2. Introduction
This presentation covers the history of text
mining, significant practices, applications,
and methods for summarizing text,
enhancing understanding of unstructured
data extraction.
4. Early
Development of
Text Mining
Text mining began in the late 20th century
with the rise of computational linguistics
and natural language processing. Early
efforts focused on statistical analysis of
text data and foundational algorithms.
5. • Major milestones in text mining include the introduction of machine learning
algorithms, advancements in natural language processing, and the emergence of
sophisticated software tools that enhance text analytics, shaping modern practices.
Key Milestones
6. • Over the years, text mining techniques have evolved from simple
frequency counts in terms to complex deep learning methods, allowing
for more nuanced understanding and processing of human language.
Evolution of
Techniques
8. Text
Classification
Text classification is the process of assigning
predefined categories to a text document. It
utilizes machine learning algorithms to analyze
text data, enabling automatic sorting and
organization of documents based on content.
9. • Sentiment analysis uses natural language processing to determine the emotional
tone behind a series of words. This is widely applied in monitoring social media,
customer feedback, and market research to gauge public opinion.
Sentiment Analysis
10. • Information extraction (IE) is the automated processing of structured information from
unstructured text sources. This involves identifying entities, relationships, and events, allowing
actionable insights to be gleaned from large text corpora. Techniques like named entity
recognition and relation extraction are vital for transforming raw data into usable information.
Information
Extraction
12. Market
Analysis
Text mining aids in market analysis by extracting insights
from customer reviews, surveys, and social media data.
Businesses can identify trends, customer preferences, and
feedback in real-time, enabling informed decision-making
and more targeted marketing strategies to enhance
customer satisfaction and loyalty.
13. • In healthcare, text mining is used to analyze clinical notes, research articles, and patient
feedback. It helps in identifying patient outcomes, tracking disease outbreaks, and conducting
systematic reviews, thereby improving patient care and supporting clinical decisions with data-
driven insights.
Healthcare Insights
14. • Text mining techniques monitor social media platforms to analyze public sentiment, brand
perception, and engagement levels. By processing large volumes of posts and comments,
organizations can respond proactively to customer concerns, adapt their strategies, and gain
competitive advantages by understanding audience sentiments.
Social Media
Monitoring
16. Extractive
Summarization
Techniques
Extractive summarization focuses on selecting key sentences
or phrases from a text to create a summary. Techniques
involve ranking sentences based on importance, using
methods like frequency analysis, and employing machine
learning algorithms. This method preserves the original
wording and context of the text while providing concise
outputs.
17. • Abstractive summarization generates new phrases to convey the main ideas of a text. This
approach mimics human-like summarization by interpreting the original material. Techniques
often involve deep learning and advanced natural language processing to paraphrase and
condense information significantly while maintaining coherence and meaning.
Abstractive Summarization
Approaches
18. • Different metrics evaluate the quality of text summaries. Common measures include ROUGE,
BLEU, and METEOR. These metrics assess factors like the overlap of words and phrases
between generated summaries and reference texts, ensuring the automated summarization
meets certain precision and recall standards for effectiveness.
Evaluation Metrics for
Summaries
19. • In conclusion, text mining integrates various practices including information extraction,
application in diverse fields like market research and healthcare, and effective summarization
techniques. Understanding these components empowers organizations to leverage unstructured
data for actionable insights, ultimately leading to better decision-making.
Conclusios
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