Sentiment analysis is a vital task in NLP. That identifies the emotions in the text. Many studies concentrate only on the English language. There is an insufficient resource in Kannada language emotion analysis. This paper examines the sentiment in Kannada text using a manually prepared dataset. The datasets are divided into three classes positive, negative, and neutral. The processing techniques like context cleaning, tokenization, and sequence padding are used. The model uses RNN-LSTM which is efficient in handling the sequential data. The embedding layer is used to represent the words, the LSTM layer is used to get the context of the sentence, the Dropout layer is used to reduce the over fitting of the model and the dense layer is used to classify the sentiments into categories. The effectiveness of the model was measured using evaluation metrics like precision, recall and f1 score. By predicting sentiments for Kannada text, the paper also exhibits practical use of the mod-el. This study demonstrates that LSTM-based models work well for sentiment analysis in Kannada. It also emphasizes the importance of creating and using manual datasets for low resource languages. The findings would be helpful to further research, and the out-comes can be directly applied in many areas, including social media monitoring, custom-er feedback analysis, and regional language processing.