A survey on deep learning based sentiment analysis

Joseph, Jyothis; Vineetha, S.; Sobhana, N. V.

doi:10.1016/j.matpr.2022.02.483

Cited by 18 publications

(5 citation statements)

References 17 publications

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“…SVM and Naíve Bayes classifiers have been used in paper (Pavitha et al., 2022 ) for sentiment analysis. Deep neural networks are also used for sentiment analysis tasks (Joseph et al., 2022 ). Authors of Rani et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The machine learning approach can also be based on powerful Deep Neural Networks. They are found to give better results than conventional models because of their ability to detect features from a large amount of data (Joseph et al., 2022 ). Such a technique requires high-dimensional labelled data to train the model, but the training process may be a memory and time-consuming task.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based sentiment analysis of public perception of working from home through tweets

Vohra

Garg

2022

J Intell Inf Syst

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Nowadays, we are witnessing a paradigm shift from the conventional approach of working from office spaces to the emerging culture of working virtually from home. Even during the COVID-19 pandemic, many organisations were forced to allow employees to work from their homes, which led to worldwide discussions of this trend on Twitter. The analysis of this data has immense potential to change the way we work but extracting useful information from this valuable data is a challenge. Hence in this study, the microblogging website Twitter is used to gather more than 450,000 English language tweets from 22nd January 2022 to 12th March 2022, consisting of keywords related to working from home. A state-of-the-art pre-processing technique is used to convert all emojis into text, remove duplicate tweets, retweets, username tags, URLs, hashtags etc. and then the text is converted to lowercase. Thus, the number of tweets is reduced to 358,823. In this paper, we propose a fine-tuned Convolutional Neural Network (CNN) model to analyse Twitter data. The input to our deep learning model is an annotated set of tweets that are effectively labelled into three sentiment classes, viz. positive negative and neutral using VADER (Valence Aware Dictionary for sEntiment Reasoning). We also use a variation in the input vector to the embedding layer, by using FastText embeddings with our model to train supervised word representations for our text corpus of more than 450,000 tweets. The proposed model uses multiple convolution and max pooling layers, dropout operation, and dense layers with ReLU and sigmoid activations to achieve remarkable results on our dataset. Further, the performance of our model is compared with some standard classifiers like Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest. From the results, it is observed that on the given dataset, the proposed CNN with FastText word embeddings outperforms other classifiers with an accuracy of 0.925969. As a result of this classification, 54.41% of the tweets are found to show affirmation, 24.50% show a negative disposition, and 21.09% have neutral sentiments towards working from home.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning based sentiment analysis of public perception of working from home through tweets

Vohra

Garg

2022

J Intell Inf Syst

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“…Deep learning adalah sub bidang machine learning yang menggunakan jaringan saraf untuk memecahkan masalah yang sangat kompleks, Berkembangnya deep learning yang dapat digunakan untuk analisis sentimen yang memiliki kinerja lebih baik dari machine learning [15]. Long Short-Term Memory (LSTM) adalah salah satu varian Recurrent Neural Network (RNN) yang menyimpan informasi jangka panjang ke dalam sel memori dan mempelajari informasi kontekstual yang berguna untuk tugas klasifikasi [16].…”

Section: Pendahuluanunclassified

Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

2023

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Consumers believe that restaurant reviews are very important when choosing a restaurant. Due to the fact that reviews have become one of the most effective ways to influence customer decisions, research that has been done on restaurant customer reviews is about sentiment analysis. Previous studies have only used sentiment analysis at the sentence or document level, while a better level uses Aspect-Based Sentiment Analysis (ABSA), or a type of aspect-based sentiment analysis. LSTM is a variant of RNN that stores long-term information in memory cells. Use of global max pooling to reduce output resolution features and prevent overfitting. In addition, the optimization method used by Adam Optimizer is an adaptive learning rate optimization algorithm specifically designed to train deep neural networks. This study aims to classify restaurant customer opinions based on aspects (food, place, service, and price) based on restaurant customer reviews on Indonesian-language TripAdvisor with LSTM and global max pooling for sentiment classification (negative, half negative, neutral, half positive, positive). The results of this study indicate that the ABSA in restaurant customer reviews for sentiment classification accuracy is 78.7% and the aspect category accuracy is 78%, both are interconnected and can help understand restaurant customer opinions on TripAdvisor.

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“…Regardless of the sentiment analysis model, it often draws on the evaluation methods of classification algorithms and uses accuracy, recall, precision, and F1 Score to measure the performance of an algorithm, and the meanings are shown in Figure 5 (Bhowmik et al, 2022;Birjali et al, 2021;Joseph et al, 2022;Vashishtha & Susan, 2019). Meanwhile, as mentioned in Figure 14, there are four stages to constructing a sentiment analysis solution, and the different programming in each stage affect the length of the running time, which refers to the time interval from the beginning to the end of the program execution (Barr et al, 1995).…”

Section: * 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 * 𝑅𝑒𝑐𝑎𝑙𝑙 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙mentioning

confidence: 99%

Evaluation & Analysis of Movie Aspects: Based on Sentiment Analysis

2023

FMS

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The objective of this project is to address the research question “What factor determines the success of an action movie?”, where the success factors are quantified as plot, music, plot, and director. Based on the research question, the author conducts a literature review of the development of the UK film industry, the impact of social media and film criticism on the film industry. To evaluate film reviews, sentiment analysis, which can efficiently convert text into structured data, is utilized in the project. In the methodology and the case study, the method of constructing the VADER model and the SVM model with the process including data processing, modelling, result correctness verification, and model evaluation are expounded and shown. Finally, the correctness of the two models is verified and the model output is visualized for analysis. The conclusion of the project is as follows: (1) Responding to the inquiry: In action films, the plot is the aspect that the audience pays the most attention to, but first the actors and music, and finally the director. (2) When compared to the experimental findings of Wang et al. (2020a), the project came to different conclusions, presumably as a result of the project’s limited access to movies and the change in the data processing approach. (3) Given that the two models employed in this research did well in the evaluation, they can be effectively applied to help production firms raise the caliber of action movies. (4) The models in the research can be easily developed and used to other domains because film review analysis can be compared with film reviews from other industries. However, some restrictions are also brought up, which offers suggestions for further work.

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A survey on deep learning based sentiment analysis

Cited by 18 publications

References 17 publications

Deep learning based sentiment analysis of public perception of working from home through tweets

Deep learning based sentiment analysis of public perception of working from home through tweets

Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

Evaluation & Analysis of Movie Aspects: Based on Sentiment Analysis

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