Sentiment Analysis

Usage Example

Our Sentiment Analysis Models

  1. Our model to evaluate sentiments of English language news is based on TF-IDF vectorization of text. To deal with ambitious word combinations like “not terrible”, “not bad” we applied n-grams splitting of the text. C-Support Vector Classifier was used at the top of it. To train our model we used a human-labeled dataset of news titles sentiment created by Connexun team.
  2. Multilingual sentiment model based on the pre-trained joeddav/xlm-roberta-large-xnli model. This model is a result of fine-tuning of xlm-roberta-large on a combination of Natural Language Inference (NLI) data in many languages. It is widely used for zero-shot text classification. NLI approach defines is two sentences in entailment between each other or in contradiction or neutral with respect to each other.
  3. Sentiment analysis of entities with respect to their context exploits the Aspect Based Sentiment Analysis. On top of this model, we fine-tuned the logistic regression classifier which provides probabilities that a given NER has positive or negative sentiment in a given sentence. The logistic regression was trained on a human-labeled training dataset created in Connexun.

Application in various industries. Recommender systems for e-commerce

Bank Performance and News Sentiment

Our Services

Conclusion

About Connexun

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Connexun is the ultimate AI news engine — turning unstructured news content into multi-purpose actionable data.

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Connexun | news api

Connexun | news api

Connexun is the ultimate AI news engine — turning unstructured news content into multi-purpose actionable data.

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