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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Aspect Extraction and Aspect based sentiment analysis
Organization Unit
Authors
  • Nivedita Nivedita
Supervisors
  • Martin Volk
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Date 2022
Abstract Text Aspect extraction (AE) and aspect-based sentiment analysis (ABSA) are tasks that aim to identify ”topics” and sentiment polarity of these topics in the text corpus. In industry, aspects in the review texts are the categories/topics talked about in the review text and are usually the features of a product/business. Obtaining feedback/opinion on these features is an essential part of feature improvement and, in turn, customer satisfaction for businesses. Consequently, a mechanism to read and extract information from the plethora of feedback available becomes an essential task. Extracting the sentiment polarities of these categories is done by a more complicated method than sentiment analysis known as aspect-based sentiment analysis (ABSA). Aspect-based sentiment analysis aims at extracting the sentiment of each of the categories/topics from the text. Aspect-based sentiment analysis is a sub-task of sentiment analysis (SA) but is especially challenging since there can be varying numbers of aspects mentioned and each with a different sentiment polarity. Moreover, the availability of labelled industrial data is limited and restricts the use of various supervised machine learning algorithms. High accuracy is also expected in industries to be able to make business decisions from model predictions. In order to solve this issue, in this paper, we aim to extract these aspects and their sentiment polarity using BERTBASE model using a weakly supervised technique and obtain high accuracy such that it can be used by businesses to make data driven business decisions for feature improvements in their product. BERTBASE uses unsupervised masked (Masked Language model) training and next sentence prediction (NSP) for learning. We propose a method to modify the unsupervised masked language model, training of the model to make it more category aware. We then, fine tune the weights of this pre-train the model using the auxiliary sentence pairs to extract aspects and predict its sentiment polarity to get state-of-the-art results.
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