Interpretable Sentiment Analysis based on Deep Learning: An overview

Jawale, Shila; Sawarkar, Sudhir

doi:10.1109/punecon50868.2020.9362361

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…It helps to recognize tweet sentiment containing any of the entities which are incapable of looking in the training dataset and composed with their possibilities in positive and negative tweets [35]. Various methods are possible with sentiment analysis to Naive Bayes probabilistic classifier [36,37] where a scheme of a sentiment class c can be classified with a specified tweet tw which is calculated and given as,…”

Section: Semantic Features For Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Chouhan¹,

Yadav²,

Rout³

et al. 2023

Computer Systems Science and Engineering

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Twitter is a radiant platform with a quick and effective technique to analyze users' perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user's perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.

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Section: Semantic Features For Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Chouhan¹,

Yadav²,

Rout³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Sentiment Analysis (SA), the automated process of identifying and extracting subjective information like opinions, emotions, and attitudes from text data, has become an increasingly critical technique across social science domains: ranging from Policy Making to Business Analytics, and from Social/Behavioural Analytics to Finance (Jawale and Sawarkar 2020;Raheman et al 2022;Al-Qablan et al 2023;Fioroni et al 2023;Venkit et al 2023). While deep learning models have excelled in achieving high accuracy, often surpassing simpler lexicon models in SA tasks (Al-Qablan et al 2023), their inherently opaque nature poses challenges for applications in high-stakes domains like government policy making or mental health diagnosis, where transparent and interpretable decision-making is crucial (Rudin 2019;Jawale and Sawarkar 2020). Recognising the continued importance of rule-based SA, particularly in computational social science fields where interpretability is paramount, improving rule-based SA remains vital.…”

Section: Introductionmentioning

confidence: 99%

Sentibank: A Unified Resource of Sentiment Lexicons and Dictionaries

2024

ICWSM

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Sentiment analysis is critical across computational social science domains, but faces challenges in interpretability. Rule-based methods relying on expert lexicons enable transparency, yet applying them is hindered by resource fragmentation and lack of validation. This paper introduces sentibank, a large-scale unified database consolidating 15 original sentiment dictionaries and 43 preprocessed dictionaries, spanning 7 genres and 6 domains.

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“…throughtheuseofexplainablemethodsortechniquessuchasvisualizationtechniquesandothers Jawale(2020),VanderMaaten(2008, Jawale(2023).…”

mentioning

confidence: 99%

Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

Jawale

Sawarker

2022

International Journal of Applied Evolutionary Computation

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Regarding the ubiquity of digitalization and electronic processing, an automated review processing system, also known as sentiment analysis, is crucial. There were many architectures and word embeddings employed for effective sentiment analysis. Deep learning is now-a-days becoming prominent for solving these problems as huge amounts of data get generated per second. In deep learning, word embedding acts as a feature representative and plays an important role. This paper proposed a novel deep learning architecture which represents hybrid embedding techniques that address polysemy, semantic and syntactic issues of a language model, along with justifying the model prediction. The model is evaluated on sentiment identification tasks, obtaining the result as F1-score 0.9254 and F1-score 0.88, for MR and Kindle dataset respectively. The proposed model outperforms many current techniques for both tasks in experiments, suggesting that combining context-free and context-dependent text representations potentially capture complementary features of word meaning. The model decisions justified with the help of visualization techniques such as t-SNE.

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Interpretable Sentiment Analysis based on Deep Learning: An overview

Cited by 8 publications

References 34 publications

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Sentibank: A Unified Resource of Sentiment Lexicons and Dictionaries

Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

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