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A Comprehensive Machine-Learning-Based Approach for Aspect-Based Sentiment Analysis Over Food Consumables

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Evolution in Signal Processing and Telecommunication Networks (ICMEET 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1155))

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Abstract

Due to the large growth of web technologies and online resources, there is high information made available to everyone. With the growth of technology and resources, people are expressing their opinions and thoughts more comfortably. It impacts the decision-making of everyone, the mindset of the customer is based on the reviews of the other users in the web resources. Hence, going through all the reviews and getting to a final conclusion about the product has become a major issue nowadays. The solution to the problem is provided by sentiment analysis. Sentiment analysis derives the final polarity of the product by analyzing all the reviews of users. The overall sentiment polarity is affected by aspects within the domain, knowing the polarity of every aspect in the considered domain can be done by ABSA (aspect-based sentiment analysis). Aspect-based sentiment analysis is developed using many different solutions. Among them, deep learning and machine-learning methods made a great impact on the development of ABSA. In ABSA, the primary feature to be taken into consideration is aspects of domain is polarity. Choosing the most suitable model from the different models which are available is the difficult part. A comparative analysis is performed between ML (machine learning) and DL (deep learning) models.

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Authors and Affiliations

  1. Department of Information Technology, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

    T. LahariSuvarchala, C. S. Pavan Kumar & V. Deepthi Sasidhar

Authors
  1. T. LahariSuvarchala
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  2. C. S. Pavan Kumar
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  3. V. Deepthi Sasidhar
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Corresponding author

Correspondence to C. S. Pavan Kumar .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology, Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India

    P. Satish Rama Chowdary

  3. Autonomous University of Baja California, Mexicali, Baja California, Mexico

    Wendy Flores-Fuentes

  4. Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

    Shabana Urooj

  5. Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, Aizawl, India

    Rudra Sankar Dhar

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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LahariSuvarchala, T., Kumar, C.S.P., Sasidhar, V.D. (2024). A Comprehensive Machine-Learning-Based Approach for Aspect-Based Sentiment Analysis Over Food Consumables. In: Bhateja, V., Chowdary, P.S.R., Flores-Fuentes, W., Urooj, S., Sankar Dhar, R. (eds) Evolution in Signal Processing and Telecommunication Networks. ICMEET 2023. Lecture Notes in Electrical Engineering, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-97-0644-0_21

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  • DOI: https://doi.org/10.1007/978-981-97-0644-0_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0643-3

  • Online ISBN: 978-981-97-0644-0

  • eBook Packages: EngineeringEngineering (R0)

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