The week's pick
Reseach Article
Customer Reviews’ Sentiments Analysis using Deep Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 30 |
| Year of Publication: 2020 |
| Authors: Kusum Mehta, Supriya P. Panda |
10.5120/ijca2020920842
|
Kusum Mehta, Supriya P. Panda . Customer Reviews’ Sentiments Analysis using Deep Learning. International Journal of Computer Applications. 175, 30 ( Nov 2020), 27-31. DOI=10.5120/ijca2020920842
Abstract
In this era of digitalization, Sentiment Analysis(SA) has become a necessity for progress and prosperity in marketing. Sentiment analysis has become a powerful way of knowing the opinions and thoughts of users. The viewpoint of the consumer, such as knowledge sharing would include a lot of useful experience, while one wrong idea will cost too much for the company.SA has many social media data-related problems, such as natural language interpretation, etc. Issue of theory and technique also affect the accuracy of detecting the polarity. There is a problem of text classification such as analysis of sentiments in document level, sentence level, feature based. Document level analysis is done by two approaches: supervised learning and unsupervised learning. Sentence level contains sentences containing opinions. Aspect based analysis have different attributes is performed in customer reviews. Product opinion is taken for knowing the sentiments. Opinions are compared and are extracted as a feature.SA is very important for business purpose because it gives the way for improving their operations and the products they offer. For improving business strategy it plays an important role. It provides many key benefits like impactful decisions, for finding relevant products, improving business strategy, beating competitions, tackling positive or negative issues affecting the product. To overcome such issues, deep learning processes are applied. The work focuses on two main tasks. Firstly, to extract sentiments present in data of social media of customers' reviews and secondly, to use the deep-learning process for the sentiments' extraction for customer reviews.
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