This paper outlines our submission to the Sentiment Analysis Shared Task at the Bangla Language Processing (BLP) Workshop at EMNLP2023 (Hasan et al., 2023a). The objective of this task is to detect sentiment in each text by classifying it as Positive, Negative, or Neutral. This shared task is based on the MUltiplatform BAngla SEntiment (MUBASE) (Hasan et al., 2023b) and SentNob (Islam et al., 2021) dataset, which consists of public comments from various social media platforms. Our proposed method for this task is based on the pre-trained Bangla language model BanglaBERT (Bhattacharjee et al., 2022). We trained an ensemble of BanglaBERT on the original dataset and used it to generate pseudo-labels for data augmentation. This expanded dataset was then used to train our final models. During the evaluation phase, 30 teams submitted their systems, and our system achieved the second highest performance with F1 score of 0.7267. The source code of the proposed approach is available at https://github.com/KnowdeeAI/blp_task2_knowdee.git.