Sentiment Analysis for IMDb Reviews Using Deep Learning Classifier

Sabba, Sara; Chekired, Nahla; Katab, Hana; Chekkai, Nassira; Chalbi, Mohammed

doi:10.1109/ispa54004.2022.9786284

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…The authors achieved 95% accuracy with CNN as compared to ML models. Other studies ( Sabba et al, 2022 ; Ali, Abd El Hamid & Youssif, 2019 ; Rehman et al, 2019 ; Yenter & Verma, 2017 ; Gandhi et al, 2021 ) also developed deep-learning CNN and LSTM models on movie datasets for sentiment classification. LISAC Laboratory ( Elfaik & Nfaoui, 2021 ) used NLP techniques and the BiLSTM model for sentiment extraction.…”

Section: Related Workmentioning

confidence: 99%

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

Mujahid

Rustam

Alasim

et al. 2023

PeerJ Computer Science

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With the rise of social media platforms, sharing reviews has become a social norm in today’s modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald’s, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.

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