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Learning Objectives for Chapter 7
1. Describe text mining and understand the need for text mining
2. Differentiate among text analytics, text mining, and data mining
3. Understand the different application areas for text mining
4. Know the process of carrying out a text mining project
5. Appreciate the different methods to introduce structure to text-based data
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7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into
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7.2 Text Analytics and Text Mining Overview
7.3 Natural Language Processing (NLP)
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7.5 Text Mining Process
7.6 Sentiment Analysis
7.7 Web Mining Overview
7.8 Search Engines
7.9 Web Usage Mining
7.10 Social Analytics
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Section 7.1 Review Questions
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Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.
2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?
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1. Describe text mining and understand the need for text mining
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5. Appreciate the different methods to introduce structure to text-based data
6. Describe sentiment analysis
7. Develop familiarity with popular applications of sentiment analysis
8. Learn the common methods for sentiment analysis
9. Become familiar with speech analytics as it relates to sentiment analysis
10. Learn three facets of Web analytics—content, structure, and usage mining
11. Know social analytics including social media and social network analyses
CHAPTER OVERVIEW
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7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into
Near-Real-Time Sales
7.2 Text Analytics and Text Mining Overview
7.3 Natural Language Processing (NLP)
7.4 Text Mining Applications
7.5 Text Mining Process
7.6 Sentiment Analysis
7.7 Web Mining Overview
7.8 Search Engines
7.9 Web Usage Mining
7.10 Social Analytics
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- 1. TEXT ANALYSIS AND ITS IMPORTANCE A GUIDE TO PRESENTATION
- 2. Definition and Types Importance and applications Challenges and Conclusion Introduction The The topics which we will cover in this presentation.
- 3. Text Analysis? What is DEFINITION The process of analyzing and understanding the meaning of text data using various techniques such as statistical, computational, and linguistic methods. Text analysis involves extracting insights and patterns from unstructured data sources such as social media posts, customer reviews, and news articles DEFINITION
- 4. Text analysis Types of SENTIMENT ANALYSIS TOPIC MODELLING NAMED ENTITY RECOGNITION TEXT CLASSIFICATION Analyzing the emotions and opinions expressed in text data. Identifying and extracting the main topics or themes in a document or set of documents. Identifying and categorizing named entities, such as people, organizations, and locations. Classifying text data into predefined categories based on its content.
- 5. Text analysis Techniques NATURAL LANGUAGE PROCESSING MACHINE LEARNING DATA VISUALIZATION A subfield of computer science and artificial intelligence that focuses on the interaction between computers and human language. A type of artificial intelligence that uses algorithms to identify patterns in data and make predictions. The graphical representation of data and information to facilitate understanding and communication.
- 6. Text Analysis Applications of CUSTOMER FEEDBACK ANALYSIS NEWS ANALYSIS SOCIAL MEDIA MONITORING Analyzing customer reviews and feedback to understand customer sentiment and improve product or service offerings. Analyzing news articles to identify emerging trends and changes in public opinion. Monitoring social media platforms to identify trends, customer opinions, and brand reputation.
- 7. Challenges Text analysis Data quality Interpretation Multilingual data Text data can be noisy, incomplete, and difficult to standardize. The meaning of text can be subjective and context- dependent, making it challenging to analyze accurately. Analyzing text data in different languages can pose significant challenges, especially in languages with complex syntax and grammar.
- 8. Conlusion Text analysis is a powerful tool for extracting insights and understanding the meaning of unstructured data sources. Through techniques such as natural language processing, machine learning, and data visualization, we can gain valuable insights from text data, from customer feedback and social media monitoring to news analysis and fraud detection. However, we must also be aware of the challenges of working with text data, such as data quality, interpretation, and multilingual data. Despite these challenges, there is no doubt that text analysis will continue to play a critical role in helping individuals and organizations make informed decisions and drive business value.