This paper discusses a process for visualizing music artists' main topics and sentiment by analyzing lyrics. Lyrics are scraped from websites and preprocessed. Topic analysis identifies the most common nouns as main topics. Sentiment analysis determines the percentage of positive and negative words using lexicons. A moodboard is generated by selecting images related to topics and adjusting saturation based on sentiment scores. The results are meant to offer insight into an artist's topics and mood in a visual format. Areas for improvement include developing domain-specific models for part-of-speech tagging and sentiment analysis.
The goal of this project is to build a classifier able to predict whether a song is happy or sad analysing its lyrics. Most of the research on music classication is based on features
obtained by audio signals. However, the exploration of lyrics alone as a source of information can be relevant in music
classication. It is an interesting problem and it has not been widely explored in the literature.
In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.
Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion...IJECEIAES
Music has lyrics and audio. That‟s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. „ooh‟, „ah‟, „yeah‟, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.
Using automated lexical resources in arabic sentence subjectivityijaia
A common point in almost any work on Sentiment analysis is the need to identify which elements of
language (words) contribute to express the subjectivity in text. Collecting of these elements (sentiment
words) regardless the context with their polarities (positive/negative) is called sentiment lexical resources
or subjective lexicon. In this paper, we investigate the method for generating Sentiment Arabic lexical
Semantic Database by using lexicon based approach. Also, we study the prior polarity effects of each word
using our Sentiment Arabic Lexical Semantic Database on the sentence-level subjectivity and multiple
machine learning algorithms. The experiments were conducted on MPQA corpus containing subjective and
objective sentences of Arabic language, and we were able to achieve 76.1 % classification accuracy.
The goal of this project is to build a classifier able to predict whether a song is happy or sad analysing its lyrics. Most of the research on music classication is based on features
obtained by audio signals. However, the exploration of lyrics alone as a source of information can be relevant in music
classication. It is an interesting problem and it has not been widely explored in the literature.
In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.
Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion...IJECEIAES
Music has lyrics and audio. That‟s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. „ooh‟, „ah‟, „yeah‟, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.
Using automated lexical resources in arabic sentence subjectivityijaia
A common point in almost any work on Sentiment analysis is the need to identify which elements of
language (words) contribute to express the subjectivity in text. Collecting of these elements (sentiment
words) regardless the context with their polarities (positive/negative) is called sentiment lexical resources
or subjective lexicon. In this paper, we investigate the method for generating Sentiment Arabic lexical
Semantic Database by using lexicon based approach. Also, we study the prior polarity effects of each word
using our Sentiment Arabic Lexical Semantic Database on the sentence-level subjectivity and multiple
machine learning algorithms. The experiments were conducted on MPQA corpus containing subjective and
objective sentences of Arabic language, and we were able to achieve 76.1 % classification accuracy.
USING AUTOMATED LEXICAL RESOURCES IN ARABIC SENTENCE SUBJECTIVITYijaia
A common point in almost any work on Sentiment analysis is the need to identify which elements of
language (words) contribute to express the subjectivity in text. Collecting of these elements (sentiment
words) regardless the context with their polarities (positive/negative) is called sentiment lexical resources
or subjective lexicon. In this paper, we investigate the method for generating Sentiment Arabic lexical
Semantic Database by using lexicon based approach. Also, we study the prior polarity effects of each word
using our Sentiment Arabic Lexical Semantic Database on the sentence-level subjectivity and multiple
machine learning algorithms. The experiments were conducted on MPQA corpus containing subjective and
objective sentences of Arabic language, and we were able to achieve 76.1 % classification accuracy.
Toward an Understanding of Lyrics-viewing Behavior While Listening to Music o...Kosetsu Tsukuda
本ポスターは2021年11月7日~12日に開催された「22nd International Society for Music Information Retrieval Conference (ISMIR 2021)」の発表資料です。
発表した論文のPDFは以下のURLから閲覧できます。
http://ktsukuda.me/wp-content/uploads/ISMIR2021_Lyrics_tsukuda.pdf
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
Computational Approaches for Melodic Description in Indian Art Music CorporaSankalp Gulati
Presentation for my PhD defense, Music Technology Group, Barcelona, Spain.
