Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model

Authors

  • Shilpi Gupta Shobhit Institute of Engineering & Technology (Deemed to-be University), Meerut, 250110, India
  • Niraj Singhal Sir Chhotu Ram Institute of Engineering & Technology, Chaudhary Charan Singh University, Meerut, India
  • Sheela Hundekari School of Engineering and Technology, Pimpri Chinchwad University, Maval Talegaon, Pune, India
  • Kamal Upreti CHRIST University, Delhi NCR Campus, Ghaziabad, India
  • Anjali Gautam Manav Rachna International Institute of Research & Studies, Faridabad, Haryana
  • Pradeep Kumar JSS Academy of Technical Education, Noida, India
  • Rajesh Verma CHRIST University, Delhi NCR Campus, Ghaziabad, India

DOI:

https://doi.org/10.13052/jmm1550-4646.2048

Keywords:

Sentiment analysis, NLP, Text processing, Aspect sentiment extraction, DL based sentiment classification

Abstract

In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models.

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Author Biographies

Shilpi Gupta, Shobhit Institute of Engineering & Technology (Deemed to-be University), Meerut, 250110, India

Shilpi Gupta is pursuing her Ph.D. from Shobhit Institute of Engineering and Technology, (Deemed-to-be University), Meerut. She is currently working as an Assistant Professor in the Department of Computer Science. She has guided various students for their project work. She is having more than fifteen years of teaching experience. Her area of interest are Sentiment Analysis, Natural Language Processing and Data Mining.

Niraj Singhal, Sir Chhotu Ram Institute of Engineering & Technology, Chaudhary Charan Singh University, Meerut, India

Niraj Singhal is Graduate in Computer Science and Engineering, M.Tech. in Computer Engineering and, Ph.D. (Computer Engineering & Information Technology). He is Fellow, Senior Member and member of several National/International bodies. He is serving as reviewer and member of advisory board for several International/National journals and International/National Conferences also. He has guided hundreds of undergraduate engineering students for their project work and postgraduate engineering students for their thesis work. He has guided many Ph.D. scholars and, also guiding many. He has more than one hundred and fifty research publications to his credit in National/International journals/conferences of repute. He has twenty-eight years of rich experience of administration, coordinating, supervising and teaching at various levels. Presently, he is working as Director at Sir Chhotu Ram Institute of Engineering and Technology (SCRIET), Chaudhary Charan Singh University, Meerut (NAAC A++ Accredited). His area of interest includes web information retrieval, smart cities and software agents.

Sheela Hundekari, School of Engineering and Technology, Pimpri Chinchwad University, Maval Talegaon, Pune, India

With an illustrious academic journey spanning over 25 years, Currently working with renowned University, Pimpri Chinchwad University, Pune. Professor Dr. Sheela Hundekari embodies a remarkable blend of scholarly prowess and practical expertise. Armed with a Ph.D. and a diverse array of qualifications including an MBA, MCA, and MCM, their academic voyage has been marked by an insatiable thirst for knowledge and an unwavering commitment to excellence.

Dr. Sheela Hundekari is not merely confined to the academic realm; they have garnered acclaim on a global scale through their certifications and training endeavors. Holding five prestigious certifications, including three in Java and two in Oracle, Professor Dr. Sheela stands as a paragon of technical proficiency and industry relevance. Their designation as a NASSCOM certified trainer further solidifies their status as a leading authority in the field.

In addition to their instructional prowess, Professor Dr. Sheela Hundekari is an avid researcher, with a prolific portfolio of national and international research papers to their credit. Their contributions to the academic discourse have not only enriched their respective fields but have also spurred innovation and progress.

Kamal Upreti, CHRIST University, Delhi NCR Campus, Ghaziabad, India

Kamal Upreti is currently working as an Associate Professor in Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He completed is B. Tech (Hons) Degree from UPTU, M. Tech (Gold Medalist), PGDM (Executive) from IMT Ghaziabad and PhD in Department of Computer Science & Engineering. He has completed Postdoc from National Taipei University of Business, TAIWAN funded by MHRD.

