  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.
