{"id":17976493,"url":"https://github.com/rupav/predict-happiness","last_synced_at":"2025-06-22T00:05:07.361Z","repository":{"id":252335116,"uuid":"107537929","full_name":"rupav/Predict-Happiness","owner":"rupav","description":"Hackerearth Challenge 😃😃😃","archived":false,"fork":false,"pushed_at":"2019-10-04T10:44:03.000Z","size":29605,"stargazers_count":2,"open_issues_count":2,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-04T00:17:13.723Z","etag":null,"topics":["hackerearth","hotel-review-sentiments","multinomial-naive-bayes","naive-bayes","nlp-machine-learning","sentiment-analysis","stoplists"],"latest_commit_sha":null,"homepage":"https://www.hackerearth.com/challenge/competitive/predict-the-happiness/machine-learning/predict-the-happiness/","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Predict-Happiness\nHackerearth Challenge( deadline- 30th nov. 2017)\n\n![Smileys]( https://github.com/rupav/Predict-Happiness/blob/master/smileys.jpg )\n\n## Stats of 70% of the test dataset, as checked on Hackerearth:\n\nSubmission date|My Score | Leaderboard Max score| Approach\n-----------    | ------- |        ------------- | ---------------\n18th oct. 17   | 86.781  | 90.624               | multinomialNB with standard stoplist|\n20th oct. 17   |86.363   | 90.624               | using MultinomialNB with TF1 stoplist only |\n20th oct. 17   |84.070   | 90.624               | using MultinomialNB with TF1 stoplist only and TFIDF approach|\n20th oct. 17   |86.300   | 90.624               | using MultinomialNB with tf_high(thresh 7500) stoplist in addition to tf1|\n20th oct. 17   | 86.630  | 90.624               | using MultinomialNB with tf_high(thresh 7500) stoplist in addition to tf1 and standard stoplist|\n23rd oct. 17   |80.878   | 90.624               | Random Forest Classiffier\n28th oct. 17   |86.668   | 90.624               | Removed hyphens and used Lemmatizer, used MultinomialNB\n\n\n* My Final Private Leaderboard Ranking and score : 177/554 and 86.781\n* Private Leaderboard top score : 91.051\n* My Final Public Leaderboard Ranking and score : \n* Public Leaderboard top score :\n\n## Comments:\nThis repository is made to accumulate and test various techniques in sentiment analysis.\nCouldnt make any submissions in nov. because of college exams :( . \nShould have tried Deep Learning. \n\n# References:\n[Stopwords analysis](http://www.lrec-conf.org/proceedings/lrec2014/pdf/292_Paper.pdf) : For 2nd approach- key points :\n * TF1 outperforms other stoplists.\n * standard stoplist has a negative impact on sentiment analysis.\n * NB is more sensitive to stopwords removal than MaxEntropy.\n * Two more approaches- TBRS and Mutual Information can be explored!\n\n# TO DO after challenge deadline:\n * To explore Deep Learning Techniques on sentiment Analysis.\n   * CBOG (continuous bag of words) techniques\n   * skip grams with negative sampling.\n * Other techniques with different preprocessing.\n \n# Contribution:\nPlease create an issue first, and then make a relevant PR for it.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frupav%2Fpredict-happiness","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frupav%2Fpredict-happiness","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frupav%2Fpredict-happiness/lists"}