Resources: http://compmusic.upf.edu/node/304
Short abstract:
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify melodic patterns, compare them both within and across music pieces, and finally, characterize the specific melodic context of IAM, the rāgas. All these analyses are done using data-driven methodologies on sizable curated music corpora. Our work paves the way for addressing several interesting research problems in the field of music information research, as well as developing novel applications in the context of music discovery and music pedagogy.
The most integral part of our work is to extract Aspects from User Feedback and associate Sentiment and Opinion terms to them. The dataset we have at our disposal to work upon, is a set of feedback documents for various departments in a Hospital in XML format which have comments represented in tags. It contains about 65000 responses to a survey taken in a Hospital. Every response or comment is treated as a sentence or a set of them. We perform a sentence level aspect and sentiment extraction and we attempt to understand and mine User Feedback data to gather aspects from it. Further to it, we extract the sentiment mentions and evaluate them contextually for sentiment and associate those sentiment mentions with the corresponding aspects. To start with, we perform a clean up on the User Feedback data, followed by aspect extraction and sentiment polarity calculation, with the help of POS tagging and SentiWordNet filters respectively. The obtained sentiments are further classified according to a set of Linguistic rules and the scores are normalized to nullify any noise that might be present. We lay emphasis on using a rule based approach; rules being Linguistic rules that correspond to the positioning of various parts-of-speech words in a sentence.
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields collecting review from people about products and social and political events through the web. Currently, Sentiment Analysis concentrates for subjective statements or on subjectivity and overlook objective statements which carry sentiment(s). During the sentiment classification more challenging problem are faced due to the ambiguous sense of words, negation words and intensifier. Due to its importance the correct sense of target word is extracted and determined for which the similarity arise in WordNet Glosses. This paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field.
What is a Close Reading or Explicating a PoemTo explicate comlorileemcclatchie
What is a Close Reading or Explicating a Poem?
To "explicate" comes from a Latin word meaning to unfold.
The purpose of an explication or close reading is to unfold the significance of a poem.
Explication pays close attention to the parts of a poem in order to support a larger argument about its overall impact. For your paper you will want to choose
one
poem.
Your essay should reveal how the parts of the poem, like the parts of a tree, relate and form a totality. Ideally, your paper should reveal some of the wonder and excitement that first inspired you to choose this poem.
You should consider the following questions:
Are you able to provide an argument about what the poem means?
Are you able to provide evidence of how the poetic techniques (tone, speaker, figurative language, form, rhythm, etc.) enhances or creates that meaning? Is the evidence effective or is anything important being left out?
Summarizing
:
Pre-writing
Once you have chosen a poem, paraphrase it (i.e. put it in your own words). You will want to deliberately avoid using figurative language. The purpose of this step is two-fold. First, it ensures that you know what the poem is saying. Second, it allows you to see the moments where the poet uses an intense kind of language.
Poetic Techniques:
Poetic Devices and examples
The following are some poetic techniques that you may want to consider in your paper. In your final exam you will want as wide a variety of techniques as possible. In earlier papers you may focus on only the ones covered in the day's readings or that we have covered so far. These questions are only the most basic ones: As we cover more poetic techniques this semester you will want to create your own list of questions that you ask yourself.
1 . Examine the language of the poem. Look up any words that seem important or unclear. How does the text make use of the particular connotations of its words? Are there patterns of word choice (diction), such as language associated with religion or with everyday speech? What images and image patterns are prominent? What are the associations of these images? Do the images take on larger significance as symbols? What other metaphoric language contributes to the poem's meaning? Similes? Puns? Are there larger patterns of allegory or allusion?
2 . How is the author using the form? How does the form suit the poet's intent? What variations are there in meter and rhyme scheme? How do these variations affect the meaning? How does the poet use the break between octave and sestet or quatrains and couplets? What other sound effects do you notice (alliteration, assonance, etc.) and how do they fit the larger effects of the poem? How does the poem use line and stanza breaks? How does it use syntax to emphasize or enact its meaning?
