Text Mining for Online Reputation Monitoring

SSIM 2016

Pedro Saleiro

POPSTAR

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Media Coverage

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Media Coverage

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Media Coverage

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Reputation[Van Riel et al., 2007] define reputation as “overall assessments of organizations by their stakeholders”

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Social Media & Online News

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Online Reputation MonitoringTracking what is said about a given entity on Social Media

Early ORM systems focused on counting entity mentions on Social Media

Implies collecting, cleaning, filtering, mining, exploring and analysing large streams of unstructured text data

Current Systems focus in NER, NED, Polarity Classification and Visualisation (Social Media Analytics)

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Online Reputation Monitoring

Social

Entity∑+/-

Vision

News Social

Pred.

Rel.

Entity∑+/-

Retr.

Framework for ORM

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PolarityClassification

NER

EntitiesWarehouse

Online News Social Media

Soc. Media Crawling

Active Learning

Data Aggregation

News Crawling

Services & Applications

Prediction Tasks

Mentions Extraction

Relations Extraction

NED

Entity-RelationIndexes

Entity-Rel. Indexing

Entity-Rel. Ranking

Applications

Reputation Management

Fraud Detection

Computational Journalism

Political Science

Social Media Marketing

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Preliminary WorkExtraction

NED approach for Twitter (binary classification)

Context similarity with Wikipedia, Freebase

Dataset -140K tweets (96K test set)

1st place REPLAB 2013 (0.90 Accuracy)

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Planed Work

Extraction

Active Learning for

NED

Sentiment Analysis

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Preliminary Work

Retrieval

Entity-Relation Retrieval using MdT dataset (11M news articles)

Mentions and Relations Extraction using ClueWeb 09 (50M web pages)

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Planed Work

Retrieval

Scalable indexing approach for entities and relations

Learning to rank/data fusion approach

Strategy for automatic collection of relevance judgments (Wikipedia Tables)

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Preliminary WorkPrediction

Classification approach for Twitter entity popularity prediction based on news

Regression approach for Portuguese polls prediction based on Twitter sentiment aggregate functions

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Planed WorkPrediction

Extend number of entities for popularity prediction

Deal with bursts

Different learning approach/experimental setting

Use different entity-centric knowledge for polls prediction

Evaluate predictive approaches at different periods in time

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