Automated Classification of Book Blurbs According to the Emotional Tags of the Social Network Zazie

Transcription

1 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

2 Zazie Zazie is an Italian social network for book readers that introduces a new dimension on book characterization, the emotional icon tagging. Zazie was created by Digit-Pub with Marco Ghezzi and Barbara Sgarzi on the model of Anobii, with the introduction of emotional icon tags. read again lightning

3 Zazie s Mood Icons

4 Zazie s Mood Icons smile cry think sad love angry

5 Automated classification of books smile cry sad? think love angry

6 Which information can we use? for a supervised learning approach? Always present: Title Author Editor Pages Blurb

7 The Idea Emotional automated classification according to Zazie. The necessity arises from the presence of a lot of books that have not been tagged yet by the users with the goal of an emotion-driven search. Lexical analysis of the book blurbs.

8 The Approach Correlation between the characteristics of a book blurb and the emotional icons associated to the book by the users. Book blurbs can contain relevant emotional information. Blurbs are written to attract the reader, emphasizing some book aspects with the use of emotional terms.

9 System Architecture Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

10 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

11 Zazie Database records: associations of tags in the MOOD set to books. 8 fields: (user_id, book_isbn, mood) (book_isbn, title,pages, publisher, author,blurb)

12 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

13 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

14 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 moods angry,cry,love,sad,smile,think

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

16 Tag filtering

17 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

18 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

19 Filtered Database 300 records High variance Unbalanced distribution Preliminary dataset A new dataset is under testing 6% 9% 37% 40% Distribution of records, with respect to selected MOODs at the end of the filtering steps.

20 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

21 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

22 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.

23 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

24 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

25 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.

26 WordNet-Affect Emotional hierarchy of WordNet-Affect (296 nodes) too finely pronged!

27 Emotion reduction Two techniques were implemented: To the third level in WordNet-Affect hierarchy

28 WordNet-Affect Third level of emotional hierarchy of WordNet-Affect (32 nodes)

29 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

30 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]

31 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

32 Architecture of the model Filtering Blurb analysis Zazie DB Book filtering Grouping Tag filtering Mood filtering Filtered DB Preprocessing: - Stop words - Tokenization - Lemmatization Emotion extraction Dataset Classifiers J48 BFTree Classification Model

33 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.

34 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

35 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: TP i : true positive FP i : false positive FN i : false negative

36 Experiments Best accuracy levels obtained with J48 and BFTree. Classification results with respect to selected emotional MOODs

37 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.

38 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

39 Questions and comments