A Dataset for the Sentiment Analysis of Indo-Pak Music Industry

Asif, Muhammad; Qureshi, Muhammad Aasim; Abid, Adnan; Kamal, Asad

doi:10.1109/icic48496.2019.8966720

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…For the same text (i.e., word) different patterns (mix of lowerand upper-case letters) were found like good, Good, GOOD, that type of data (both uppercase and lowercase) cases issues in classification, classifiers found different deviations in results. To avoid these issues all the text is converted in lowercase [44].…”

Section: Lowercasingmentioning

confidence: 99%

Aspect Level Songs Rating Based Upon Reviews in English

Qureshi¹,

Asif²,

Anwar³

et al. 2023

Computers, Materials &Amp; Continua

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With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms-Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.

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Section: Lowercasingmentioning

confidence: 99%

Aspect Level Songs Rating Based Upon Reviews in English

Qureshi¹,

Asif²,

Anwar³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Recent studies like [46][47][48], have witnessed that researchers are annotating the text manually and some of them by using TextBlob [49][50][51][52]. There exist tools like PDFAnno [41], MyMiner [39], BtableRAT [32] for text annotation, but no literature has witnessed text annotation for sentiment analysis at the aspect level.…”

Section: Annotation For Text Datamentioning

confidence: 99%

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

Qureshi¹,

Asif²,

Hassan³

et al. 2022

Computers, Materials &Amp; Continua

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In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects-voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset-Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.

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“…Development of dataset had been attempted, previously, but most of them are of small sizes. As [24], [25] and [33] used dataset of 300, 600 and 1600 Roman Urdu reviews. In English, Chinese, and affluent languages benchmark datasets are available.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study

Qureshi

Asif

Hassan³

et al. 2022

IEEE Access

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Opinion Mining from user reviews is an emerging field. Sentiment Analysis of Natural Language text helps us in finding the opinion of the customers. These reviews can be in any language e.g. English, Chinese, Arabic, Japanese, Urdu, and Hindi. This research presents a model to classify the polarity of the review(s) in Roman Urdu text (reviews). For the purpose, raw data was scraped from the reviews of 20 songs from Indo-Pak Music Industry. In this research a new dataset of 24000 reviews of Roman Urdu text is created. Nine Machine Learning algorithms-Naïve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Networks, ID3 and Gradient Boost Tree, are attempted. Logistic Regression outperformed the rest, based on testing and cross validation accuracies that are 92.25% and 91.47% respectively.

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A Dataset for the Sentiment Analysis of Indo-Pak Music Industry

Cited by 9 publications

References 16 publications

Aspect Level Songs Rating Based Upon Reviews in English

Aspect Level Songs Rating Based Upon Reviews in English

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study

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