Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets
This addresses sentiment analysis challenges in multilingual social media contexts, but it is incremental as it combines existing models.
The study tackled the problem of sentiment analysis for foreign languages with limited labeled data by proposing an ensemble of multilingual transformer models, achieving over 86% performance.
Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentiment. Along with the growth of social media and the Internet, the significance of sentiment analysis has grown across numerous industries such as marketing, politics, and customer service. Sentiment analysis is flawed, however, when applied to foreign languages, particularly when there is no labelled data to train models upon. In this study, we present a transformer ensemble model and a large language model (LLM) that employs sentiment analysis of other languages. We used multi languages dataset. Sentiment was then assessed for sentences using an ensemble of pre-trained sentiment analysis models: bert-base-multilingual-uncased-sentiment, and XLM-R. Our experimental results indicated that sentiment analysis performance was more than 86% using the proposed method.