automated classification of book blurbs according to the...

Automated Classification of Book

Blurbs According to the Emotional Tags

of the Social Network Zazie

V. FRANZONI, V. POGGIONI AND F. ZOLLO

DIPARTIMENTO DI MATEMATICA E INFORMATICA

UNIVERSITÀ DEGLI STUDI DI PERUGIA

Zazie

Zazie is an Italian social network for book readers that introduces a new

dimension on book characterization, the emotional icon tagging.

V. Franzoni, V. Poggioni and F. Zollo, Dipartimento di Matematica e Informatica, Università degli Studi di PerugiaAutomated Classification of Book Blurbs According to the Emotional Tags of the Social Network Zazie.

Zazie was created by

Digit-Pub with Marco

Ghezzi and Barbara Sgarzi

on the model of Anobii,

with the introduction of

emotional icon tags.

lightningread again

Zazie’s Mood Icons


Zazie’s Mood Icons


smile

sad

love

angry

think

cry


smile

sad

love

angry

think

cry

Automated classification of books

?

Always present:• Title• Author• Editor• Pages• Blurb

Which information can we use?


for a supervised learning approach

?

The Idea

Emotional automated classification according toZazie.

The necessity arises from the presence of a lot ofbooks that have not been tagged yet by the userswith the goal of an emotion-driven search.

Lexical analysis of the book blurbs.


The Approach

Correlation between the characteristics of a bookblurb and the emotional icons associated to thebook by the users.

Book blurbs can contain relevant emotionalinformation.

Blurbs are written to attract the reader, emphasizingsome book aspects with the use of emotional terms.


System Architecture

Zazie DB

Filtering

• Book filtering• Grouping• Tag filtering• Mood filtering

Filtered DB

Blurb analysis

• Preprocessing:- Stop words- Tokenization- Lemmatization

• Emotion extraction

DatasetClassifiers

• J48• BFTree• …

Classification Model


Architecture of the model

Zazie DB

Filtering


Filtered DB

Blurb analysis



DatasetClassifiers




Zazie Database

38374 records:

associations of tags in the MOOD set to books.

8 fields:

(user_id, book_isbn, mood)

(book_isbn, title,pages, publisher, author,blurb)



Zazie DB

Filtering


Filtered DB

Blurb analysis



DatasetClassifiers




DB filtering

Book filtering: books, most tagged by the community

Grouping: tag, count for each book (book_isbn, mood)

Tag filtering: books, predominant moods (standard

deviation)

Mood filtering: emotional moodsangry,cry,love,sad,smile,think


Book filtering

Distribution of the records with respect to the MOODS, after the book filtering step.


Tag filtering



Zazie DB

Filtering


Filtered DB

Blurb analysis



DatasetClassifiers




Filtered Database

Distribution of records, with respect to selected MOODs at the end of the filtering steps.


40%

37%

9%

6%

300 records

High variance

Unbalanced distribution

Preliminary dataset

A new dataset is under testing


Zazie DB

Filtering


Filtered DB

Blurb analysis



DatasetClassifiers




Preprocessing

Normalization of the DB:

1. Stop words deletion e.g, articles and preposition

2. Tokenization ignoring punctuation marks and digits

3. Lemmatization using Morph-it! Reducing noise due to

variabilities such as singolar or plural, male or female etc.

All lemmata are kept in case of ambiguity.



Zazie DB

Filtering


Filtered DB

Blurb analysis



DatasetClassifiers




Emotion extraction

Synset retrieval (WordNet/MultiWordNet) for each lemma.

Exploitation of the affective domain WordNet-Affect to

associate an emotion to each synset.

Terms which don’t convey emotional information are

filtered out.

Multiple occurrences of the same emotion are counted.


WordNet-Affect

Emotional hierarchyof WordNet-Affect(296 nodes)

…too finely pronged!


Emotion reduction

Two techniques were implemented:

To the third level in WordNet-Affect hierarchy


WordNet-Affect

Third level ofemotional hierarchyof WordNet-Affect(32 nodes)


Emotion reduction

Two techniques were implemented:

To the third level in WordNet-Affect hierarchy

To an extended set of Ekman model of emotions:

anger, disgust, fear, happiness, sadness, surprise

+

neutral, ambiguous


Emotion extraction

Example:

Emotions extracted from the blurb of the book «The Count of

Montecristo» by Alexandre Dumas (emotion[#occ]).

anxiety[3], enthusiasm[1], love[1], affection[1],

joy[1], negative-fear[2], general-dislike[1]



Zazie DB

Filtering


Filtered DB

Blurb analysis



DatasetClassifiers




Dataset

Selected features for book representation:

Author (nominal attribute)

Emotions extracted from the blurb (32 or 8 numerical

attributes)

Mood (nominal attribute): class attribute

Publisher, pages attributes were discarded as

representative of a specific edition.


Classification model building

Multiclass classification model: each book is associated to

one of the 6 selected moods angry,cry,love,sad,smile,think

Classification models were built by means of Weka software,

using different machine learning algorithms:

Decision tree

Decision rules

Bayesian classifiers

Random forest


Classification model evaluation

Cross validation technique with ten folds, in particular algorithms based

on decision trees, without pruning showed the best results for accuracy,

precision and recall.

Decision trees also give a more readable model.


TP: totally right classifications

N: #instances

NC: #classes

For each class i:TPi: true positive

FPi: false positiveFNi: false negative

Experiments

Best accuracy levels obtained with J48 and BFTree.

Classification results with respect to selectedemotional MOODs


Conclusions

Experiments are encouraging, considering ongoing

improvements.

The blurb is confirmed to be a good source of

emotional information about a book, to be analyzed

with the aim of sentiment analysis and emotion

recognition.

Zazie provides directly a emotional model of classes:

we don’t need a manually annotation.


Further developments

Dataset improvement in both preprocessing/filtering

(use of web-based proximity measures) and emotion

extraction with ontology-driven approach that uses

the ArsEmotica ontology

Binary classification

Feedback process from Zazie’s side

Extension to a multilabel classification


Questions and comments

[email protected]

[email protected]

[email protected]


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