The Boyer–Moore majority vote algorithm is an algorithm for finding the majority of a sequence of elements using linear time and a constant number of words of memory. It is named after Robert S. Boyer and J Strother Moore, who published it in 1981, and is a prototypical example of a streaming algorithm.

1. Technical Seminar on
Text Mining: Overview, Steps, Applications
KISHKINDA UNIVERSITY
DEPARTMENT OF COMPUTER SCIENCE &
ENGINEERING
Presented By : P Sahana Prasad
(KUB24MCS015)
HOD [M.Tech(CSE)] & Guide,
Dr. Rajashree V Biradar

2. What is Text Mining?
 Text mining is the process of analyzing
large collections of textual data to
extract meaningful patterns, insights, and
useful information.
 It combines techniques from natural
language processing (NLP), machine
learning, and statistics to turn
unstructured text into structured data.

3. Steps in Text Mining

4. Applications of Text Mining
• Sentiment Analysis: Understand public
opinion from social media or reviews.

5. Applications of Text Mining
•Spam Detection: Identify and filter spam
emails or messages.

6. Applications of Text Mining
•Healthcare & Biomedicine: Extract
relevant information from research papers
and clinical notes.

7. Advantages & Disadvantages of Text
Mining
Advantages:
• Automates analysis of large volumes of
text.
• Uncovers hidden patterns and insights.
• Improves decision-making with data-
driven insights.
• Enhances customer service and marketing
strategies.
Disadvantages:
• Can be computationally expensive for
very large datasets.
• Challenges with ambiguous language,
sarcasm, and context.

8. Text Mining Code Example (Python)
from sklearn.feature_extraction.text import CountVectorizer
corpus = ['Text mining turns text into insights', 'Natural Language Processing is
powerful']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names_out())
print(X.toarray())
```
**Output:**
['into' 'is' 'language' 'mining' 'natural' 'powerful' 'processing' 'text' 'turns']
[[0 0 0 1 0 0 0 1 1]
[0 1 1 0 1 1 1 0 0]]

9. Conclusion
 Text mining is a powerful tool for
extracting knowledge from unstructured
text.
 It has wide applications across industries,
from marketing to healthcare.
 Despite challenges, it remains a key
technology for making sense of vast
amounts of textual data.
 Unlock the power of words with Text
Mining!