3 . Who is the speaker of the poem? How would you characterize the speaker? What is the tone of the poem? How does it change? Does it use irony? What techniques does poet use to get this tone ...
Concert Evaluation AssignmentIn a well-written essay of approx.docxmaxinesmith73660
Concert Evaluation Assignment
In a well-written essay of approximately 700 words (typed), create a review of a live professional concert relevant to this course. Discuss the strengths and weaknesses of the concert and focus mainly on the most important/striking elements and qualities of the performance. Use that information to arrive at an overall evaluation of the event.
Hints to keep you writing:
Describe: This is the very basic and factual content of your paper and should function as the introduction to your review. When was the concert? Where was it? Who performed? What kind of music was performed? Was there any particular theme to the concert? Was there anything unusual about the concert occasion? Don't list everything on the program, but do summarize the important information.
Analyze: To analyze something is to examine its components and, more than just describe them, to seek out relationships among the parts. What were the outstanding musical characteristics of the pieces? How did the pieces that were performed compare to each other? How do they compare to what you have learned in class and to music you have heard at other times? This section is where you may want to use the musical terms and concepts that have been discussed in class (but see the section below on using musical terminology).
Interpret: Did you find some meaning in the music? What do you think may have been the original significance of the music? Does it have a different significance today? What people might want to listen to this music, and why? There are no right or wrong answers to these kinds of questions, but always give examples and make supporting statements to back up your assertions.
Judge: Remember that a concert consists of two things: the music being performed and the people who are doing the performing. Don't confuse the two. Do talk about each one. Did you like the music, and why (or why not)? Regardless of whether you liked the music, did you think that the performers did a good job? What, if anything, do you think would have improved the performance? Again, these are individual and subjective responses, but give explanations for your judgments.
Disclaimer and General Guidelines: Do not write your review only by answering some or all of the questions listed in the above paragraphs. They are not intended to be a checklist of things that you must write about, but rather are suggestions to direct your thinking about various aspects of the music and performance. The principal goal of this assignment is for you to listen critically to a live performance and then to articulate in writing your observations and reactions. Originality of thought and clarity of expression are more important to this assignment than is addressing each and every point outlined above.
Using Program Notes: Many concert programs provide information about either the music being performed or the performers. These notes are for your benefit and can assist you in understanding .
USING AUTOMATED LEXICAL RESOURCES IN ARABIC SENTENCE SUBJECTIVITYijaia
A common point in almost any work on Sentiment analysis is the need to identify which elements of
language (words) contribute to express the subjectivity in text. Collecting of these elements (sentiment
words) regardless the context with their polarities (positive/negative) is called sentiment lexical resources
or subjective lexicon. In this paper, we investigate the method for generating Sentiment Arabic lexical
Semantic Database by using lexicon based approach. Also, we study the prior polarity effects of each word
using our Sentiment Arabic Lexical Semantic Database on the sentence-level subjectivity and multiple
machine learning algorithms. The experiments were conducted on MPQA corpus containing subjective and
objective sentences of Arabic language, and we were able to achieve 76.1 % classification accuracy.
Toward an Understanding of Lyrics-viewing Behavior While Listening to Music o...Kosetsu Tsukuda
本ポスターは2021年11月7日~12日に開催された「22nd International Society for Music Information Retrieval Conference (ISMIR 2021)」の発表資料です。
発表した論文のPDFは以下のURLから閲覧できます。
http://ktsukuda.me/wp-content/uploads/ISMIR2021_Lyrics_tsukuda.pdf
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
Computational Approaches for Melodic Description in Indian Art Music CorporaSankalp Gulati
Presentation for my PhD defense, Music Technology Group, Barcelona, Spain.
Resources: http://compmusic.upf.edu/node/304
Short abstract:
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify melodic patterns, compare them both within and across music pieces, and finally, characterize the specific melodic context of IAM, the rāgas. All these analyses are done using data-driven methodologies on sizable curated music corpora. Our work paves the way for addressing several interesting research problems in the field of music information research, as well as developing novel applications in the context of music discovery and music pedagogy.
