Sentiment Analysis of Code-Mixed Social Media Text (Hinglish)

TL;DR

This study evaluates various machine learning techniques for sentiment analysis of Hinglish social media text, achieving a maximum F1-score of 69.07 with ensemble classifiers.

Contribution

It systematically compares multiple data cleaning, transformation, and modeling techniques specifically for Hinglish sentiment analysis.

Findings

Ensemble voting classifier achieved the highest F1-score of 69.07.

Multiple data transformation methods were tested, including tf-idf and word embeddings.

Data cleaning improved model performance across iterations.

Abstract

This paper discusses the results obtained for different techniques applied for performing the sentiment analysis of social media (Twitter) code-mixed text written in Hinglish. The various stages involved in performing the sentiment analysis were data consolidation, data cleaning, data transformation and modelling. Various data cleaning techniques were applied, data was cleaned in five iterations and the results of experiments conducted were noted after each iteration. Data was transformed using count vectorizer, one hot vectorizer, tf-idf vectorizer, doc2vec, word2vec and fasttext embeddings. The models were created using various machine learning algorithms such as SVM, KNN, Decision Trees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting classifiers. The data was obtained from a task on Codalab competition website which was listed as Task:9 on the Semeval-2020 competition website. The models created were evaluated using the F1-score (macro). The best F1-score of 69.07 was achieved using ensemble voting classifier.