Evaluating the influence of feature selection-based dimensionality reduction on sentiment analysis

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Observational analysis improves sentiment classification in social media text, indicating feature selection enhances accuracy.

Sentiment analysis accuracy improves significantly when using dimensionality reduction techniques.
The combination of regularized locality preserving indexing (RLPI) and long short-term memory (LSTM) outperforms other models.
Text cleaning techniques are essential for reducing noise and redundancy in social media datasets.
Overall, effective feature selection methods are crucial for enhancing the performance of sentiment analysis tasks.

Kishore et al. (2025) studied this question.

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View Full Paper

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Authors

Observational analysis improves sentiment classification in social media text, indicating feature selection enhances accuracy.

Sentiment analysis accuracy improves significantly when using dimensionality reduction techniques.
The combination of regularized locality preserving indexing (RLPI) and long short-term memory (LSTM) outperforms other models.
Text cleaning techniques are essential for reducing noise and redundancy in social media datasets.
Overall, effective feature selection methods are crucial for enhancing the performance of sentiment analysis tasks.

Kishore et al. (2025) studied this question.

View Full Paper

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