The most integral part of our work is to extract Aspects from User Feedback and associate Sentiment and Opinion terms to them. The dataset we have at our disposal to work upon, is a set of feedback documents for various departments in a Hospital in XML format which have comments represented in tags. It contains about 65000 responses to a survey taken in a Hospital. Every response or comment is treated as a sentence or a set of them. We perform a sentence level aspect and sentiment extraction and we attempt to understand and mine User Feedback data to gather aspects from it. Further to it, we extract the sentiment mentions and evaluate them contextually for sentiment and associate those sentiment mentions with the corresponding aspects. To start with, we perform a clean up on the User Feedback data, followed by aspect extraction and sentiment polarity calculation, with the help of POS tagging and SentiWordNet filters respectively. The obtained sentiments are further classified according to a set of Linguistic rules and the scores are normalized to nullify any noise that might be present. We lay emphasis on using a rule based approach; rules being Linguistic rules that correspond to the positioning of various parts-of-speech words in a sentence.
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields collecting review from people about products and social and political events through the web. Currently, Sentiment Analysis concentrates for subjective statements or on subjectivity and overlook objective statements which carry sentiment(s). During the sentiment classification more challenging problem are faced due to the ambiguous sense of words, negation words and intensifier. Due to its importance the correct sense of target word is extracted and determined for which the similarity arise in WordNet Glosses. This paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field.
What is a Close Reading or Explicating a PoemTo explicate comlorileemcclatchie
What is a Close Reading or Explicating a Poem?
To "explicate" comes from a Latin word meaning to unfold.
The purpose of an explication or close reading is to unfold the significance of a poem.
Explication pays close attention to the parts of a poem in order to support a larger argument about its overall impact. For your paper you will want to choose
one
poem.
Your essay should reveal how the parts of the poem, like the parts of a tree, relate and form a totality. Ideally, your paper should reveal some of the wonder and excitement that first inspired you to choose this poem.
You should consider the following questions:
Are you able to provide an argument about what the poem means?
Are you able to provide evidence of how the poetic techniques (tone, speaker, figurative language, form, rhythm, etc.) enhances or creates that meaning? Is the evidence effective or is anything important being left out?
Summarizing
:
Pre-writing
Once you have chosen a poem, paraphrase it (i.e. put it in your own words). You will want to deliberately avoid using figurative language. The purpose of this step is two-fold. First, it ensures that you know what the poem is saying. Second, it allows you to see the moments where the poet uses an intense kind of language.
Poetic Techniques:
Poetic Devices and examples
The following are some poetic techniques that you may want to consider in your paper. In your final exam you will want as wide a variety of techniques as possible. In earlier papers you may focus on only the ones covered in the day's readings or that we have covered so far. These questions are only the most basic ones: As we cover more poetic techniques this semester you will want to create your own list of questions that you ask yourself.
1 . Examine the language of the poem. Look up any words that seem important or unclear. How does the text make use of the particular connotations of its words? Are there patterns of word choice (diction), such as language associated with religion or with everyday speech? What images and image patterns are prominent? What are the associations of these images? Do the images take on larger significance as symbols? What other metaphoric language contributes to the poem's meaning? Similes? Puns? Are there larger patterns of allegory or allusion?
2 . How is the author using the form? How does the form suit the poet's intent? What variations are there in meter and rhyme scheme? How do these variations affect the meaning? How does the poet use the break between octave and sestet or quatrains and couplets? What other sound effects do you notice (alliteration, assonance, etc.) and how do they fit the larger effects of the poem? How does the poem use line and stanza breaks? How does it use syntax to emphasize or enact its meaning?
3 . Who is the speaker of the poem? How would you characterize the speaker? What is the tone of the poem? How does it change? Does it use irony? What techniques does poet use to get this tone ...
