6 - Supervised learning, classification Flashcards

1
Q

Customer Relationship Analytics

Sentiment analysis

A

Sentiment analysis can offer direct insights on the firm’s relationship to (potential) customers

Also called: opinion analysis

What is new:

  • automated analysis
  • of large amounts of text data
  • collected over the web
  • with objective predictive power (risk of bias)
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2
Q

Where can I find data on sentiments?

A

Traditional: surveys, interviews, written complaints

  • Advantages: sentiment expressions are somewhat structured or at least targeted
  • Disadvantages: limited responses, possibly sugar-coated, possibly too negative

New: “Harvesting” social networks

  • Advantage: customers freely express their sentiments in large quantities
  • Disadvantages: expressions are not structured, difficult to control, probably still biased
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3
Q

Excursus: Collecting Data from a population

Population

A

The complete set of elements we study

  • also called: sampling frame
  • e.g. all people in the UK, all BMW cars, all patients going through a medical treatment, …
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4
Q

Excursus: Collecting Data from a population

Census

A

Collecting data from every population member

  • expensive and lengthy
  • in the US: every 10 years, every household is contacted
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5
Q

Excursus: Collecting Data from a population

Referendum

A

Collection of data form every population member on a voluntary basis

  • expensive
  • e.g.: 2016: “Should the UK remain a member of the EU or leave?”
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6
Q

Collecting data from sample

Sample

A

Subset of members selected from a population

  • Exhibits characteristics typical of those possessed by the population of interest
  • data is collected from the sample with the objective to analyse and make inferences about the population
  • sample must be “well-selected” to “well represent” the population (we are always interested in the population)
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7
Q

Bias

Whats a bias?

A

Also: tendency, prejudice

can be positive or negative

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8
Q

Bias

Biased sample

A
  • measurements, observations or responses are likely to be unrepresentative of the population as a whole, because of the way the sample is chosen
  • e.g. test Whiskas only on cats from Whiskas employees
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9
Q

Bias

Analysis of a biased sample

A
  • results may be very misleading

- a severely biased sample is usually worthless

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10
Q

Bias

Biased sample

What does that mean for sentiment analysis?

A
  • different channels can offer different messages

- people are more likely to say bad things

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11
Q

Web scraping: extracting sentiments from online sources

A
  • still includes manual efforts to varying extends
  • can be automated via a variety of tools or code in Python or Scrapy
  • efficiency depends on the fit of the search pattern
  • ethically and legal “grey” area: customers do not direct their expressions toward the company, but toward an audience of peers
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12
Q

Approaches to sentiment analysis

Which perspective should be taken?

A

needs to be defined beforehand

Focus

  • domain
  • document or sentence
  • context
  • aspect or holistic

Question

  • quality
  • reason
  • intensity
  • subjectivity
  • seriosity (e.g. irony)
  • originality (customer vs. competitor)
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13
Q

What is an opinion?

A

opinion = (entity, aspect, sentiment, owner, date)

  • aspects can be dresses directly or indirectly
  • sentiments can be stated in a subjective or objective way
  • the statement’s author is not always the sentiment’s owner
  • date information can be used to trace trends
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