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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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new_version: v1.1 |
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datasets: |
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- custom |
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- chatgpt |
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pipeline_tag: text-classification |
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library_name: transformers |
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tags: |
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- emotion |
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- classification |
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- text-classification |
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- bert |
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- emojis |
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- emotions |
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- v1.0 |
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- sentiment-analysis |
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- nlp |
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- lightweight |
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- chatbot |
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- social-media |
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- mental-health |
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- short-text |
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- emotion-detection |
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- transformers |
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- real-time |
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- expressive |
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- ai |
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- machine-learning |
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- english |
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- inference |
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- edge-ai |
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- smart-replies |
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- tone-analysis |
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base_model: |
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- boltuix/bert-lite |
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- boltuix/bert-mini |
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--- |
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# BERT Mini Sentiment Analysis – Emotion & Text Classification Model |
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[](https://opensource.org/licenses/Apache-2.0) |
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[](https://huggingface.co/docs/transformers) |
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[](https://en.wikipedia.org/wiki/Natural_language_processing) |
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[](https://huggingface.co/tasks/text-classification) |
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[](https://huggingface.co/boltuix/bert-mini) |
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[](https://en.wikipedia.org/wiki/English_language) |
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[](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini) |
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.png) |
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--- |
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## 🌟 Overview |
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The **[BERT Mini Sentiment Analysis](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini)** model is a **lightweight, high-performance transformer** fine-tuned from **[Boltuix's BERT Mini](https://huggingface.co/boltuix/bert-mini)** for **emotion-based sentiment analysis**. It excels at classifying text into emotional categories such as **happiness**, **sadness**, **anger**, and more, making it ideal for understanding human emotions in text. |
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With only **11.2M parameters**, this model is **fast, efficient**, and tailored for **low-resource environments** like mobile devices, edge computing, and real-time applications. Whether you're analyzing social media trends, customer feedback, or building sentiment-aware chatbots, this model delivers **robust performance** with minimal computational overhead. |
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--- |
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## 🛠️ Model Details |
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- **Model Name:** BERT Mini Sentiment Analysis |
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- **Developed by:** Varnika S |
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- **Model Type:** Transformer (BERT-based) |
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- **Base Model:** [Boltuix BERT Mini](https://huggingface.co/boltuix/bert-mini) |
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- **Language:** English (en) |
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- **License:** [MIT](https://opensource.org/licenses/MIT) |
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- **Parameters:** 11.2M |
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- **Pipeline Tag:** Text Classification |
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- **Library:** Transformers (Hugging Face) |
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This model is fine-tuned on an **emotion-labeled dataset**, ensuring high accuracy in detecting nuanced emotional states. Its compact size and optimized architecture make it perfect for **real-time applications** and **resource-constrained environments**. |
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--- |
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## 🚀 Key Applications |
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Explore the versatile use cases of this model: |
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| **Use Case** | **Description** | |
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|--------------|-----------------| |
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| **Social Media Monitoring** | Track sentiment trends on platforms like Twitter, Reddit, and Instagram to understand audience emotions. | |
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| **Customer Feedback Analysis** | Extract actionable insights from product reviews, surveys, and support tickets. | |
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| **Mental Health AI** | Detect emotional distress or mood patterns in online conversations for proactive interventions. | |
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| **AI Chatbots & Assistants** | Build sentiment-aware chatbots that respond empathetically to user emotions. | |
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| **Market Research** | Analyze audience reactions to products, campaigns, or services for data-driven decisions. | |
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--- |
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## 💻 Example Usage |
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Get started with the model using the **Hugging Face Transformers** library. Below is a simple example to classify text sentiment: |
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```python |
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from transformers import pipeline |
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# Initialize the sentiment analysis pipeline |
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sentiment_analyzer = pipeline("text-classification", model="Varnikasiva/sentiment-classification-bert-mini") |
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# Analyze text |
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text = "I feel amazing today!" |
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result = sentiment_analyzer(text) |
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print(result) # Output: [{'label': 'happy', 'score': 0.98}] |
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``` |
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🔗 **Try it now**: [Hugging Face Model Page](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini) |
