AUTOMATIC AND INTELLIGENT SENTIMENT ANALYSIS AND OPINION MINING ON SOCIAL MEDIA AND THE WEB IN THE MEDICAL FIELD
General Information:
Level |
Master |
Title |
AUTOMATIC AND INTELLIGENT SENTIMENT ANALYSIS AND OPINION MINING ON SOCIAL MEDIA AND THE WEB IN THE MEDICAL FIELD |
Specialty |
Biomedical informatics |
Cover Page:
Outline:
General Introduction
CHAPTER 1: SOCIAL MEDIA
1. Introduction
2. Developing a Social Media Strategy
3. Mobile Social Media
4. Social Network
4.1 Definition
4.2 Definition Of Social Media
4.3 Social media and social networking
4.4 Examples Of Social Networks
4.5 The first social media website
5. Blog
6. Microblog
7. Twitter
7.1 Introduction
7.2 Twitter Terminology
7.3 Twitter Is So Popular
CHAPTER 2: SENTIMENT ANALYSIS
1. introduction
2. Sentiment Analysis
3. Types of Sentiment Analysis
3.1. Fine-grained Sentiment Analysis
3.2. Emotion detection
4. Sentiment analysis work
4.1. Rule-based Approaches
4.2. Automatic Approaches
4.3. Hybrid Approaches
5. Sentiment Analysis Approach
CHAPTRE 3: APPLICATION
I. CORPUS
1. Introduction
2. Types of corpora
3. Types Of Text Corpora
Monolingual Corpus
Multilingual Corpus
Comparable Corpus
Parallel Corpus
Learner Corpus
Specialized Corpus
Diachronic Corpus
Multimedia Corpus
II. The Tools Used
1. Python
2. Anaconda
3. Twitter API
4. Libraries
III. Collecting tweets
Step 1: Getting Twitter API keys
Step 2: Connecting to Twitter Streaming API and downloading data
IV. Pre-processing
1. Automatic detection of the language of tweets
2. Tokenization
3. Stop Words
4. Lemmatization
4.1. Lemmatization of English tweets
4.2. Lemmatization of French tweets
4.3. Lemmatization of Arabic tweets
V. Annotation of the corpus
VI. Classification of tweets
Conclusion
General conclusion
References
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