Text-Mining-Presentation artificial intelligence

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 S entiment 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!