Concert Evaluation AssignmentIn a well-written essay of approx.docxmaxinesmith73660
Concert Evaluation Assignment
In a well-written essay of approximately 700 words (typed), create a review of a live professional concert relevant to this course. Discuss the strengths and weaknesses of the concert and focus mainly on the most important/striking elements and qualities of the performance. Use that information to arrive at an overall evaluation of the event.
Hints to keep you writing:
Describe: This is the very basic and factual content of your paper and should function as the introduction to your review. When was the concert? Where was it? Who performed? What kind of music was performed? Was there any particular theme to the concert? Was there anything unusual about the concert occasion? Don't list everything on the program, but do summarize the important information.
Analyze: To analyze something is to examine its components and, more than just describe them, to seek out relationships among the parts. What were the outstanding musical characteristics of the pieces? How did the pieces that were performed compare to each other? How do they compare to what you have learned in class and to music you have heard at other times? This section is where you may want to use the musical terms and concepts that have been discussed in class (but see the section below on using musical terminology).
Interpret: Did you find some meaning in the music? What do you think may have been the original significance of the music? Does it have a different significance today? What people might want to listen to this music, and why? There are no right or wrong answers to these kinds of questions, but always give examples and make supporting statements to back up your assertions.
Judge: Remember that a concert consists of two things: the music being performed and the people who are doing the performing. Don't confuse the two. Do talk about each one. Did you like the music, and why (or why not)? Regardless of whether you liked the music, did you think that the performers did a good job? What, if anything, do you think would have improved the performance? Again, these are individual and subjective responses, but give explanations for your judgments.
Disclaimer and General Guidelines: Do not write your review only by answering some or all of the questions listed in the above paragraphs. They are not intended to be a checklist of things that you must write about, but rather are suggestions to direct your thinking about various aspects of the music and performance. The principal goal of this assignment is for you to listen critically to a live performance and then to articulate in writing your observations and reactions. Originality of thought and clarity of expression are more important to this assignment than is addressing each and every point outlined above.
Using Program Notes: Many concert programs provide information about either the music being performed or the performers. These notes are for your benefit and can assist you in understanding .
1. Visualizing music artists’ main topics and overall sentiment by
analyzing lyrics
Abstract
This paper discusses the process of visualizing
music artists’ main topics and overall sentiment
by analyzing lyrics. While artists themselves
translate their lyrics into sound, we are chal-
lenged by the problem of visualizing their songs
and automatically generating a moodboard based
on written songs. This forms the main problem
of this article, because visualized lyrics can be
valuable for deaf people, and analyzing them
may be of commercial use for music software
companies. The approach to this problem in-
volves scraping the lyrics from the web, pre-
processing the scraped texts and analysis of top-
ics and overall sentiment. Furthermore, based on
the main topics, corresponding images are
scraped from the web and placed into the mood-
board. Based on the sentiment score, a matching
level of saturation is given to these images. Fi-
nally, some results are given and discussed fol-
lowed by a link to the final application.
1 Introduction
Every music artist is unique, just like every song
is unique. Songs can express positive or negative
emotions through their sounds and lyrics. While
humans can comprehend the emotions of a song
by simply listening to it, a different approach is
required for a Natural Language Processing sys-
tem to perform this task, since only the textual
component of lyrics can be utilized. Without
sound, it is more difficult to extract the sentiment
of a song, since all that is left are words. Another
problem is that lyrics do not follow the same
syntactic rules as informative texts. The language
structure that is adhered to in lyrics is more simi-
lar to the structure of poetry. Lyrics can be am-
biguous, since they often contain metaphors, idi-
oms and polysemous words; the interpretation is
left to the listener of the song, and can be inter-
preted differently by different people. To enable
an automated system to interpret lyrics is there-
fore a demanding task. However, lyrics might be
able to provide an automated system enough in-
formation about a song to detect its main topics
and overall sentiment. Therefore, in this research
is analyzed whether it is possible to use Natural
Language Processing in order to extract the sen-
timent of songs based on their lyrics only. It is
also analyzed how to extract the main topics of
an artist’s songs, that are embedded in the lyrics.