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For more advanced usage, check out the [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers). |
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--- |
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## 📊 Model Performance |
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The model delivers **high accuracy** and **ultra-fast inference**, making it a top choice for real-time applications. |
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| **Metric** | **Score** | |
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|------------|-----------| |
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| **Accuracy** | High (fine-tuned on emotion-labeled dataset) | |
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| **Inference Speed** | ⚡ Ultra-fast (optimized for low-latency) | |
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| **Model Size** | 11.2M Parameters | |
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| **Training Data** | Emotion-Labeled Dataset | |
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The model's lightweight design ensures **low memory usage** and **high throughput**, even on edge devices. |
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--- |
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## 🛠️ Fine-Tuning Guide |
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Want to adapt the model for your specific domain (e.g., finance, healthcare, or customer service)? You can fine-tune it further using **Hugging Face's Trainer API** or **PyTorch Lightning**. Here's a sample setup: |
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```python |
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from transformers import Trainer, TrainingArguments |
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# Define training arguments |
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training_args = TrainingArguments( |
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output_dir="./results", |
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evaluation_strategy="epoch", |
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learning_rate=2e-5, |
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per_device_train_batch_size=16, |
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per_device_eval_batch_size=16, |
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num_train_epochs=3, |
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weight_decay=0.01, |
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save_strategy="epoch", |
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logging_dir="./logs", |
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) |
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# Initialize Trainer |
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trainer = Trainer( |
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model=model, |
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args=training_args, |
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train_dataset=train_dataset, |
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eval_dataset=eval_dataset, |
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) |
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# Start fine-tuning |
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trainer.train() |
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``` |
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This setup allows you to **customize the model** for domain-specific tasks with minimal effort. |
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--- |
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## ❓ Frequently Asked Questions (FAQ) |
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### **Q1: What datasets were used for fine-tuning?** |
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The model was fine-tuned on a **curated emotion-labeled dataset**, enabling it to accurately detect emotions like happiness, sadness, anger, and more. |
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### **Q2: Is this model suitable for real-time applications?** |
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Absolutely! Its **compact size** and **optimized inference speed** make it ideal for real-time use cases like chatbots, social media monitoring, and live sentiment analysis. |
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### **Q3: Can I fine-tune this model for my own use case?** |
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Yes! Use the **Hugging Face Trainer API** or **PyTorch Lightning** to fine-tune the model on your dataset for enhanced performance in specific domains. |
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### **Q4: What makes this model different from other BERT models?** |
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This model is based on **Boltuix's BERT Mini**, a lightweight version of BERT with only 11.2M parameters, fine-tuned specifically for **emotion-based sentiment analysis**. It balances performance and efficiency, making it perfect for resource-constrained environments. |
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--- |
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## 🔗 Additional Resources |
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- 📚 [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers) |
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- 🧠 [Boltuix BERT Mini Model](https://huggingface.co/boltuix/bert-mini) |
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- 📜 [MIT License](https://opensource.org/licenses/MIT) |
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- 📖 [Guide to Fine-Tuning BERT Models](https://huggingface.co/docs/transformers/training) |
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--- |
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## 🤝 Contribute & Collaborate |
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We welcome contributions, feedback, and ideas to enhance this model! Whether it's improving performance, adding new features, or exploring new applications, your input is valuable. |
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- **Report Issues:** Open an issue on the [Hugging Face model page](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini). |
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- **Suggest Features:** Share your ideas for extending the model's capabilities. |
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- **Collaborate:** Interested in research or building applications? Reach out! |
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📬 **Contact:** [varnikas753@gmail.com](mailto:varnikas753@gmail.com) |
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--- |
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## 🌟 Why Choose This Model? |
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- **Lightweight & Efficient:** Only 11.2M parameters for fast inference on low-resource devices. |
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- **Emotion-Focused:** Fine-tuned for nuanced emotion detection, not just positive/negative sentiment. |
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- **Open-Source:** Licensed under MIT for flexible use in commercial and research projects. |
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- **Easy to Use:** Seamless integration with Hugging Face's Transformers library. |
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- **Versatile:** Applicable to social media, customer feedback, mental health, and more. |
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--- |
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## 🎯 Get Started Today! |
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Ready to dive into emotion-based sentiment analysis? Head over to the [Hugging Face Model Page](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini) to explore the model, try the demo, or download it for your project. |
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**Happy Coding! 🚀** |
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--- |
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*Tags: #transformers #bert #nlp #sentiment-analysis #emotion-detection #huggingface #text-classification #machine-learning #open-source #ai #mental-health #customer-feedback #social-media-analysis* |
