Can We Infer Book Classi cation by Blurbs?

Abstract

The aim of this work is to study the feasibility of an automated classi cation of books in the social network Zazie by means of the lexical analysis of book blurbs. A supervised learning approach is used to determine if a correlation between the characteristics of a book blurb and the emotional icons associated to the book by the Zazie's users exists.

Introduction

Sentiment Analysis and Opinion Mining are receiving increasing attention in many sectors because knowing and predicting opinions of people is considered a strategic added value. In the last years an increasing attention has also been devoted to Emotion Recognition, often by developing automated systems that can associate user's emotions to texts, music or artworks (e.g. [ 1 ], [ 6 ]).

In the wide scenario of text mining, several attempts have been made in order to associate emotions and moods to blogs [ 2 ], tales [ 3 ] and newspapers titles, in several domains and contexts. A particular and interesting domain that has been introduced in [ 9 ] is the association of emotions to books 1.

The idea of building a model for classifying books from an emotional point of view was born from Zazie2, an Italian social network for readers that, di erently from other similar projects e.g., aNobii or Goodreads, introduces a new dimension for books description, the emotional icon tagging.

Each book in Zazie, besides user's comments and reviews, can be tagged with two moods icons, selected in a set of 25 di erent climates related to the reader's opinion about the book or to the emotion induced by the book, e.g. cool, culture, cry, sleep, love, hoax or smile.

Starting from this context, it would be very innovative to provide Zazie users with an emotion-driven search within the social network. The necessity of such an automated system arises also from the presence of a lot of books that have not been tagged yet by the users, for which there is not any information besides the characteristics stored in the database i.e., title, author or publisher.

The rst step of this research was focused on the selection of relevant attributes among those usable and available in Zazie to describe a book. We decided to analyze the book blurb because it can contain relevant emotional 1 An extended version of this work has been presented in [ 9 ] 2 www.zazie.it We are grateful to Marco Ghezzi and Zazie developers, Joe and David, for the collaboration to this research. information, since it is generally written for attracting the reader and it can emphasize and highlight some aspects with the use of emotional terms. On the other hand, a possible drawback is the introduction of a bias caused by the excessive use of words with a high emotional meaning, so the problem is not trivial. Moreover, the blurb represents an information always available on Zazie, regardless of user's opinions, reviews or tags. The main original contribution of this work is to determine if the book blurb re ects the same emotions that the reader can nd in the book itself. The emotional model used for moods representation is directly provided by Zazie by means of its emotional tags (icons) and can be easily correlated to the well known discrete emotional models, such as the ones de ned by Ekman [ 4 ] or Plutchik [ 5 ]. 2

Blurb Analysis

The blurb analysis has been realized in three main phases: preprocessing, extraction of emotions and reduction of emotions.

Preprocessing consisted in stop words deletion, tokenization and lemmatization, realized by means of Morph-it! , a morphological resource for the Italian language.

Extraction of Emotions was realised by means of WordNet-A ect,[ 8 ] retrieving for each lemma the WordNet synsets associated to it, using the multilingual lexical database MultiWordNet [ 7 ]. The a ective domain WordNet-A ect was exploited in order to obtain all the emotions associated to each synset, ltering out terms that did not convey a ective information and taking into account multiple occurrences of the same emotion.

Reduction of Emotions was necessary because the emotional hierarchy of WordNet-A ect is particularly pronged (296 nodes) and the result of the emotion extraction phase can be excessively detailed. It was implemented in two step following two di erent approaches, rst reducing the set of emotions to the 32 emotions corresponding to the third level of WordNet-A ect hierarchy (i.e., the subtree rooted in emotion)

and then, associating these 32 emotions to an extended set of Ekman emotions[ 4 ], formed by eight emotional categories happiness, anger, disgust, fear, sadness, surprise, neutral, ambiguous. 3

Experiments

Experiments were carried on to test if an automated classi cation of book blurbs based on Zazie emotional tags is possible and can actually be used with a satisfactory accuracy.

The database provided by Zazie authors was constituted of 38374 records, each one representing the association of a tag (one of the 25 Zazie moods) to a book by an user. Each record is represented by 7 elds (user id, book isbn, book title, book pages, book publisher, book blurb, mood). Those data then underwent a ltering and cleaning process as detailed in [ 9 ].

In this group of experiments the classi cation is limited to a subset of the Zazie mood set identi ed by the selected moods fsmile, love, sad, think, angry, cryg; this subset was chosen because it contains emotional tags that are clearly related to emotions aroused in the reader by the book.

Among the information characterizing a book which is available in the Zazie database, the author and the emotions extracted by the blurb analysis have been used as the sample features. Publisher and number of pages have been discarded, because they are associated to a particular edition of the book and do not characterize the book as its general literary work. Each record in the dataset represents a book and is characterized by either 34 or 10 features: { the author (nominal attribute) { the emotions extracted from the blurb (numerical attributes) valued by their occurrences (32 or 8 depending on which strategy for emotion reduction is applied) { the mood tag (nominal attribute) representing the class attribute Experiments had been carried on by means of the software Weka.

The experimentation has been realized through the cross validation technique with ten folds using, in particular, algorithms based on decision trees.

Models have been evaluated by the accuracy, recall and precision measures, de ned as follow: { Accuracy = T P=N where T P is the number of instances correctly classi ed and N is the total number of instances. { Recall = N1C Pi=1::NC Recalli where N C is the number of classes, Recalli = T PiT+PFi Ni and T Pi and F Ni are respectively the instances correctly classi ed as members of class i and the instances wrongly classi ed as not belonging to the class i. { P recision = N1C Pi=1::NC P recisioni where P recisioni = T PTi+PFi Pi and F Pi is the number of instances wrongly classi ed as members of class i.

Results for accuracy, recall and precision are presented in Table 1: best accuracy levels are obtained with J48 and BFTree algorithms that have equivalent performances, while, LADTree and RandomForest did not perform as well as expected. Details about the tested algorithms and their implementations can be found in the Weka documentation. Results show that algorithms having an unpruned version perform better. A satisfactory level of accuracy was obtained reaching more than 70% and considering that other experiments and the design of other techniques to build a reliable dataset are ongoing works. 4

Conclusions and Future Work

The blurb is con rmed to be a good source of emotional information about a book and it actually can be analyzed with the aim of sentiment analysis and emotion recognition. (a) Emotional model with 32 emotions derived from WordNet-A ect (b) Emotional model with 8 emotions derived from extended Ekman model Attributes Accuracy J48 -U -M 3 70.31% BFTree -U -M 3 72.92% LADTree -B 20 58.85% RandomForest -depth 5 66.15% Attributes Accuracy J48 -U -M 3 72.02% BFTree -U -M 3 70.46% LADTree -B 20 52.85% RandomForest -depth 5 68.91%

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