Sentiment analysis, a key task in Natural Language Processing (NLP), involves determining the emotional value of text, categorizing it as positive, neutral, or negative. This article explores various Python-based sentiment analysis methods, including rule-based approaches like TextBlob and VADER, which use predefined lexical features to assess sentiment, and embedding-based models like Flair that utilize sophisticated text representations for improved performance. Additionally, it discusses building custom sentiment analysis models from scratch using tools like TensorFlow and the Universal Sentence Encoder, highlighting the advantages of custom models for niche applications. The performance of these methods is compared using a common dataset, revealing that custom models often outperform pre-existing packages, although none generalize well on Twitter data. The article aims to provide insights into the different approaches and their applications, helping readers choose the most suitable method for their projects.