Tutorial, March 26 State of the Art Sentiment Analysis: Techniques and Applications

Prof. Ronen Feldman will offer a 3-hour”State of the Art Sentiment Analysis” tutorial on Monday afternoon, March 26, 1:30 pm to 4:45 pm, followed by a half-hour session on Deep Learning Methods for Text Classification presented by data scientist Garrett Hoffman. The sessions are designed for practitioners, developers, and managers who seek a comprehensive and concise introduction to techniques and applications.

State of the Art Sentiment Analysis: Outline

  1. Introduction: What is Sentiment Analysis and why is it hard?
    1. Uses of sentiment analysis
    2. Different types of sentiment analysis
  2. Sentiment analysis and Information Extraction (IE)
    1. Why sentiment analysis requires IE
    2. The core components of IE Systems
      1. entity recognition
      2. Anaphora resolution
      3. relationship extraction
  3. Sentiment Analysis (SA)
    1. Sentiment analysis types and uses
      1. Kinds of sentiment
      2. Polarity, intensity and subjectivity
    2. Sentiment Granularity
      1. Document Sentiment Classification
      2. Sentence Subjectivity and Sentiment Classification
      3. Aspect Sentiment Classification
      4. Aspect and Entity Extraction
    3. Approaches to sentiment analysis
      1. Dictionary-based
      2. Pattern-based
      3. Event-based
    4. Machine learning methods for sentiment analysis
      1. Unsupervised
      2. Supervised
      3. Semi-supervied
      4. Deep Learning
    5. Analysis of Comparative Opinions
    6. Opinion Summarization and Visualization
  4. Sentiment Analysis in Social Networks
    1. Challenges of Sentiment Analysis in Social Networks
    2. Sentiment Analysis of Product reviews
      1. Quality of Reviews
    3. Opinion Spam Detection in Social Networks
    4. Irony, Sarcasm
    5. Opinion Leader Detection
    6. Deep Analysis of Debates and Comments
    7. Mining Intentions
    8. Detecting Fake or Deceptive Opinions
  5. Detailed sentiment analysis case studies:
    1. Discussion Boards
      1. Extracting product comparisons
      2. Assessing market structure
    2. Medical Forums
      1. How users feel about various drugs?
      2. When do they switch to other drugs and why?
      3. Predicting FDA actions based on medical forums analysis
    3. Facebook
      1. predicting user personality and happiness
    4. Scientific and technological texts
      1. sentiment about emerging technologies
      2. predicting successful and failing products
    5. Social and mainstream news media
      1. Extracting sentiment about stocks and companies, applications for hedge funds and banks.
      2. How can Sales reps utilize the sentiment about their prospective companies
  6. Conclusions
    1. What works when
    2. Resources for sentiment analysis
      1. Sentiment dictionaries
      2. Corpora
    3. Emerging industry and research directions

Deep Learning Methods for Text Classification

This workshop will review deep learning methodologies used for text classification while working through a live example using python and TensorFlow. We will start with Representation Learning for text by exploring word2vec word embedding. We will go over the CBOW And Skip-Gram models, demonstrating how to train custom word embeddings or use pre-trained word embeddings trained on google news articles. Next, we will go over traditional Recurrent Neural Networks (RNN) and introduce improvements to the methods using Long Short-Term Memory (LSTM) cells and Gated Recurrent Units (GRUs) and explain the intuition behind why these models provide improvements in accuracy. Next discuss how Convolutional Neural Networks (CNNs) traditionally applied to Computer Vision are now being applied to Language Models. We will close out the session with some practical considerations for applying these methods to different business problems.

Symposium day 2 will feature conference presentations and panels. Attend either day or both, but whichever day(s) you attend, join us for a networking reception Tuesday evening, March 27, 5:30-7 pm!



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