Sentiment Analysis plays a crucial role in understanding user opinions in various languages. The paper presents an experiment with a sentiment analysis model fine-tuned on Marathi sentences to classify sentiments into positive, negative, and neutral categories. The fine-tuned model shows high accuracy when tested on Konkani sentences, despite not being explicitly trained on Konkani data; since Marathi is a language very close to Konkani. This outcome highlights the effectiveness of Zero-shot learning, where the model generalizes well across linguistically similar languages. Evaluation metrics such as accuracy, balanced accuracy, negative accuracy, neutral accuracy, positive accuracy and confusion matrix scores were used to assess the performance, with Konkani sentences demonstrating superior results. These findings indicate that zero-shot sentiment analysis can be a powerful tool for sentiment classification in resource poor languages like Konkani, where labeled data is limited. The method can be used to generate datasets for resource-poor languages. Furthermore, this suggests that leveraging linguistically similar languages can help generate datasets for low-resource languages, enhancing sentiment analysis capabilities where labeled data is scarce. By utilizing related languages, zero-shot models can achieve meaningful performance without the need for extensive labeled data for the target language.