Tutorial: Computing Sentiment, Emotion & Personality

The half-day tutorial — Computing Sentiment, Emotion, and Personality — Monday, July 11, 2 pm – 5:30 pm — will introduce key concepts in sentiment analysis and opinion mining from unstructured text and will focus on computational tools and techniques. The tutorial is designed for advanced users, developers, consultants, and others who seek to understand the technology behind the tools they’re using (or hope to build).


Jason Baldridgeco-founder and chief scientist at People Pattern and associate professor of computational linguistics at the University of Texas at Austin, @JasonBaldridge.

Tutorial Description

Opinion mining is a well-known natural language processing technique that generally focuses on the explicit portion of opinion expression. Given the great volume of text created and readily accessible online, tremendous value can be derived from this level of analysis, especially for marketers, political campaigns, and the like. Opinion mining itself takes many forms depending on the granularity of analysis desired, from the most basic determination of whether a given document is generally positive or negative to much more specific questions such as whether a given individual is strongly in favor of a given political position based on texts they’ve authored, their online behaviors and their social network.

This tutorial will dive below the surface of opinion mining in three primary ways. First, we focus on some of the underlying algorithms and the opportunities and challenges for the varied kinds of inputs and outputs involved. In particular, we will discuss semi-supervised learning techniques and their relevance for entity and topic extraction in combination with opinion mining. We will also cover the difference between features used for topic classification and sentiment analysis and those used for stylistic analysis (such as authorship determination). Second, we focus on author modeling, which seeks to understand an individual’s demographic and psychographic attributes based on what they say and how they say it. Third, we look at what additional information might be determined from non-explicit components of linguistic expression, as well as non-textual aspects of the input, such as geography, social networks and images.

The premise: People use language to communicate not just ideas, but also to selectively reveal their internal mental and psychological life. By expressing opinions about ideas, people, and products, a person conveys secondary information about their personality, their values, and their background. Some of this secondary information is explicitly coded in the message, e.g. saying “I love hiking” sends the clear message that the speaker is an outdoorsy sort; however, other properties of the speaker can be gleaned from the general topics they discuss and from their sub-conscious linguistic expression, such as extroversion and status.

Tutorial Outline

  • Introduction. Brief overview of natural language processing, emphasizing different levels of analysis and why it is hard.
  • Sentiment analysis basics. A brief overview covering rules, annotation, machine learning, and evaluation.
  • Aspect-based opinion mining. Identifying the entities and topics that opinions have been expressed about, and building more NLP pipelines and granular models to analyze them.
  • Stylistics and author modeling: authorship, demographics and psychographics.
  • Beyond text: geography, social networks, images, and computational models of meaning.


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