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Difference between Stemming and Lemmatization
Stemming is simpler and faster but less precise. On the other hand, Lemmatization is slower and more
computationally intensive but produces contextually meaningful results. Their differences can be summarised
as follows:
Aspect Stemming Lemmatization
Definition Reduces a word to its root form by chopping off Reduces a word to its base or dictionary form
prefixes or suffixes without considering meaning. (lemma) considering the context and meaning.
Output Produces a truncated form of the word, which Produces a meaningful, valid word (lemma).
may not be a valid word.
Approach Rule-based (simple removal of affixes like -ing, Dictionary or vocabulary-based, requiring
-ed, etc.). morphological analysis.
Speed Faster, as it only involves simple string operations. Slower, as it involves more computational
complexity and context analysis.
Accuracy Less accurate, may produce results that are not More accurate, results are meaningful and
meaningful (e.g., “running” → “run”). contextually appropriate.
Use Cases Used when speed is more important than Used in applications requiring precise
precision, e.g., in search engines. understanding of words, e.g., machine translation.
Reboot
Perform the given operations on the foll
Knives Stemming
Caring Stemming
Studies Lemmatization
Caring Lemmatization
Task 21 st Century #Critical Thinking
Skills
#Information Literacy
1. Provide examples of sentiment expressed in the given text, categorising them as Positive, Neutral or Negative.
a. I absolutely love this product! It works perfectly."
b. "I bought this book last week, and I am halfway through it.
c. "The hotel room was dirty, and the service was extremely slow
d. "This movie was incredibly inspiring and beautifully made."
e. "The restaurant was crowded, and the food arrived after 30 minutes."
f. "She explained the concept in detail, covering both pros and cons."
g. "The quality of this item is terrible; it broke within a day."
h. "The customer service was fantastic, and they resolved my issue quickly."
i. "I had a very disappointing experience with their support team."
2. How can sentiment analysis be quantified, and what real world applications does it have?
Natural Language Processing (Theory) 381

