如何在基于无监督方面的情感分析中进行主题检测

Question

我想使用Python制作ABSA，并从在线评论中分析预定义方面（例如交付，质量，服务）的情绪。我想在无人监督的情况下进行操作，因为这将使我免于手动为评论添加标签，并且我可以分析更多评论数据（大约有10万条评论）。因此，我的数据集仅包含评论，而没有评分。我希望有一个模型，该模型可以首先检测方面类别，然后分配情感极性。例如。当评论中显示“运输顺利，但产品坏了”时，我希望模型将“装运”一词分配给“交货”方面，而“平稳”则涉及积极的情绪。

我已经寻找了采取的方法，我想知道是否有人对此有经验，可以指导我朝可以帮助我的方向发展。将不胜感激！

Answer 1

基于方面的情感分析（ABSA），其中任务首先要执行提取实体的方面或特征（即方面术语提取）或ATE1），然后确定情感指向该实体各个方面的极性（SP）。的ABSA的重要性导致ABSA任务的创建

B-LSTM和CRF分类器将用于特征提取和方面有监督和无监督ATE的术语检测。

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 <[email protected]>
# 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)