Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour, Kia; Gogate, Mandar; Adeel, Ahsan; Larijani, Hadi; Hussain, Amir

doi:10.3390/e23050596

Cited by 81 publications

(37 citation statements)

References 74 publications

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“…Deep-learning-based sentiment analysis methods are able to actively learn text features and learn semantic information in text based on deep network models to achieve sentiment classification [39]. Dashtipour et al [40] automatically mined relevant features in text information for sentiment classification by convolutional neural network and LSTM model. Gopalakrishnan et al [41] performed sentiment analysis on Twitter datasets by constructing an LSTM model with optimal parameters, and satisfactory sentiment classification performance was achieved.…”

Section: Methods Of Sentiment Analysismentioning

confidence: 99%

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Meng

2022

Sustainability

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In recent years, the price of small agricultural products has both plummeted and skyrocketed, which has a great impact on people’s lives. Studying the factors affecting the price fluctuation of small agricultural products is of great significance for stabilizing their price. With the development and application of social media, farmers and consumers are more greatly influenced by online public opinion, resulting in irrational planting behavior or purchasing behavior, which has a complex impact on the price of small agricultural products. Taking garlic as an example, we crawled through network public opinions about garlic price from January 2015 to December 2020 using web crawler technology. Then, the network public opinions were quantified using a natural language processing and time-varying parameter vector autoregression (NLP-TVP-VAR) model to empirically analyze their dynamic influence on garlic price fluctuation. It was found that both public attitude and public attention have a short-term influence on garlic price fluctuation, and the influences of each differ according to direction, intensity and timing. The influence of public attitude on garlic price fluctuation is positive, while the influence of public attention on garlic price fluctuation is largely negative. The influence intensity of public attitude is stronger than of public attention on garlic price fluctuation. The influence of public attitude on garlic price fluctuation shows a trend of intensifying, while that of public attention has been weaker than in previous years. In addition, based on the results of our study, we present some recommendations for improving the comprehensive information platform and price fluctuation early warning system for the whole industry chain of small agricultural products.

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Section: Methods Of Sentiment Analysismentioning

confidence: 99%

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Meng

2022

Sustainability

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“…However, the Deep Learning (DL) technique solves this problem through its computational model which involves multiple processing layers to automatically discover the word pattern from a vast amount of data [ 35 ]. Several researchers have implemented deep learning to analyse customer sentiments in their domain [ 36 , 37 , 38 , 39 , 40 ]. A recent work [ 28 ] built and compared two ML and DL models to perform sentiment analysis on reviews extracted from the Yelp website.…”

Section: Related Workmentioning

confidence: 99%

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Adak

Pradhan

Shukla

et al. 2022

Foods

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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model.

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“…Furthermore, LSTM predicts time series with long-range dependence more accurately than the RNN [41,44]. According Dashtipour et al LSTM is a successful augmented RNN model which is used to learn sequential information with dependencies that LSTM can store and use to compute information for a long time period [45]. LSTM memory block comprises four main units: the memory cell, the forget gate, the input gate, and the output gate.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Improving Perceived Quality of Live Adaptative Video Streaming

Santos

Silva

Pedroso

2021

Entropy

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Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

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Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Cited by 81 publications

References 74 publications

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Improving Perceived Quality of Live Adaptative Video Streaming

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