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Aspect-Based Sentiment Analysis of Financial Headlines and Microblogs

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Deep Learning-Based Approaches for Sentiment Analysis

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

To improve the performance in e-commerce markets, big giants like Amazon, Myntra and Flipkart are providing consumers with a platform to review their services and also give them an opportunity to provide a useful insight of the service to the future buyers. On the other hand, companies use such reviews to make a significant upgradation in their products (or services) to survive in the competition from others in the market. This shows the importance of studying user views or opinions on a particular product (or service) consumed by users. In Natural Language Processing (NLP), the process of studying such user opinion is termed as opinion mining. It is a task of finding out overall sentiment present in a review. Past research in this area has assumed that a sentence cannot have multiple sentiments associated with it. However, this is not true. For example, “This car looks beautiful, but does not handle very well.” comprises a positive sentiment towards the looks of the car but a negative sentiment towards its handling. To address such issues, aspect-based sentiment analysis (ABSA) was introduced. ABSA aims to detect an aspect (i.e. features) in a given text and then perform sentiment analysis of the text with respect to that aspect. The chapter aims to discuss the concept of ABSA for the problem introduced as a FiQA 2018 challenge subtask 1 (https://sites.google.com/view/fiqa) in WWW 2018 shared task. It highlights all the state-of-the-art models in the domain and discusses some new approaches. We propose neural network models combined with hand-engineered features and attention mechanism, to perform ABSA on financial headlines and microblogs. Our proposed model outperformed the existing state-of-the-art results in sentiment part by 50% and in the aspect part by 20%.

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Notes

  1. 1.

    https://spacy.io/.

  2. 2.

    http://networkx.github.io/.

  3. 3.

    https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit.

  4. 4.

    https://blog.minitab.com/blog/adventures-in-statistics-2/five-reasons-why-your-r-squared-can-be-too-high.

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Author information

Authors and Affiliations

  1. Indraprastha Institute of Information Technology, Delhi, India

    Hitkul, Shivangi Singhal, Debanjan Mahata, Ponnurangam Kumaraguru & Rajiv Ratn Shah

  2. Delhi Technological University, Delhi, India

    Simra Shahid

Authors
  1. Hitkul
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  2. Simra Shahid
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  3. Shivangi Singhal
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  4. Debanjan Mahata
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  5. Ponnurangam Kumaraguru
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  6. Rajiv Ratn Shah
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Corresponding author

Correspondence to Rajiv Ratn Shah .

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Editors and Affiliations

  1. Indian Institute of Information Technology Kota (IIIT-Kota), Jaipur, Rajasthan, India

    Basant Agarwal

  2. Faculty of Science and Engineering, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

    Richi Nayak

  3. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Namita Mittal

  4. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

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Hitkul, Shahid, S., Singhal, S., Mahata, D., Kumaraguru, P., Shah, R.R. (2020). Aspect-Based Sentiment Analysis of Financial Headlines and Microblogs. In: Agarwal, B., Nayak, R., Mittal, N., Patnaik, S. (eds) Deep Learning-Based Approaches for Sentiment Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1216-2_5

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  • DOI: https://doi.org/10.1007/978-981-15-1216-2_5

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