Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions

Fiok, Krzysztof; Karwowski, Waldemar; Gutiérrez, Edgar; Wilamowski, Maciej

doi:10.1016/j.eswa.2021.115771

Cited by 31 publications

(16 citation statements)

References 25 publications

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“…The pattern found must be meaningful and the pattern provides benefits, usually economic benefits. Large amounts of data are needed [18]. Characteristics of data mining as follows:…”

Section: Data Miningmentioning

confidence: 99%

COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia

Hananto

2022

IJIIS Int. J. Informatics Inf. Syst.

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“…The pattern found must be meaningful and the pattern provides benefits, usually economic benefits. Large amounts of data are needed [18]. Characteristics of data mining as follows:…”

Section: Data Miningmentioning

confidence: 99%

COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia

Hananto

2022

IJIIS Int. J. Informatics Inf. Syst.

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“…Researchers of Fiok et al. ( 2021 ) have performed a sentiment analysis on a five-level sentiment scale, based on tweets posted to a specific Twitter account. Paper (Chen et al., 2017 ) proposes a BiLSTM-CRF based approach to improve sentence type classification.…”

Section: Literature Reviewmentioning

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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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: Methods For Interpreting Ai Systemsmentioning

confidence: 99%

“…An example of how such model explanations can be presented is demonstrated in Figure 4, where an ML classifier trained to analyze sentiment in Twitter and fed features extracted from text by SEANCE is explained. 29 For text-specific XAI methods, Fiok et al 29 observed that if a text-level representation is obtained with long short-term memory networks (LSTMs) 32 benefiting from word embeddings that do not change with the context of words in a sentence, rationale for model predictions can be assessed with instance-level visualizations, as shown in Li et al 33 and Arras et al 34 In addition, Karpathy 35 demonstrated that a similar process is possible when recurrent neural networks are used for text analysis on character-level. Unfortunately, the above-mentioned predictive methods do not provide state-of-the-art performance, and the proposed XAI methods have not yet gained wide popularity.…”

Section: Xai In Natural Language Processingmentioning

confidence: 99%

Explainable artificial intelligence for education and training

Fiok

Farahani

Karwowski

et al. 2021

Journal of Defense Modeling & Simulation

Self Cite

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Researchers and software users benefit from the rapid growth of artificial intelligence (AI) to an unprecedented extent in various domains where automated intelligent action is required. However, as they continue to engage with AI, they also begin to understand the limitations and risks associated with ceding control and decision-making to not always transparent artificial computer agents. Understanding of “what is happening in the black box” becomes feasible with explainable AI (XAI) methods designed to mitigate these risks and introduce trust into human-AI interactions. Our study reviews the essential capabilities, limitations, and desiderata of XAI tools developed over recent years and reviews the history of XAI and AI in education (AIED). We present different approaches to AI and XAI from the viewpoint of researchers focused on AIED in comparison with researchers focused on AI and machine learning (ML). We conclude that both groups of interest desire increased efforts to obtain improved XAI tools; however, these groups formulate different target user groups and expectations regarding XAI features and provide different examples of possible achievements. We summarize these viewpoints and provide guidelines for scientists looking to incorporate XAI into their own work.

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Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions

Cited by 31 publications

References 25 publications

COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia

COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia

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

Explainable artificial intelligence for education and training

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