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Exploring Sentiment Analysis Techniques
This chapter examines the diverse applications and methodologies of sentiment analysis, focusing on lexicon-based approaches and tools like TextBlob. It discusses the complexities of analyzing sentiments across different languages, highlighting the challenges posed by linguistic nuances and the importance of tailored dictionaries. Additionally, the chapter emphasizes the role of machine learning in sentiment classification, the use of Python libraries, and the creation of robust processing pipelines for accurate sentiment evaluation.
What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python.
Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career.
In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own.
This week’s episode is brought to you by Sentry.
Course Spotlight: Learn Text Classification With Python and Keras
In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.
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