Preprocessing Before applying a topic modeling algorithm, the text data must be preprocessed. This involves removing stop words, stemming, and lemmatization. In the context of nanotechnology, domain-specific stop words (e.g., common scientific terms that may not contribute to topic differentiation) might also be removed.
Choosing the Number of Topics One of the challenges in topic modeling is selecting the appropriate number of topics. Techniques such as cross-validation or metrics like perplexity and coherence scores can help in determining the optimal number of topics. In nanotechnology, this might involve balancing the granularity of topics to ensure they are neither too broad nor too specific.
Interpreting Topics Each topic generated by the model is represented by a set of words with associated probabilities. Interpreting these topics requires domain expertise to label them meaningfully. For example, a topic with high probabilities for words like "quantum", "dot", "semiconductor" might be labeled as "Quantum Dots".
