<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper investigates the effectiveness of sentiment analysis (SA) methods, ranging from rule-based approaches to deep learning architectures, in analysing textual data. The study focuses on three Python libraries: TextBlob, VADER, and Flair, evaluating their accuracy on a public dataset of Twitter posts. Additionally, custom neural network architectures are developed to optimize sentiment classification. Results indicate that while rule-based libraries offer simplicity, deep learning-based libraries show promise for higher accuracy. The customized LSTM models, particularly LSTM2 with architectural adjustments and regularization techniques, demonstrate improved performance over baseline models with classification accuracy as high as 76.3%.</p>
</abstract>

This paper investigates the effectiveness of sentiment analysis (SA) methods, ranging from rule-based approaches to deep learning architectures, in analysing textual data. The study focuses on three Python libraries: TextBlob, VADER, and Flair, evaluating their accuracy on a public dataset of Twitter posts. Additionally, custom neural network architectures are developed to optimize sentiment classification. Results indicate that while rule-based libraries offer simplicity, deep learning-based libraries show promise for higher accuracy. The customized LSTM models, particularly LSTM2 with architectural adjustments and regularization techniques, demonstrate improved performance over baseline models with classification accuracy as high as 76.3%.

Improving Sentiment Analysis With Neural Networks

International conference KNOWLEDGE-BASED ORGANIZATION

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Improving Sentiment Analysis With Neural Networks"}

This paper investigates the effectiveness of sentiment analysis (SA) methods, ranging from rule-based approaches to deep learning architectures, in analysing...

Improving Sentiment Analysis With Neural Networks

Publié en ligne: 04 juil. 2024

Pages: 1 - 6

DOI: https://doi.org/10.2478/kbo-2024-0095

Mots clés
Sentiment analysis, Long Short-Term Memory network, Python libraries, Natural Language Processing

© 2024 Annamaria Sârbu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Improving Sentiment Analysis With Neural Networks

Publié en ligne: 04 juil. 2024

Pages: 1 - 6

DOI: https://doi.org/10.2478/kbo-2024-0095

Mots clésSentiment analysis, Long Short-Term Memory network, Python libraries, Natural Language Processing

© 2024 Annamaria Sârbu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Sentiment analysis, Long Short-Term Memory network, Python libraries, Natural Language Processing