Mood classifiaction of lyrics using SentiWordNet

Kumar, Vipin; Minz, Sonajharia

doi:10.1109/iccci.2013.6466307

Cited by 17 publications

(6 citation statements)

References 9 publications

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“…Therefore, in order to use mining algorithms for classifying text documents effectively, feature selection process is considered very important. Studies exist that have used combination of feature selections process and Information Gain Ratio (IGR) for classifying various types of text data such as lyrics [40] and poems [39,41].…”

Section: 1text Categorizationmentioning

confidence: 99%

Review of feature selection methods for text classification

Iqbal¹,

Abid²,

Khalid³

et al. 2020

IJACR

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We can also define this problem as: assigning a document D to some pre-determined set of categories |C|. Where pre-determined set of categories, C, is defined by equation 1.() In short, text classification is used for categorizing documents into predefined classes based on their contents. This is an automatic assignment process for text categorization. Text classification is the initial requirement of Text Retrieval Systems (TRS), which digs texts in response to user queries. Nowadays, different ML algorithms are in practice to manage and organize documents for Information Retrieval Systems (IRS) [4].

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Section: 1text Categorizationmentioning

confidence: 99%

Review of feature selection methods for text classification

Iqbal¹,

Abid²,

Khalid³

et al. 2020

IJACR

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“…One of the lyrics features is psycholinguistic feature. these features can be presented differently depending on model of emotion and type of corpus used.Vipin Kumar extract psycolinguistic features lyrics from Sentiwordnet [14]. Sentiwordnet is a corpus that has a positive-negative score [15].…”

Section: Music Emotion Classification Based On Lyrics-audio Using Cbementioning

confidence: 99%

Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion

Rachman

Sarno

Fatichah

2018

IJECE

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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%.

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“…Feature Level or Aspect level is used to analyze the sentiment of a statement at a lower level and directly looks at the sentiment itself [2]. Feature based sentiment classification done in previous research work [3,4] was based on feature selection and extraction which was done by finding the sentiment words in the document and also the feature to which they refer. When it comes to feature extraction the sentence on which opinion is given has some target which needs to be extracted.…”

Section: Related Workmentioning

confidence: 99%

Customers Sentiment on Banks

Chaturvedi¹,

Chopra²

2014

IJCA

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Social media has revolutionized the new-age customer's decision making through the myriads of sources available to them like online feedback or reviews, forum discussions, blogs and Twitter on the web. There is no need for them to depend on their peers any longer. When more convenient and efficient sources like user reviews are readily available to them over the internet. Vast and authentic information about all possible products ranges and services are at a click away. Even for commercial organization the task of gathering public opinion has been rendered tremendously easy, for the same reason that taking opinion polls and conducting surveys are now much simpler due to the abundance of information on the web. However, finding and monitoring opinion sites on the Web and filtering the information contained in them according to our need remains a difficult task because of the rapid increase in the number of distinct sites. Each site usually contains a huge volume of opinionated text which is difficult for any individual to go through. The average human reader will have difficulty identifying relevant sites and extracting and summarizing the opinions in them. Automated sentiment analysis systems are thus needed. This paper focuses on extracting the features from bank reviews taken from mouthshut.com and myBankTracker.com sites given by reviewers to state their opinions. This is done at aspect level of analysis using ontology. Then it determines whether they are positive or negative. Output of such analysis is then summarized.

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Mood classifiaction of lyrics using SentiWordNet

Cited by 17 publications

References 9 publications

Review of feature selection methods for text classification

Review of feature selection methods for text classification

Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion

Customers Sentiment on Banks

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