Crowdsourcing Emotions in Music Domain

An important source of intelligence for music emotion recognition today comes from user-provided community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags, design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of various crowdsourcing instruments providing examples from research works. We also share our own experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs than negative ones

PI1710-3-EmoAnalysi : desarrollo de una herramienta software para el análisis de emociones presentes en la transcripción de llamadas telefónicas para apoyar procesos de inteligencia de negocios de un centro de atención a clientes

En este trabajo se propone el modelo de análisis de emociones CATALINA, que permite identificar las emociones presentes en una llamada telefónica transcrita. CATALINA analiza las oraciones y las clasifica según el modelo de emociones de Ekman, utilizando los algoritmos de clasificación KNN, PMI y el PMI-IR. Este modelo fue validado a través del análisis de doscientas veinte oraciones emocionales provenientes de llamadas recibidas en las oficinas de soporte técnico de una compañía de software reconocida en Colombia, en donde el modelo obtuvo una precisión total del 70%.In this document, we propose the CATALINA emotion analysis model, which allows us to identify the emotions present in a transcribed telephone call. CATALINA analyzes the sentences and classifies them according to Ekman's model of emotions, using the classification algorithms KNN, PMI and PMI-IR. This model was validated through the analysis of two hundred and twenty emotional sentences coming from calls received in the technical support offices of a recognized software company in Colombia, where the model obtained an accuracy of 70%.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Lyrics Matter: Using Lyrics to Solve Music Information Retrieval Tasks

Music Information Retrieval (MIR) research tends to focus on audio features like melody and timbre of songs while largely ignoring lyrics. Lyrics and poetry adhere to a specific rhyme and meter structure which set them apart from prose. This structure could be exploited to obtain useful information, which can be used to solve Music Information Retrieval tasks. In this thesis we show the usefulness of lyrics in solving MIR tasks. For our first result, we show that the presence of lyrics has a variety of significant effects on how people perceive songs, though it is unable to significantly increase the agreement between Canadian and Chinese listeners about the mood of the song. We find that the mood assigned to a song is dependent on whether people listen to it, read the lyrics or both together. Our results suggests that music mood is so dependent on cultural and experiental context to make it difficult to claim it as a true concept. We also show that we can predict the genre of a document based on the adjective choices made by the authors. Using this approach, we show that adjectives more likely to be used in lyrics are more rhymable than those more likely to be used in poetry and are also able to successfully separate poetic lyricists like Bob Dylan from non-poetic lyricists like Bryan Adams. We then proceed to develop a hit song detection model using 31 rhyme, meter and syllable features and commonly used Machine Learning algorithms (Bayesian Network and SVM). We find that our lyrics features outperform audio features at separating hits and flops. Using the same features we can also detect songs which are likely to be shazamed heavily. Since most of the Shazam Hall of Fame songs are by upcoming artists, our advice to them is to write lyrically complicated songs with lots of complicated rhymes in order to rise above the "sonic wallpaper", get noticed and shazamed, and become famous. We argue that complex rhyme and meter is a detectable property of lyrics that indicates quality songmaking and artisanship and allows artists to become successful

Emotion Recognition Education in Western Art Music Appreciation

Because Western art music is harmonically more complex than popular music and because it is written with musical notation, it may be challenging for certain people with no music training (non-musicians), those who did not grow up with Western art music, or those who did not choose to listen to this type of music for enjoyment to understand and appreciate it. Furthermore, there is a prevalent belief that Western art music is for the wealthy and elderly. This belief may be preventing symphony orchestra groups from cultivating new audiences. This study aims to determine if a narrative music listening activity would generate emotional response and cognitive engagement in a study group of non-Western art music listeners and prompt them to create musical narratives. Theoretically, narrative form music listening may present episodic memories, which can be built up into stories. To test the effect of narrative music listening activities, an online survey was distributed to non-Western art music listeners in the 20 through 40 age range, and pretest–treatment–posttest activity was devised and administered to three groups, an absolute music listening group, a programmatic music listening group, and a polyphonic texture listening group. In the treatment section, the creative listening activity, participants were prompted to create musical narratives, which take the form of colors, shapes, dialogues, or explicit stories. Participants were then asked to write about the music they heard before and after the narrative music listening activity. Participants’ motivation to attend a Western art music concert was assessed via a motivation scale using Likert scales. The results suggest that this online activity’s multimodality was a promising method for enhancing the appreciation of Western art music

Characterisation of business documents: an approach to the automation of quality assessment

This thesis explores a new approach to automatic characterisation of business documents of different levels of document effectiveness. Supervised text categorisation techniques are used to derive text features that characterise a specific type of business document in accordance with pre-assigned levels of document utility. The documents in question are the executive summary sections of a representative sample of sales proposal documents. The executive summaries are first rated by domain experts against a quality framework comprising pre-selected dimensions of document quality. An automatic analysis of the texts shows that certain words, word sequences, and patterns of words have the capacity to discriminate between executive summaries of varying levels of document effectiveness. Function words, which are frequently ignored in many text classification tasks, are retained and are shown to provide an important element of the word patterns. Automatic text classifiers that utilise these features are shown to categorise previously unseen executive summaries at an acceptable level of classification performance. The outcomes of the research are applied to the development of a new computer application. The application identifies, in the text of a new executive summary, word patterns that discriminate between sets of summaries previously categorised into different levels of document utility. The action of highlighting the respective categories of discriminating word patterns directs authors to areas of text that may need further attention. A trial of a prototype of the application suggests that it provides an effective way to help sales professionals improve the content and quality of the text of this type of business document. Moreover, as the approach is suitably generic, it could be applied to different types of document in different domains