He has published 50+ Patents, 32+ Magazine issues and 110+ Research papers in in various reputed Journals and international Conferences. His areas of Interest such as Modern Physics, Data Analytics, Cyber Security, Machine Learning, Health Care, Embedded System and Cloud Computing. He has published more than 45+ authored and edited books under CRC Press, IGI Global, Oxford Press and Arihant Publication. He is having enriched years’ experience in corporate and teaching experience in Engineering Colleges.

He worked with HCL, NECHCL, Hindustan Times, Dehradun Institute of Technology and Delhi Institute of Advanced Studies, with more than 15+ years of enrich experience in research, Academics and Corporate. He also worked in NECHCL in Japan having project – “Hydrastore” funded by joint collaboration between HCL and NECHCL Company. He has completed project work with Joint collaboration with GB PANT & AIIMS Delhi, under funded project of ICMR Scheme on Cardiovascular diseases prediction strokes using Machine Learning Techniques from year 2017–2020 of having fund of 80 Lakhs. He got 3 Lakhs fund from DST SERB for conducting International Conference, ICSCPS-2024, 13–14 Sept 2024. Recently, he got 10 Lakhs fund from AICTE – Inter-Institutional Biomedical Innovations and Entrepreneurship Program (AICTE-IBIP) for 2024–2026. He has attended as a Session Chair Person in National, International conference and key note speaker in various platforms such as Skill based training, Corporate Trainer, Guest faculty and faculty development Programme. He awarded as best teacher, best researcher, extra academic performer and Gold Medalist in M. Tech programme.

Anjali Gautam, Manav Rachna International Institute of Research & Studies, Faridabad, Haryana

Anjali Gautam is working as an Associate Professor in the School of Computer Applications, Manav Rachna International Institute of Research & Studies. She holds a Doctorate Degree in Computer Science from University of Delhi, where her research contributed significantly to develop Recommender Systems for large-scale data. With her background in computer science, she has developed a keen interest in machine learning models, cyber-physical systems & data mining. Dr. Anjali has published numerous papers in reputable indexed journals and has presented her work at various international conferences. She is also actively involved in other research related activities such as chairing a session in international conference and a reviewer for research publication. Dr. Anjali’s work continues to influence and shape the understanding of machine learning, driving further advancements in the field.

Pradeep Kumar, JSS Academy of Technical Education, Noida, India

Pradeep Kumar working as assistant professor in computer science and engineering JSS Academy of technical education Noida. He has completed Ph.D. computer engineering and engineering at Department of Computer Engineering Shobhit Institute of Engineering & Technology (Deemed-to-be University), Meerut, 250110. He has obtained his M.Tech. in Computer Science and engineering Department of Computer Engineering Shobhit Institute of Engineering & Technology (Deemed-to-be University), with first class. He obtained his B.Tech in Computer Engineering and engineering degree from college of engineering Roorkee, India in 2006 with first class.

Rajesh Verma, CHRIST University, Delhi NCR Campus, Ghaziabad, India

Rajesh Verma, is an Associate Professor at Christ University, Delhi NCR Campus, is celebrated for his expertise in management education. Holding a Ph.D. in Management from SMVDU, Katra, he has cleared the UGC NET in Management and holds an MBA (Marketing) from MDU Rohtak and a PGDM (Marketing) from Lal Bahadur Shastri Institute of Management, Delhi. With nearly two decades in academia, His specializes in Marketing Management, Business Research Methods, Statistics for Management, and Strategic Management. He has co-authored influential books on these subjects, exemplifying his scholarly contributions. As a faculty and mentor, he ensures academic excellence and fosters critical thinking and research skills among students. His extensive research at national and international levels underscores his commitment to advancing management knowledge, making him a pivotal figure at Christ University in nurturing future management leaders.

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Published

2024-10-01

How to Cite

Gupta, S., Singhal, N., Hundekari, S., Upreti, K., Gautam, A., Kumar, P., & Verma, R. (2024). Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model. Journal of Mobile Multimedia, 20(04), 935–960. https://doi.org/10.13052/jmm1550-4646.2048

Issue

2024: Vol 20 Iss 4

Section

Articles