pdf) : For 2nd approach- key points :

 * TF1 outperforms other stoplists.

 * standard stoplist has a negative impact on sentiment analysis.

 * NB is more sensitive to stopwords removal than MaxEntropy.

 * Two more approaches- TBRS and Mutual Information can be explored!

# TO DO after challenge deadline:

 * To explore Deep Learning Techniques on sentiment Analysis.

   * CBOG (continuous bag of words) techniques

   * skip grams with negative sampling.

 * Other techniques with different preprocessing.

 

# Contribution:

Please create an issue first, and then make a relevant PR for it.