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Publication Type : Conference Paper
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Source : Lecture Notes in Networks and Systems (2021)
Url : https://link.springer.com/chapter/10.1007/978-981-33-4305-4_7
ISBN : 9789813343047
Campus : Coimbatore
School : School of Artificial Intelligence, School of Artificial Intelligence - Coimbatore, School of Engineering
Center : Center for Computational Engineering and Networking, Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication
Year : 2021
Abstract : With the evolving digital era, the amount of online data generated such as product reviews in different languages via various social media platforms. Information analysis is very beneficial for many companies such as online service providers. This task of interpreting and classifying the emotions behind the text (review) using text analysing techniques is known as sentiment analysis (SA). Sometimes, the sentence might have positive as well as negative polarity at the same time, giving rise to conflict situations where the SA models might not be able to predict the polarity precisely. This problem can be solved using aspect-based sentiment analysis (ABSA) that identifies fine-grained opinion polarity towards a specific aspect associated with a given target. The aspect category helps us to understand the sentiment analysis problem better. ABSA on the Hindi benchmark dataset, having reviews from multiple web sources, is performed in this work. The proposed model has used two different word embedding algorithms, namely Word2Vec and fastText for feature generation and various machine learning (ML) and deep learning (DL) models for classification. For the ABSA task, the LSTM model outperformed other ML and DL models with 57.93 and 52.32% accuracy, using features from Word2Vec and fastText, respectively. Mostly, the performance of classification models with Word2Vec embedding was better than the models with fastText embedding.
Cite this Research Publication : Sai Aparna, T., Simran, K., Premjith, B., Soman, K. P., "Aspect-Based Sentiment Analysis in Hindi: Comparison of Machine/ Deep Learning Algorithms," (2021) Lecture Notes in Networks and Systems, 173 LNNS, pp. 81-91., DOI: 10.1007/978-981-33-4305-4_7