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Traditional ML models outperform deep learning for tweet and email sentiment analysis

By PulseAugur Editorial · [4 sources] ·

A recent study compared traditional machine learning models with deep learning architectures for sentiment analysis on social media and email data. For tweet sentiment classification, a Logistic Regression model using TF-IDF features outperformed a BiLSTM model, achieving 73.5% accuracy. In email sentiment analysis, a Support Vector Machine (SVM) model demonstrated superior performance with 98.74% accuracy, offering a better balance of precision and processing speed compared to LSTM models. AI

IMPACT Suggests that for certain text classification tasks, traditional ML models may offer better performance and efficiency than complex deep learning approaches.

RANK_REASON The cluster contains two academic papers published on arXiv comparing machine learning and deep learning models for sentiment analysis tasks.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Traditional ML models outperform deep learning for tweet and email sentiment analysis

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Research
The cluster contains two academic papers published on arXiv comparing machine learning and deep learning models for sentiment analysis tasks.
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4 independent sources
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paper, other
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High
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144 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [4]

  1. arXiv cs.CL TIER_1 English(EN) · Vita Anggraini, Cintya Bella, Bastian, Luluk Muthoharoh, Ardika Satria, Martin C. T. Manullang ·

    A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset

    arXiv:2605.04888v1 Announce Type: new Abstract: The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a …

  2. arXiv cs.CL TIER_1 English(EN) · Martin C. T. Manullang ·

    A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset

    The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a deep learning Bidirectional Long Short-Term Memo…

  3. arXiv cs.CL TIER_1 English(EN) · Virdio Samuel Saragih, Baruna Abirawa, Kartini Lovian Simbolon, Luluk Muthoharoh, Ardika Satria, Martin C. T. Manullang ·

    A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis

    arXiv:2605.03440v1 Announce Type: new Abstract: The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorith…

  4. arXiv cs.CL TIER_1 English(EN) · Martin C. T. Manullang ·

    A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis

    The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorithms and deep learning architectures, specifically…

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