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Deep Learning Approaches for Textual Sentiment Analysis

Deep Learning Approaches for Textual Sentiment Analysis

Tamanna Sharma, Anu Bajaj, Om Prakash Sangwan
Copyright: © 2022 |Pages: 12
ISBN13: 9781668463031|ISBN10: 1668463032|EISBN13: 9781668463048
DOI: 10.4018/978-1-6684-6303-1.ch014
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MLA

Sharma, Tamanna, et al. "Deep Learning Approaches for Textual Sentiment Analysis." Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, IGI Global, 2022, pp. 256-267. https://doi.org/10.4018/978-1-6684-6303-1.ch014

APA

Sharma, T., Bajaj, A., & Sangwan, O. P. (2022). Deep Learning Approaches for Textual Sentiment Analysis. In I. Management Association (Ed.), Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 256-267). IGI Global. https://doi.org/10.4018/978-1-6684-6303-1.ch014

Chicago

Sharma, Tamanna, Anu Bajaj, and Om Prakash Sangwan. "Deep Learning Approaches for Textual Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, 256-267. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6303-1.ch014

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Abstract

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

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