Google ML Olympiad Hausa Sentiment Analysis

This challenge is part of the ML Olympiad, An associated Kaggle Community Competitions hosted by ML GDEs or TFUGs, sponsored by Google Developers.

A Repo of my solution to the Google Machine Learning OLympiad Competition Hosted on Kaggle 1st Place Solutuion

Position on Leader Board -- 1st (Gold)

To get the Data check the link Below link -- https://www.kaggle.com/c/hausa-sentiment-analysis

ML Olympiad - Hausa Sentiment Analysis

Hausa is a Chadic language, a branch of the Afroasiatic language family. It is the most spoken in the family, next to Arabic. Hausa is considered the largest ethnic group in sub-Saharan Africa, with some diverse native speakers who are culturally homogeneous.

Sentiment analysis relies on multiple word senses and cultural knowledge and can be influenced by age, gender and socio-economic status. For this task, we have collected and annotated sentences from different social media platforms.

The objective of this challenge is to develop a multi-class classification model to classify news content according to its specific category. Given a sentence, the task is to classify whether the sentence is of positive (1), negative (-1) or neutral (0) sentiment. For messages conveying more than one sentiment, whichever is the stronger sentiment should be chosen.

Predict if the text would be considered positive, neutral or negative (for an average user).

MY Solution Approach

• A Random Forest Model
  • Exploratory Data Analysis removing redundant features
  • Used a RepeatedStratifiedKfold of 5 Splits and repeats of 3 creating 15 splits (To overcome overfitting)
  • Used different Random Forest Parameters based on parameter tunning
  • Hence created 3 models
• Finally created a Blend Model for the 3 Model

Experiment that Performed Less

• Using Simple Transformers Classifiers
• Using Deep Learning (RNN and LSTM)
• Using Catboot and XGBoost (This was attributed to the low dataset sample)