The challenge and main problem statement of
this paper is to express the extracted sentiment
and topics of the songs of an artist in a different
way than sound: visually, by generating a mood-
board. A moodboard as visualization method is
chosen because it has the convenience of carry-
ing the same ambiguity as a song, which makes it
a perfect fit for the domain.
The resulting moodboard of an artist’s main top-
ics and overall sentiment can be useful for sever-
al purposes. It would offer an opportunity for
deaf people to comprehend music by taking ad-
vantage of their visual senses. This can bring
them closer to a domain that they are often dis-
tanced from. Besides that, these moodboards
could also be useful for music software compa-
nies such as Spotify. Spotify is well aware of the
fact that music and mood are intertwined, and
anticipate on that by offering playlists based on
moods. An addition to these playlists would be
embedding the proposed moodboard of an art-
ist’s topics and sentiment, for an additional visu-
al sensation.
Kayleigh Beard
Vrije Universiteit
Amsterdam, Nederland
k.l.beard@student.vu.nl
Anita Tran
Vrije Universiteit
Amsterdam, Nederland
a.v.t.t.tran@student.vu.nl
Nathalie Post
Vrije Universiteit
Amsterdam, Nederland
n3.post@student.vu.nl
Laila van Ments
Vrije Universiteit
Amsterdam, Nederland
l.ments@student.vu.nl
2. 2 Data Used
2.1 Lyrics
The used data consists of lyrics from the website
‘http://www.songteksten.nl’. However, no data
is stored within the application itself. A user of
the application can query an artist, and only after
this query the data is retrieved by scraping all the
artist’s lyrics from ‘http://www.songteksten.nl’,
using ‘Scrapy’, a Python module for scraping
websites.
2.2 Images
Based on the queried artist, and the results of the
topic analysis, images are scraped from photo-
sharing website Flickr, ‘http://www.flickr.com’.
3 Methodology
This section describes the four separate steps that
take place within the application. After the lyrics
are scraped, the lyrics are preprocessed, as de-
scribed in Section 3.1. Subsequently, the main
topics of the lyrics are extracted (Section 3.2)
and sentiment analysis is conducted (Section
3.3). Finally, the results from the topic analysis
and sentiment analysis are used in order to gen-
erate a moodboard (Section 3.4).
3.1 Preprocessing the text
The scraped lyrics are preprocessed in order to
normalize the text. The first step of prepro-
cessing consists of sentence segmentation and
tokenization. After this, Part-Of-Speech tagging
is applied, in which each word of the text is
marked with a part of speech, based on its defini-
tion and context. Finally, redundant characters
and punctuation marks (colons, apostrophes, hy-
phens, strokes, parentheses and square brackets)
are removed from the text (Fokkens, 2015).
3.2 Topic analysis
The most meaningful words of a sentence are
keywords. In order to extract the main topics
from lyrics, the goal was to filter out the key-
words from the lyrics. Keywords are most often
contained in nouns, and therefore only nouns
were extracted from the text (Common Noun,
Proper Noun, Proper Noun Singular Form and
Proper Noun Plural Form), and stored in a list.
Not all the extracted nouns in this list are actual
keywords, and some of the extracted nouns
should not be part of the topic analysis. The
NLTK ‘Stopwords’ corpus is used in order to
filter out these words. However, the ‘Stopwords’
corpus does not filter out all redundant words,
especially the domain dependent words (such as
‘chorus’, and ‘verse’). During a thorough test
procedure of the application, an additional stop-
words list was generated in order to filter out
those redundant words as well.
After a list of proper keywords is established, a
frequency distribution of the most common
words in this list is generated. From this frequen-
cy distribution the ten most common words are
assembled, which represent the ten main topics
of the queried artist (Bird, S., Klein, E. and Lop-
er, E., 2009).
3.3 Sentiment Analysis
Several approaches can be used for sentiment
analysis (Maks, 2015). In this application, a po-
larity lexicon is used to assess the overall senti-
ment, by determining how many positive and
how many negative words appear in the lyrics
(Breen, 2011). The used algorithm is based on
the approach of F. Alba (Alba, 2012), and com-
prises of a few steps.
