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ScalaConsultants / Aspect-Based-Sentiment-Analysis

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Extract Weights and scores from result

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AymenBenk
AymenBenk commented Jun 29, 2021

`import numpy as np
import aspect_based_sentiment_analysis as absa
from aspect_based_sentiment_analysis import alignment
from aspect_based_sentiment_analysis import Example

text = "I love mascara"
aspects = ['mascara']

recognizer = absa.aux_models.BasicPatternRecognizer()
nlp = absa.load(pattern_recognizer=recognizer)
task = nlp(text=text, aspects=aspects)
slack = task.examples

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declare-lab / dialogue-understanding

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This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

natural-language-processing sentiment-analysis dialogue pytorch lstm pretrained-models emotion-analysis bert dialogue-systems conversational-agents emotion-recognition natural-language-understanding conversational-ai adversarial-attacks explainable-ai dialogue-act emotion-recognition-in-conversation bert-embeddings dialogue-understanding utterance-level-dialogue
  • Updated Jun 12, 2021
  • Python
wikirec

andrewtavis / wikirec

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akshaydnicator / Twitter-Sentiment-Analysis-NLP-Hackathon

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Problem Statement: Given the tweets from customers about various tech firms who manufacture and sell mobiles, computers, laptops, etc, the task is to identify if the tweets have a negative sentiment towards such companies or products.

nlp machine-learning twitter deep-learning sentiment-analysis hackathon cross-validation spacy neural-networks keras-tensorflow pre-processing punctuation-marks cnn-classification wordvectors sklearn-library features-extraction analytics-vidhya bert-embeddings elmo-vectors lemmetization
  • Updated May 17, 2020
  • Python

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