Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks
- PDF / 2,270,811 Bytes
- 40 Pages / 439.37 x 666.142 pts Page_size
- 51 Downloads / 200 Views
Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks Jitendra V. Tembhurne 1
& Tausif Diwan
1
Received: 9 June 2020 / Revised: 31 August 2020 / Accepted: 6 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Social networking platforms have witnessed tremendous growth of textual, visual, audio, and mix-mode contents for expressing the views or opinions. Henceforth, Sentiment Analysis (SA) and Emotion Detection (ED) of various social networking posts, blogs, and conversation are very useful and informative for mining the right opinions on different issues, entities, or aspects. The various statistical and probabilistic models based on lexical and machine learning approaches have been employed for these tasks. The emphasis was given to the improvement in the contemporary tools, techniques, models, and approaches, are reflected in majority of the literature. With the recent developments in deep neural networks, various deep learning models are being heavily experimented for the accuracy enhancement in the aforementioned tasks. Recurrent Neural Network (RNN) and its architectural variants such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) comprise an important category of deep neural networks, basically adapted for features extraction in the temporal and sequential inputs. Input to SA and related tasks may be visual, textual, audio, or any combination of these, consisting of an inherent sequentially, we critically investigate the role of sequential deep neural networks in sentiment analysis of multimodal data. Specifically, we present an extensive review over the applicability, challenges, issues, and approaches for textual, visual, and multimodal SA using RNN and its architectural variants. Keywords Sentiment analysis . Emotion detection . Deep learning . Recurrent neural network . Long short term memory . Gated recurrent unit
* Jitendra V. Tembhurne [email protected] Tausif Diwan [email protected]
1
Department of Computer Science & Engineering, Indian Institute of Information Technology, Nagpur, India
Multimedia Tools and Applications
1 Introduction Now a day, the internet is the integral part of society includes people, organizations, businesses, industries, etc. This is possible by the tremendous growth in communication media and underlying technology such as 4G and 5G. This leverages the availability of communication medium (Internet) to the wide spectrum of applications such as e-commerce, online banking, stock market, social media, etc. Hence, it is observed that the involvement of people increased on internet for various activities specifically online shopping, social networking, and blogs posting, etc. wherein people are engaged in expressing their views and opinions on certain entities or issues. This leads to the development and implementation of automatic recommendation system where users play a vital role by giving their feedback or opinion. To capture the feedback, views,
Data Loading...
