Sentiment Analysis on Multilingual Data: Hinglish

In this study, sentiment analysis was done on a collection of bilingual Hindi and English tweets. Before performing sentiment analysis, we first preprocessed the dataset to improve its quality by removing hashtags, URLs, and mentions. To classify the tweets as good, negative, or neutral, we used a range of machine learning models, such as logistic regression, multinomial Naive Bayes, support vector machines, random forests, and decision trees. We compared the results and utilized the metrics of accuracy and precision to assess the performance of the models. The investigation showed that the hybrid model, which included logistic regression with support vector machines, produced the highest accuracy and precision scores. Hyperparameter adjustment was used to improve the logistic regression model’s performance even more. The best model was then used to predict sentiments on a new dataset of unlabeled Hindi-English tweets. The empirical findings demonstrated that the hybrid model outperformed the other machine learning models utilized in this study, achieving an accuracy of 61.7% and a precision of 62.1%. When compared with all forecasts that were truly positive, the precision score showed the percentage of true positives, while the accuracy score showed the percentage of accurate predictions. The results showed that a highly accurate and exact solution for sentiment analysis on Hindi-English tweets is a hybrid model combining logistic regression and a support vector model with hyperparameter tweaking.