Aspect-Based Sentiment Analysis Task

An Aspect-Based Sentiment Analysis Task is a sentiment analysis task that requires the identification and analysis of the sentiment toward specific aspects within a text.

• Context:
  • It can (typically) involve natural language processing, text analysis, and computational linguistics to identify sentiment, opinion, or emotion expressed toward specific aspects or features of a product, service, or topic.
  • It can (often) be used to analyze customer feedback, reviews, or social media mentions to understand detailed consumer sentiment.
  • It can help businesses and researchers to pinpoint specific strengths and weaknesses in products or services, rather than obtaining a general sentiment.
  • It can employ machine learning techniques, including supervised and unsupervised learning, to categorize sentiments towards different aspects.
  • It can (typically) require a fine-grained analysis that distinguishes between the sentiment toward the overall entity and its individual aspects.
  • It can range from being a Aspect-Based Sentiment Classification Task to being a Aspect-Based Sentiment Clustering Task.
  • ...
• Example(s):
  • Aspect-Based Sentiment Classification, such as:
    • [math]\displaystyle{ f }[/math]("The screen resolution on this smartphone is amazing, but the battery life is too short.") ⇒ {"Screen Resolution": Positive Polarity, "Battery Life": Negative Polarity}.
    • [math]\displaystyle{ f }[/math]("The camera quality is superb, although the device feels a bit heavy.") ⇒ {"Camera Quality": Positive Polarity, "Device Weight": Negative Polarity}.
    • [math]\displaystyle{ f }[/math]("I love the user interface and the variety of apps available, but the call quality could be better.") ⇒ {"User Interface": Positive Polarity, "App Variety": Positive Polarity, "Call Quality": Negative Polarity}.
    • [math]\displaystyle{ f }[/math]("The laptop's performance is top-notch, but it's quite pricey.") ⇒ {"Performance": Positive Polarity, "Price": Negative Polarity}.
  • ...
• Counter-Example(s):
  • General sentiment analysis that provides an overall positive or negative sentiment without distinguishing between different aspects or topics.
  • Keyword-based sentiment analysis that simply counts positive or negative words without understanding the context or the specific aspects they relate to.
• See: Natural Language Processing, Text Analysis, Computational Linguistics, Machine Learning, Supervised Learning, Unsupervised Learning.

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References

2019

• (Doa et al., 2019) ⇒ Hai H. Doa, Penatiyana WC Prasad, Angelika Maag, and Abeer Alsadoon. (2019). “Deep Learning for Aspect-based Sentiment Analysis: A Comparative Review.” Expert Systems with Applications, 118 https://doi.org/10.1016/j.eswa.2018.10.003
  • ABSTRACT: The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

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