Technology Frontiers: Text, Sentiment, and Sense

Technology Frontiers: Text, Sentiment, and Sense

Seth Grimes@sethgrimes

A Sensemaking Story

New York Times,September 30, 2012


Valium: A Chain of Connections

Natural Language Processing

By H.P. Luhn, inIBM Journal,April, 1958

http://altaplana.com/ibm-luhn58-LiteratureAbstracts.pdf

Modelling Text

“Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the auto-abstract.”

-- H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958.

Luhn’s analysis of Messengers of the Nervous System, a Scientific American article http://wordle.net,

applied to the NY Times article


Luhn’s Example

Close Reading

Can Software Make the Connection?

Mark Lombardi, George W. Bush, Harken Energy and Jackson Stephens, c. 1979-90, Detail

Insight from Connections

… via graphs, clusters, categories, and counts.

… by mining the full set of available data.

http://techpresident.com/news/21618/politico-facebook-sentiment-analysis-bogus

Online & Social Change Everything

(Accessible) Data Everywhere

Lexical, syntactic, and semantic analysis discern features including relationships in source materials.

Features = entities, measure-value pairs, concepts, topics, events, sentiment, and more.

Text analytics may draw on:

• Lexicons & taxonomies.• Statistics.• Patterns.• Linguistics.• Machine learning.

Text Analytics

From POS to Relationships

Understand parts of speech (POS), e.g. – <subject> <verb> <object> –to discern facts and relationships.

Semantic networks such as WordNet are a disambiguation asset.

Clustered Clarity

Carrot2.(open source)

Platforms and ecosystems.

APIs and services.

Text and content analytics --Discerns and extracts features including

relationships from source materials.

Features = entities, key-value pairs, concepts, topics, events, sentiment, etc.

Provide (for) BI on content-sourced data.

Data integration, record linkage, data fusion.

The Back End

Content, Composites, Connections

Content, Composites, Connections, 2

Social Sources

Sentiment Analysis

“Sentiment analysis is the task of identifying positive and negative opinions, emotions, and evaluations.”

-- Wilson, Wiebe & Hoffman, 2005, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis”

“Sentiment analysis or opinion mining is the computational study of opinions, sentiments and emotions expressed in text… An opinion on a feature f is a positive or negative view, attitude, emotion or appraisal on f from an opinion holder.”

-- Bing Liu, 2010, “Sentiment Analysis and Subjectivity,” in Handbook of Natural Language Processing

Detection, Classification

Beyond Polarity

Intent Analysis

http://www.aiaioo.com/whitepapers/intention_analysis_use_cases.pdf

http://sentibet.com/

Complications

Sentiment may be of interest at multiple levels.Corpus / data space, i.e., across multiple sources.Document.Statement / sentence.Entity / topic / concept.

Human language is noisy and chaotic!Jargon, slang, irony, ambiguity, anaphora, polysemy,

synonymy, etc.Context is key. Discourse analysis comes into play.

Must distinguish the sentiment holder from the object:“Geithner said the recession may worsen.”

Audio including speech.Images.Video.

http://www.geekosystem.com/facebook-face-recognition/

http://www.sciencedirect.com/science/article/pii/S0167639312000118

http://flylib.com/books/en/2.495.1.54/1/

Beyond Text

Sensemaking

“It is convenient to divide the entire information access process into two main components: information retrieval through searching and browsing, and analysis and synthesis of results. This broader process is often referred to in the literature as sensemaking. Sensemaking refers to an iterative process of formulating a conceptual representation from of a large volume of information. Search plays only one part in this process.”

-- Marti Hearst, 2009 http://searchuserinterfaces.com/

Apply new tech to old needs, e.g., automated coding.

Select from and use all available data.

Marry social to profiles and surveys.

Factor in behaviors.

Interpret according to context and needs.

Understand intent to create situational predictive models.

Explore; experiment.

Suggestions

Racing On

Technology Frontiers: Text, Sentiment, and Sense

Seth Grimes@sethgrimes

Technology Frontiers: Text, Sentiment, and Sense

Technology

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