如何在无监督的基于方面的情感分析中进行主题检测

Question

我想使用 Python 制作 ABSA，其中预定义方面（例如交付、质量、服务）的情绪是从在线评论中分析的。我想在无人监督的情况下进行，因为这将使我免于手动标记评论，而且我可以分析更多的评论数据（查看大约 10 万条评论）。因此，我的数据集仅包含评论而没有评级。我想要一个可以先检测方面类别然后分配情感极性的模型。例如。当评论说 "The shipment went smoothly, but the product is broken" 我希望模型将词 "shipment" 分配给方面类别 "delivery" 并且 "smoothly" 与积极情绪相关。

我一直在寻找可以采取的方法，我想知道是否有人有这方面的经验，可以指导我朝着对我有帮助的方向发展。将不胜感激！

Answer 1

Aspect Based Sentiment Analysis (ABSA), where the task is first to extract aspects or features of an entity (i.e. Aspect Term Extraction or ATE1 ) from a given text, and second to determine the sentiment polarity (SP), if any, towards each aspect of that entity. The importance of ABSA led to the creation of the ABSA task

B-LSTM & CRF classifier will be used for feature extraction and aspect term detection for both supervised and unsupervised ATE.

https://www.researchgate.net/profile/Andreea_Hossmann/publication/319875533_Unsupervised_Aspect_Term_Extraction_with_B-LSTM_and_CRF_using_Automatically_Labelled_Datasets/links/5a3436a70f7e9b10d842b0eb/Unsupervised-Aspect-Term-Extraction-with-B-LSTM-and-CRF-using-Automatically-Labelled-Datasets.pdf

https://github.com/songyouwei/ABSA-PyTorch/blob/master/infer_example.py

# -*- coding: utf-8 -*-
# file: infer.py
# author: songyouwei <youwei0314@gmail.com>
# Copyright (C) 2019. All Rights Reserved.

import torch
import torch.nn.functional as F
import argparse

from data_utils import build_tokenizer, build_embedding_matrix
from models import IAN, MemNet, ATAE_LSTM, AOA


class Inferer:
    """A simple inference example"""
    def __init__(self, opt):
        self.opt = opt
        self.tokenizer = build_tokenizer(
            fnames=[opt.dataset_file['train'], opt.dataset_file['test']],
            max_seq_len=opt.max_seq_len,
            dat_fname='{0}_tokenizer.dat'.format(opt.dataset))
        embedding_matrix = build_embedding_matrix(
            word2idx=self.tokenizer.word2idx,
            embed_dim=opt.embed_dim,
            dat_fname='{0}_{1}_embedding_matrix.dat'.format(str(opt.embed_dim), opt.dataset))
        self.model = opt.model_class(embedding_matrix, opt)
        print('loading model {0} ...'.format(opt.model_name))
        self.model.load_state_dict(torch.load(opt.state_dict_path))
        self.model = self.model.to(opt.device)
        # switch model to evaluation mode
        self.model.eval()
        torch.autograd.set_grad_enabled(False)

    def evaluate(self, raw_texts):
        context_seqs = [self.tokenizer.text_to_sequence(raw_text.lower().strip()) for raw_text in raw_texts]
        aspect_seqs = [self.tokenizer.text_to_sequence('null')] * len(raw_texts)
        context_indices = torch.tensor(context_seqs, dtype=torch.int64).to(self.opt.device)
        aspect_indices = torch.tensor(aspect_seqs, dtype=torch.int64).to(self.opt.device)

        t_inputs = [context_indices, aspect_indices]
        t_outputs = self.model(t_inputs)

        t_probs = F.softmax(t_outputs, dim=-1).cpu().numpy()
        return t_probs


if __name__ == '__main__':
    model_classes = {
        'atae_lstm': ATAE_LSTM,
        'ian': IAN,
        'memnet': MemNet,
        'aoa': AOA,
    }
    # set your trained models here
    model_state_dict_paths = {
        'atae_lstm': 'state_dict/atae_lstm_restaurant_acc0.7786',
        'ian': 'state_dict/ian_restaurant_acc0.7911',
        'memnet': 'state_dict/memnet_restaurant_acc0.7911',
        'aoa': 'state_dict/aoa_restaurant_acc0.8063',
    }
    class Option(object): pass
    opt = Option()
    opt.model_name = 'ian'
    opt.model_class = model_classes[opt.model_name]
    opt.dataset = 'restaurant'
    opt.dataset_file = {
        'train': './datasets/semeval14/Restaurants_Train.xml.seg',
        'test': './datasets/semeval14/Restaurants_Test_Gold.xml.seg'
    }
    opt.state_dict_path = model_state_dict_paths[opt.model_name]
    opt.embed_dim = 300
    opt.hidden_dim = 300
    opt.max_seq_len = 80
    opt.polarities_dim = 3
    opt.hops = 3
    opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    inf = Inferer(opt)
    t_probs = inf.evaluate(['happy memory', 'the service is terrible', 'just normal food'])
    print(t_probs.argmax(axis=-1) - 1)

如何在无监督的基于方面的情感分析中进行主题检测

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