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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
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