Text Mining on Mailing Lists: Sentiment Analysis Gordon Heiczman, B. - - PowerPoint PPT Presentation

Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Text Mining on Mailing Lists: Sentiment Analysis

Gordon Heiczman, B. Sc.

October 13, 2017 Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Introduction

What is Sentiment Analysis?

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Introduction

What is Sentiment Analysis?

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Introduction

What is Sentiment Analysis?

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Introduction

Problems of today:

• Too much information
• Too little time
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Introduction

Agenda

• Text Mining summary
• Example of practical application
• Presentation of results
• Conclusion and Lessons Learned
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Text Mining

Feature Selection Main purpose: Extract valuable information, get rid of redundant features ’Bag of Words’ approach Most common selection steps:

• Removal of stop words (the, is, at ...)
• Removal of plurals (dogs -> dog)
• Word / n-gram frequency
• Part of Speech (POS) tagging (adjectives)
• Opinion words (like, hate, love ...)
• Detection of negation (not good -> bad)
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Text Mining

Sentiment Classification Three main categories:

• Machine Learning
• Lexicon-based
• Hybrid
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Text Mining

Pitfalls

• Named Entity Recognition i.e. "What is the topic"
• Anaphora Resolution - Reference word resolution. "What is ’it’ refering to?"
• Sarcasm
• Abbreviations, poor grammar / punctuation / spelling
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Practical Application

• Dataset
• Language
• Email retrieval
• Content retrieval
• Sentiment value retrieval
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Practical Application

Dataset Collection of emails from the IETF. Task of IETF is to set standards.

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Practical Application

Language C# or Python? Not enough comprehensive, completely free tools Notable C# tools:

• VaderSharp (free but primitive)
• Aylien (paid)
• Watson D.C. (paid)
• Vivekn (free but no documentation)

Python tool: TextBlob

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Practical Application

Multiple values obtained through SA:

• Polarity ( -1.0 <-> 1.0)
• Subjectivity (0.0 <-> 1.0)
• Most used word
• Sentence Count
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Practical Application

Textblob example blob = TextBlob("I think this presentation is really, really good!") print(blob.sentiment) # Gives both polarity and subjectivity around 1.0 print(blob.words.count(’really’)) # Gives 2 print(blob.noun_phrases) # Gives nouns, in this case presentation

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Practical Application

Figure 1: Example of email with polarity 1.0

• Filename: /home/.../geopriv/2007-12.mail
• Key: 251
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Practical Application

Programflow

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Practical Application

Programflow

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Statistics

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 i p

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b 8 4 a l l i b n e m

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d e a s d a t a t r a c k e r

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s i e t f m i b s I 3 v p n h

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e y

Figure 2: Top 10 groups who use the most sentences

Even distribution Indication of in-depth discussion or off-topic rambling?

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Statistics

0.5 0.375854 0.3078360.303693 0.29163 0.276323 0.2532740.251799 0.25 0.25 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 i a

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t 6 7 a t t e n d e e s 9 6

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e n t

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Figure 3: Top 10 most positive groups

Logarithmic distribution Notable group: "iaoc-scribes"

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Statistics

• 0.50
• 0.45
• 0.40
• 0.35
• 0.30
• 0.25
• 0.20
• 0.15
• 0.10
• 0.05

0.00 i e t f

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a i l

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s i r t f

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h a r t e r 7 a t t e n d e e s s c h e m a w r e c i p s r a s a s l i u c g w e b p r i n t m i b

Figure 4: Top 10 most negative groups

Stronger logarithmic distribution Notable group: "ietf-sailors"

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Statistics

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 i a

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Figure 5: Top 10 most subjective groups

Surprising top scores Discussion groups

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Statistics

From the 7 most negative (-1.0) polarity entries 6 belong to the group ’eos’ All of them are in Spanish (?)

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Conclusion

Useful but not universally Lessons learned:

• Filter the data-set intelligently
• Don’t try to solve everything with one library
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