Social Media Sentiments Analysis using Convolutional Neural Network and Support Vector Machine
Bibliographic record
Abstract
Speech emotion recognition (SER) is a critical area of research in human-computer interaction, enabling applications such as sentiment analysis, virtual assistants, and psychological assessment. This study proposes a hybrid approach utilizing a 1D Convolutional Neural Network (CNN1D) and a Support Vector Machine (SVM) for classifying emotions from speech signals into seven categories: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The Toronto Emotional Speech Set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets were employed to ensure diverse emotional representations. The preprocessing phase involved silence removal to eliminate non-informative segments, followed by Independent Component Analysis (ICA) to enhance signal quality. Feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR), Chroma Short-Time Fourier Transform (Chroma STFT), Root Mean Square (RMS) energy, and Mel Spectrogram, were used to capture spectral, temporal, and tonal characteristics of speech. To improve model generalization, data augmentation techniques such as noise addition, time stretching, pitch shifting, and time shifting were applied. Experimental results demonstrate that the combined CNN-1D and SVM approach effectively captures emotional variations in speech, providing improved classification accuracy. This research contributes to the advancement of robust SER models by integrating deep learning with traditional machine learning, optimizing both feature representation and classification performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".