First, every word in the lyrics is compared to the
words in the opinion lexicon, which contains a
set of ‘positive polarity words’ and a set of ‘neg-
ative polarity words’. When a word contained in
the lyrics matches a word in the lexicon, it is an-
notated with a tag according to its polarity: posi-
tive or negative. However, this is not sufficient,
since this lexicon does not account for incre-
menters (‘very’, ‘super’) and decrementers
(‘barely’, ‘little’), which enhance or decrease the
strength of the sentiment. Besides that, inverters
(‘not’, ‘no’), invert the entire polarity of the
word, changing it from positive to negative or
vice versa. Therefore, additional dictionaries for
incrementers, decrementers and inverters are uti-
lized.
After every word is annotated with either a sen-
timent tag or none, the application keeps track of
two separate sentiment scores. One score keeps
track of all the positively classified words, the
other one of all the negatively classified words.
However, before a sentiment score is assigned to
a word, the previous tokens are checked for in-
crementers (in which case, the sentiment score
for that word is doubled), decrementers (in which
case, the sentiment score for that word is halved),
and inverters (in which case, the sentiment score
for that word is inverted).
3. The results of the sentiment analysis consist of
two scores: one positive score, and one negative
score. These resulting scores are not interpretable
without scaling. Some artists have many songs
and therefore many available lyrics, which re-
sults in higher scores than artists with fewer lyr-
ics. Therefore, the resulting sentiment scores are
scaled according to the amount of sentiment
tagged words, resulting in a percentage of posi-
tive sentiment carrying words and a percentage
of negative sentiment carrying words.
3.4 Moodboard Generation
The results of the topic- and sentiment analysis
are visualized in a moodboard. This moodboard
generation consists of two steps. First, for each
of the ten main topics determined in the topic
analysis, five corresponding images are scraped
from Flickr, as well as five images of the artist.
Second, the resulting sentiment score from the
sentiment analysis is translated into the amount
of saturation in the moodboard. The higher the
sentiment score (thus, the more positive words in
the songs), the higher the amount of saturation in
the pictures. The lower the sentiment score (thus,
the more negative words in the songs), the lower
the amount of saturation in the pictures.
4 Results
The resulting application from this research is
able to generate a moodboard, based on topic
analysis and sentiment analysis of the queried
artist’s lyrics. To assess the performance of a
Natural Language Processing system, often a
quantitative analysis is used. However, since
there was no annotated dataset of lyrics, this was
impossible. Therefore, in order to determine the
performance of the system, a manual approach
was used, in which a range of different artists
was queried. A handful of results from these que-
ries are attached in Appendix I. It is difficult to
establish a valid performance measure of these
results. However, the fact that it is not possible to
provide an exact performance measure is not ex-
tremely relevant for the purpose of the applica-
tion. The resulting topic- and sentiment analysis
of the application are visualized in a moodboard,
and moodboards are not an exact science. The
purpose of a moodboard is to project an overall
mood in a visual way, so even when the accuracy
of the topic analysis and sentiment analysis are
below optimal, this isn’t immediately observable
in the moodboard. Whether the moodboard por-
trays an artist’s sentiment and topics accurately
is almost as ambiguous as the lyrics themselves,
and is left to the interpretation of the user.
5 Discussion
There are many parts of the developed applica-
tion that can be improved. A few proposed future
improvements are described in this section.
As stated before, lyrics do not follow the same
syntactic structure such as informative texts,
which makes it a challenging task for a Natural
Language Processing system to correctly deter-
mine the Part-Of-Speech of each word. Words
were not always assigned the correct Part-Of-
Speech, which resulted in words being incorrect-
ly identified as nouns. This sometimes led to
non-keywords being identified as keywords, and
incorrectly projected on the moodboard. Even
though this misclassification often isn’t visible
due to the ambiguity of the moodboard, it is a
flaw in the application. A domain specific Part-
Of-Speech tagger could be useful in order to re-
solve this problem.
