This seminar provides a comprehensive exploration of text mining techniques using Python, tailored for academic researchers seeking to analyze large textual datasets effectively. Participants will gain hands-on experience with Python libraries and methodologies for natural language processing, sentiment analysis, topic modeling, text classification, and more, enhancing their data analysis capabilities across various disciplines.

Topics Covered
Text Mining Applications , Research Impact , Textual Data Challenges , Data Insight Extraction , Python Environment Setup , Installing Packages , Library Management , Best Practices , NLP Overview , Language Concepts , Tokenization , Part-of-Speech Tagging , Named Entity Recognition , Text Preprocessing Essentials , Stopwords , Text Normalization , Stemming and Lemmatization , Keyword Extraction , Text Labeling , N-grams , Bag of Words , Language Detection , TF-IDF , Regex , Python Implementation , NLTK and SpaCy , Scikit-learn Intro , Library Functions , Model Training , Model Evaluation , Machne Learning in Text Analysis , Aspect-Based Sentiment Analysis , Polarity Detection , Emotion Detection , Understanding Sentiment , Case Studies , Latent Dirichlet Allocation , Theme Identification , BERT model , Real-World Examples , Political Discourse Analysis , Climate Change Analysis , Python Application , Data Privacy Concerns , Ethical Guidelines , Impact on Research , Real Data Applications , Text Mining for policy making