<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.</p>
</abstract>

Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.

Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment...

Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

Online veröffentlicht: 30. Juni 2023

Seitenbereich: 181 - 209

Eingereicht: 07. Nov. 2022

Akzeptiert: 18. Mai 2023

DOI: https://doi.org/10.2478/fcds-2023-0008

Schlüsselwörter
Sentiment Analysis, Opinion Mining, Twitter, Active Learning, Deep Learning

© 2023 Rathan Muralidhar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

Online veröffentlicht: 30. Juni 2023

Seitenbereich: 181 - 209

Eingereicht: 07. Nov. 2022

Akzeptiert: 18. Mai 2023

DOI: https://doi.org/10.2478/fcds-2023-0008

SchlüsselwörterSentiment Analysis, Opinion Mining, Twitter, Active Learning, Deep Learning

© 2023 Rathan Muralidhar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Sentiment Analysis, Opinion Mining, Twitter, Active Learning, Deep Learning