NLP and Text Sentiment Analysis Lecture
Acoustic digital signals are ubiquitous and have many applications in several disciplines, notably in:
-Digital Media, Social Media (music, voice signals),
-Biomedical Signal Analysis and Diagnosis,
-Scientific signal acquisition of any sort, e.g., Environment Sensing, Geophysical Prospecting.
Text can be considered as 1D signal, as it evolves over time (actually as an order time series of letters or words). Its analysis is extremely important in social media applications (e.g., Tweet analysis), in the analysis of any written and/or broadcasted text (e.g., news articles in newspapers) and in literary text analysis.
This lecture overviews Natural Language Processing (NLP) and Text Sentiment Analysis that has many applications in Text Analytics, Opinion extraction, Opinion mining, Sentiment mining, Subjectivity analysis. It covers the following topics in detail: Baseline algorithms. Text pre-processing. Word embeddings (Word2Vec, Fast Text). Neural NLP and Sentiment Analysis for text classification (RNN, CNN). Contextual Embeddings (ELMo). BERT.