Also, even though the lexicons used for the sen-
timent analysis were extensive, they were not
domain specific, which can lead to inaccurate
results. Besides that, the positive polarity lexicon
consisted of 2002 words, while the negative po-
larity lexicon consisted of 4767 words. It has not
been researched for the purposes of this applica-
tion whether this ratio is a correct representation
of sentiment carrying words in English language.
If not, it is possible that the used lexicons result
in a bias toward the negative polarity lexicon.
Therefore, research about the correct ratio of
positive and negative polarity carrying words is
necessary to improve the accuracy.
Furthermore, only a rule-based approach was
used for this application. It could be useful to
explore whether machine learning or hybrid ap-
proaches would yield better results.
Finally, this application is a ‘stand-alone’ appli-
cation right now, but if it would be integrated in
a music software system such as Spotify, it
would be a great improvement if the application
could be personalized. Different people interpret
songs in a different way, so an addition to the
system would be an opportunity for the user to
4. provide feedback about the resulting visualiza-
tions. This way, the parameters of the application
could be tuned according to the user, which
could lead to a better user experience.
6 Link to Application
The application is not published online, however,
the code is made available to download at
https://www.dropbox.com/s/241gr8r9cglf8eg/Vis
ual_Songs.zip.
7 Group Work Summary
All group members brainstormed together about
the idea and worked their way through the lab
sessions. The actual application was mostly built
by Kayleigh and Nathalie, because they have
more experience with programming in Python
than Laila and Anita. Nathalie was responsible
for the scraper, the web-application using Python
CGI, and the sentiment analysis. Kayleigh was
responsible for the topic analysis and the mood-
board with images scraped from Flickr. The final
report was written by Laila, Anita, Kayleigh and
Nathalie together.
8 References
Alba, F. “Basic Sentiment Analysis in Python”. 1
Nov. 2012. Web. 23 Mar. 2015.
<http://fjavieralba.com/basic-sentiment-
analysis-with-python.html>.
Breen, J. “Twitter Sentiment Analysis Tutorial
201107: Opinion Lexicon English”. Git
hub. 12 Jul. 2011. Web. 23 Mar. 2015.
<https://github.com/jeffreybreen/twitter-
sentiment-analysis-tutorial-201107/tree
/master/data/opinion-lexicon-English>.
Bird, S., Klein, E. and Loper, E. Natural Langu
age Processing with Python, 79-128.
First Edition (2009). California: O’Reilly
Media Inc. Web.
Fokkens, A. “Introduction to NLP”. Blackboard
Learn VU. Web, Lecture 10 Feb. 2015.
Maks, I. “Text Mining 2015: Sentiment Analysis
& Opinion Mining”. Blackboard Learn
VU. Web, Lecture 24 Feb. 2015.
5. Spice Girls
The results of the sentiment analysis of Spice Girls are: 68 percent of classified words was positive,
and 31 percent of the words was negative.
The main topics of Spice Girls are: time, love, come, something, night, fun, baby, lover, deeper.
The resulting moodboard is displayed in image 1.
Image 1: Resulting moodboard for the Spice Girls.
Ellie Goulding
The results of the sentiment analysis of Ellie Goulding are: 43 percent of classified words was posi-
tive, and 56 percent of the words was negative.
The main topics of Ellie Goulding are: burn, love, time, baby, heart, fire, lights, life, anything.
The resulting moodboard is displayed in image 2.
Image 2: Resulting moodboard for Ellie Goulding.
Appendix I
6. Slipknot
The results of the sentiment analysis of Slipknot are: 23 percent of classified words was positive,
and 76 percent of the words was negative.
The main topics of Slipknot are: inside, build, fuck, life, everything, end, goodbye, man, eyes.
The resulting moodboard is displayed in image 3.
Image 3: Resulting moodboard for Slipknot.
ABBA
The results of the sentiment analysis of ABBA are: 53 percent of classified words was positive,
and 46 percent of the words was negative.
The main topics of ABBA are: man, mother, honey, waterloo, midnight, take, elaine, nothing.
The resulting moodboard is displayed in image 4.
Image 4: Resulting moodboard for ABBA.